survey/0000755000176200001440000000000014576600372011621 5ustar liggesuserssurvey/NAMESPACE0000644000176200001440000003327614571143205013043 0ustar liggesusersuseDynLib(survey) import(stats,graphics,splines,grid, survival,lattice,methods,Matrix) importFrom("grDevices", "col2rgb", "grey", "rgb") importFrom("utils", "getS3method") importFrom("mitools","withPV") importFrom(Rcpp, sourceCpp) export(svydesign, svrepdesign, as.svrepdesign,twophase, postStratify, rake, calibrate, estWeights, as.svydesign2, svyplot, svyhist, svyboxplot, svyby, cv, SE, deff, svycoxph, svyglm, svyquantile, svymean, svytotal, svyvar, svyratio, svytable, svychisq, svymle, svysmooth, withReplicates, hadamard, paley, is.hadamard, bootweights, mrbweights, jk1weights,jknweights, brrweights, regTermTest, degf, nonresponse, sparseCells, neighbours, joinCells, compressWeights, make.formula, svycontrast, svyCprod, svyrecvar, grake, svrVar, pchisqsum, pFsum, make.calfun, cal.linear, cal.raking, cal.logit, cal.sinh,svycdf, svykappa, svykm, svyolr, svycoplot, svyloglin, make.panel.svysmooth, dotchart, subbootweights, svyciprop, svyttest, marginpred, twophasevar, twophase2var, ppsmat, HR, unwtd.count, svyfactanal, svypredmeans, svyprcomp, trimWeights,stratsample,svylogrank, svyranktest,svystandardize, psrsq, svysurvreg, cal_names, svycralpha, svyivreg, svynls, svyscoretest, oldsvyquantile,svyqqplot, svyqqmath, svybys, svygofchisq, poisson_sampling,ppscov, xdesign, svysmoothArea,svysmoothUnit) S3method(svyloglin,survey.design) S3method(svyloglin,svyrep.design) S3method(svyloglin,DBIsvydesign) S3method(anova,svyloglin) S3method(print,svyloglin) S3method(print,anova.svyloglin) S3method(coef,svyloglin) S3method(vcov,svyloglin) S3method(deviance,svyloglin) S3method(print,summary.svyloglin) S3method(summary,svyloglin) S3method(update,svyloglin) S3method(model.matrix,svyloglin) S3method(terms,svyloglin) S3method(degf,svyloglin) S3method(svyttest,default) S3method(biplot,svyprcomp) S3method(marginpred,svycoxph) S3method(marginpred, svykmlist) S3method(marginpred, svyglm) S3method(print,svyciprop) S3method(vcov,svyciprop) S3method(confint,svyciprop) S3method(coef,svyciprop) S3method(svydesign, default) S3method(svydesign, imputationList) S3method(svydesign, character) S3method(svydesign, DBimputationList) S3method(as.svrepdesign, default) S3method(as.svrepdesign, svyimputationList) S3method(close, DBIsvydesign) S3method(open, DBIsvydesign) S3method(summary, DBIsvydesign) S3method(print, summary.DBIsvydesign) S3method(print, DBIsvydesign) S3method(print, pps) S3method(summary, pps) S3method(print, summary.pps) S3method(print,ppsmat) S3method(print,ppsdelta) S3method(print,ppsdcheck) S3method(svrepdesign, default) S3method(svrepdesign, imputationList) S3method(svrepdesign, character) S3method(print, DBIrepdesign) S3method(summary, DBIrepdesign) S3method(svymean, survey.design) S3method(svymean, svyrep.design) S3method(svymean, survey.design2) S3method(svymean, twophase) S3method(svymean, twophase2) S3method(svymean, pps) S3method(svymean, DBIsvydesign) S3method(svytotal, survey.design) S3method(svytotal, svyrep.design) S3method(svytotal, survey.design2) S3method(svytotal, twophase) S3method(svytotal, twophase2) S3method(svytotal, pps) S3method(svytotal, DBIsvydesign) S3method(svyratio, survey.design) S3method(svyratio, svyrep.design) S3method(svyratio, survey.design2) S3method(svyratio, pps) S3method(svyratio, twophase) S3method(svyratio, twophase2) S3method(svyratio, DBIsvydesign) S3method(svyvar, survey.design) S3method(svyvar, svyrep.design) S3method(svyvar, DBIsvydesign) S3method(svyquantile, survey.design) S3method(svyquantile, svyrep.design) S3method(oldsvyquantile, survey.design) S3method(oldsvyquantile, svyrep.design) S3method(svyquantile, DBIsvydesign) S3method(oldsvyquantile, DBIsvydesign) S3method(svytable, survey.design) S3method(svytable, svyrep.design) S3method(svytable, DBIsvydesign) S3method(svychisq, survey.design) S3method(svychisq, svyrep.design) S3method(svychisq, DBIsvydesign) S3method(svychisq, twophase) S3method(svykappa, default) S3method(svykappa, DBIsvydesign) S3method(svyglm,survey.design) S3method(svyglm,svyrep.design) S3method(svyglm, DBIsvydesign) S3method(svyplot,default) S3method(svyplot, DBIsvydesign) S3method(svycoplot,default) S3method(svycoplot, DBIsvydesign) S3method(svysmooth, default) S3method(svysmooth, DBIsvydesign) S3method(svyboxplot, DBIsvydesign) S3method(svyboxplot, default) S3method(svycdf, default) S3method(svycdf, DBIsvydesign) S3method(plot, svystat) S3method(plot, svrepstat) S3method(barplot, svystat) S3method(barplot, svrepstat) S3method(plot, svyby) S3method(barplot, svyby) S3method(dotchart, svystat) S3method(dotchart, svrepstat) S3method(dotchart, svyby) S3method(dotchart, default) S3method(predict, svyglm) S3method(predict, svrepglm) S3method(predict, svyolr) S3method(predict, svyratio) S3method(predict, svyratio_separate) S3method(coef,svyratio) S3method(svycoxph,survey.design) S3method(svycoxph,svyrep.design) S3method(svycoxph, DBIsvydesign) S3method(svysurvreg,survey.design) S3method(residuals, svrepglm) S3method(residuals, svyglm) S3method(residuals, svysurvreg) S3method(coef,svrepstat) S3method(coef,svystat) S3method(coef,svyby) S3method(coef,svymle) S3method(coef, svyolr) S3method(coef, svyglm) S3method(coef, newsvyquantile) S3method(coef, oldsvyquantile) S3method(confint,svyglm) S3method(confint, svyciprop) S3method(confint, newsvyquantile) S3method(confint, oldsvyquantile) S3method(SE,default) S3method(SE,svrepstat) S3method(SE,svystat) S3method(SE,svyby) S3method(SE,newsvyquantile) S3method(SE,oldsvyquantile) S3method(SE,svyratio) S3method(vcov,svyrep.design) S3method(vcov, newsvyquantile) S3method(vcov, oldsvyquantile) S3method(vcov, svrepstat) S3method(vcov, svyglm) S3method(vcov, svymle) S3method(vcov, svystat) S3method(vcov, svyby) S3method(vcov, svyratio) S3method(logLik,svyglm) S3method(logLik,svrepglm) S3method(extractAIC,svyglm) S3method(extractAIC,svrepglm) S3method(extractAIC,svyglm) S3method(anova,svyglm) S3method(AIC,svyglm) S3method(BIC,svyglm) S3method(anova,svycoxph) S3method(predict,svycoxph) S3method(extractAIC,svycoxph) S3method(extractAIC,svrepcoxph) S3method(AIC,svycoxph) S3method(svycontrast,svrepstat) S3method(svycontrast,svystat) S3method(svycontrast,svyby) S3method(svycontrast,svyglm) S3method(svycontrast, svrepglm) S3method(svycontrast,svycoxph) S3method(svycontrast, svrepcoxph) S3method(svycontrast,default) S3method(svycontrast, svyvar) S3method(image,svyrep.design) S3method(image,pps) S3method(plot,svysmooth) S3method(print,svysmooth) S3method(lines,svysmooth) S3method(svykm, survey.design) S3method(svykm, svyrep.design) S3method(svykm, DBIsvydesign) S3method(plot,svykm) S3method(print,svykm) S3method(print,svykmlist) S3method(lines, svykm) S3method(plot,svykmlist) S3method(quantile, svykm) S3method(confint,svykm) S3method(svyolr,survey.design2) S3method(svyolr,svyrep.design) S3method(svyolr,DBIsvydesign) S3method(vcov,svyolr) S3method(summary, svyolr) S3method(print, svyolr) S3method(print, summary.svyolr) S3method(ftable,svrepstat) S3method(ftable,svystat) S3method(ftable,svyby) S3method(weights, nonresponse) S3method(weights, survey.design) S3method(weights, survey_fpc) S3method(weights, svyrep.design) S3method(summary,survey.design) S3method(summary,survey.design2) S3method(summary,twophase) S3method(summary, twophase2) S3method(summary,svyrep.design) S3method(summary,svrepglm) S3method(summary,svreptable) S3method(summary,svycoxph) S3method(summary,svyglm) S3method(summary,svymle) S3method(summary,svytable) S3method(deviance, coxph) S3method(deviance, svycoxph) S3method(print,summary.survey.design) S3method(print,summary.survey.design2) S3method(print,summary.twophase) S3method(print,summary.twophase2) S3method(print,summary.svyrep.design) S3method(print,summary.svyglm) S3method(print,summary.svytable) S3method(print,svycdf) S3method(plot,svycdf) S3method(print, nonresponse) S3method(print, nonresponseSubset) S3method(print, regTermTest) S3method(print, regTermTestLRT) S3method(print, regTermTestWW) S3method(print, svrepstat) S3method(print, svystat) S3method(print, survey.design) S3method(print, survey.design2) S3method(print, svyrep.design) S3method(print, svyglm) S3method(print, svymle) S3method(print, oldsvyquantile) S3method(print, svyratio) S3method(print, svyratio_separate) S3method(print, twophase) S3method(print, twophase2) S3method(print, calfun) S3method(print, svyvar) S3method(print, seqanova.svycoxph) S3method(print, seqanova.svyglm) S3method(as.matrix, svyvar) S3method(print, svrepvar) S3method(as.matrix, svrepvar) S3method(withReplicates, svyrep.design) S3method(withReplicates, svrepvar) S3method(withReplicates, svrepstat) S3method(withReplicates, svyimputationList) S3method(dim,repweights_compressed) S3method(dim,survey.design) S3method(dim,twophase) S3method(dim,twophase2) S3method(dim, DBIsvydesign) S3method(dim, DBIrepdesign) S3method(dim, svyrep.design) S3method(dim, svyimputationList) S3method(dimnames,survey.design) S3method(dimnames,svyrep.design) S3method(dimnames,twophase) S3method(dimnames, DBIsvydesign) S3method(dimnames, DBIrepdesign) S3method(dimnames, svyimputationList) S3method(dimnames, repweights_compressed) S3method(degf, survey.design2) S3method(degf, svyrep.design) S3method(degf, pps) S3method(degf, twophase) S3method(degf, twophase2) S3method(cv,default) S3method(cv,svyratio) S3method(deff,default) S3method(deff,svyby) S3method(postStratify,survey.design) S3method(postStratify,svyrep.design) S3method(postStratify,twophase) S3method(postStratify,twophase2) S3method(postStratify,DBIsvydesign) S3method(calibrate,survey.design2) S3method(calibrate,svyrep.design) S3method(calibrate,twophase) S3method(calibrate,twophase2) S3method(calibrate, DBIsvydesign) S3method(calibrate, pps) S3method(estWeights,data.frame) S3method(estWeights,twophase) S3method(compressWeights, default) S3method(compressWeights, repweights_compressed) S3method(compressWeights, svyrep.design) S3method(trimWeights, svyrep.design) S3method(trimWeights, survey.design2) S3method(subset,survey.design) S3method(subset,svyrep.design) S3method(subset,twophase) S3method(subset,twophase2) S3method(subset, DBIsvydesign) S3method(update,survey.design) S3method(update,svyrep.design) S3method(update,twophase) S3method(update,twophase2) S3method(update,DBIsvydesign) S3method(update,svyimputationList) S3method(transform,survey.design) S3method(transform,svyrep.design) S3method(transform,twophase) S3method(transform,twophase2) S3method(transform,DBIsvydesign) S3method(transform,svyimputationList) S3method(svyby, default) S3method(svyby, DBIsvydesign) S3method(svyby, survey.design2) S3method(regcalibrate, survey.design2) S3method(regcalibrate, svyrep.design) S3method(na.exclude,survey.design) S3method(na.exclude,twophase) S3method(na.fail,survey.design) S3method(na.fail,twophase) S3method(na.omit,survey.design) S3method(na.omit,twophase) S3method(model.frame,survey.design) S3method(model.frame,svycoxph) S3method(model.frame,svyrep.design) S3method(model.frame, twophase) S3method(model.frame, twophase2) S3method(model.frame, svyolr) S3method(as.matrix,repweights) S3method(as.matrix, repweights_compressed) S3method(as.vector, repweights_compressed) S3method(as.data.frame,svrepstat) S3method(as.data.frame,svystat) S3method(with,svyimputationList) S3method(print, svyimputationList) S3method(subset, svyimputationList) S3method(print, svyDBimputationList) S3method(with, svyDBimputationList) S3method(update, svyDBimputationList) S3method(close, svyDBimputationList) S3method(open, svyDBimputationList) S3method(subset, svyDBimputationList) S3method(withPV, survey.design) S3method(withPV, svyrep.design) S3method(confint,svystat) S3method(confint,svrepstat) S3method(confint, svyratio) S3method(confint, svyby) S3method(confint,svyttest) S3method(`[`, survey.design) S3method(`[`, survey.design2) S3method(`[`, svyrep.design) S3method(`[`, twophase) S3method(`[`, twophase2) S3method(`[`, repweights_compressed) S3method(`[`, nonresponse) S3method(`[`, DBIsvydesign) S3method(`[`, DBIrepdesign) S3method(`[<-`, survey.design) S3method(`names<-`, svrepstat) S3method(svylogrank,survey.design2) S3method(svylogrank,svyrep.design) S3method(svylogrank,twophase) S3method(svylogrank,DBIsvydesign) S3method(svyranktest,survey.design) S3method(svyranktest,svyrep.design) S3method(svyranktest,DBIsvydesign) S3method(svyivreg,survey.design) S3method(svyivreg, svyrep.design) S3method(summary, svyivreg) S3method(vcov, svyivreg) S3method(psrsq,glm) S3method(psrsq,svyglm) S3method(cal_names, survey.design) S3method(cal_names, DBIsvydesign) S3method(svynls, DBIsvydesign) S3method(svynls, survey.design2) S3method(svynls, svyrep.design) S3method(print, svynls) S3method(summary, svynls) S3method(coef,svynls) S3method(vcov,svynls) S3method(svyscoretest,svyglm) S3method(unwrap,default) S3method(unwrap2, default) S3method(unwrap2, newsvyquantile) S3method(svyxvar,xdesign) S3method(svymean, xdesign) S3method(svyglm, xdesign) S3method(print,xdesign) S3method(degf, xdesign) S3method(update,xdesign) S3method(transform, xdesign) S3method(subset, xdesign) S3method("[", xdesign) S3method(svyby, xdesign) S3method(dim, xdesign) S3method(dimnames, xdesign) S3method(estfuns, coxph) S3method(estfuns, glm) S3method(estfuns, lm) S3method(sigma, svyglm) S3method(sigma, svrepglm) S3method(pps_design, ppsdcheck) S3method(pps_design, ppsdelta) S3method(pps_design, HR) S3method(pps_design, ppsmat) S3method(pps_design, character) S3method(survfit,svycoxph) S3method(influence,svystat) S3method(estWeights,twophase2) S3method(postStratify,pps) S3method(svycontrast,svyolr) S3method(svylogrank,twophase2) S3method(svytotal,xdesign) S3method(svysmoothArea, survey.design) S3method(svysmoothArea, default) S3method(svysmoothUnit, survey.design) S3method(svysmoothUnit, default) S3method(summary, svysae) survey/THANKS0000644000176200001440000000562514565514512012542 0ustar liggesusersThe survey package has benefited from code, bug reports, and feature requests from users, including: Justin Allen Daniel Almirall Guiseppe Antonaci Vincent Arel-Bundock Pedro Luis Baldoni Robert Baskin Asia Bento Maciej Beręsewicz Brad Biggerstaff Milan Bouchet-Valat Adrienne Bradford Norman Breslow Arthur Burke Ron Burns Trent Buskirk Stefano Calza Shelby Chartkoff Henry Chiem David Collins Anthony Damico Wade Davis Qiaolan Deng Neil Diamond Michael Donohue Rex Dwyer Melanie Edwards Adam Elder Hannah Evans Francisco Fabuel Boris Fazio Bob Fay Daniel Fernandes Greg Freedman Ben French Jacques Ferrez Natalie Gallagher Daniella Gollinelli Barry Graubard Alistair Gray James Greiner Zachary Grinspan Brian Guay Alois Haslinger Kieran Healy Lucia Hindorff Jeffery Hughes Beat Hulliger Steven Johnston David Eduardo Jorquera Petersen Lauren Kennedy Ehsan Kharim Ward Kinkade Stas Kolenikov Franziska Kößler Scott Kostyshak Alex Kowarik Michael Laviolette Thomas Leeper Thomas Loughlin Chandler McClellan Klaus Ignacio Lehmann Melendez Benmei Liu Sebastien Lucie Jean Paul Lucas Lisa McShane Ana Patricia Martins Tapan Mehta Isabelle Michaud Daryl Morris Joey Morris Kirill Mueller Daniel Oehm Jean Opsomer Raymond Pan Gonzalo Perez Djalma Pessoa Molly Petersen Elizabeth Purdom Ana Quiterio Gillian Raab Christian Raschke Brian Ripley Fred Rohde Steve Roberts Andrew Robinson Ángel Rodríguez Laso Mark Rosenstein Benjamin Schneider Dirk Schumacher Dan Simpson Pam Shaw Bryan Shepherd Keiran Shao Lee Sieswerda Phil Smith Matthew Soldner Corey Sparks Elena Stern Ingmar Sturm Terry Therneau Takahiro Tsuchiya Richard Valliant Tobias Verbeke Renzo Vettori Chris Webb Justin Wishart Steve White Jasper Yang Zhiwen Yao Thomas Yokota Diego Zardetto Alan Zaslavsky Stephanie Zimmer Jing Zhang Tomasz Żółtak and from dicussions with Chris Wild and Alastair Scott. Some work on the package while I was on sabbatical was funded by Fondation Leducq through a grant to the LINAT collaboration and by the Marsden Fund through a grant to the University of Auckland. Much of the new methodology from 2009 to 2016 was supported by the Marsden Fund, in particular the developments of the Rao-Scott likelihood Patrick Aboyoun did the S-PLUS port of version 3.6-8 John Preston provided an example of the multistage rescaled bootstrap for testing. The svyolr() code and the likelihood-based confidence intervals are largely ripped from the MASS package of Venables & Ripley, and parts of the code for svycoxph() are modified from Therneau's survival package. 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E}ts͚Ħ8rL?'Bxn J-tq]Ex1h}mp[HHW  $JGROLaDI\ry1/=|Aw͎" t>Ί&G` a &*FɾHՍV-180DE=)w#o{ f6m+5ǂ(*7_ɒ?}U&6h+dzCrV լ.voxV~P  ʧizuU3y [t-A֖` {~(H:5+Cg1[>Ο{Ӧ߇m 7`٧#p YLl!0 YZsurvey/data/fpc.rda0000644000176200001440000000055514576431435014001 0ustar liggesusers r0b```b`afd`b2Y# 'fN+H@&10 D;0`́s@i(-<Tsi9"q7G*NNaKKKJK; JPqޙ3h)3gAJּb m6 {qIQbIf LMAq)VQ1eX~0c)%9I9P{`*M+.I-@S)hdU`d```hneh=-(7'zsY} D&QDbapk@1Vs8*/CN 41` c01` s"n/Jrn`!cV ,ۧhIsurvey/data/myco.rda0000644000176200001440000000272614576431435014202 0ustar liggesusersBZh91AY&SYlp$hӯBA`@)`>8  tU `iPihL65RMh&h?ʣC@?RI(444MR(&`S&##&&!aH䞣ѨC@ W %Dƪr @$ . bYCF[]׸obT3ҚT@HXTDj)fBYcNJ>^PR^,IrªQH&H @P8ڔ"}*PP !)JljLVTE^DMPl9=G|V=[cM mzm6ymmNm$I:ѕUюu<˦mG\MfIJ0 ▲U7@3rMI$Q]><= L6X* Fq)p#ĥUpyke֭mh-_$42%e6Tꖷ'P)mTUL ASquz/.`$I*Jym$M$ I4$DII$M$i]mݤJIZJo{ؤl m%xH!"- PfU +*@eV`@UePǭ^;޵kZҰ$mw$ rB" /u99ε&s.`T(I$Q$IPI$DDDACMiJRs!"N$ !!ȤR4D@xMqDFPF$kMmiᶴ۶$OMmI$>/survey/data/mu284.rda0000644000176200001440000000051114576431435014100 0ustar liggesusersRN0FAP$>!@v+`@b`AbbjHE`!ҋs~x%#@׭\gsA!{Cf6@l܀)B\'5pAmZVtEV#o?Z&ej!idʦpX;tDN/\nu5Lk0aZôi-ZLk{CTY޳YC `yQ#S{q.ˈ"?7jrܾ d:0}. \code{"integration"} requires the \code{CompQuadForm} package. For \code{pchisqsum} it uses Farebrother's algorithm if all \code{a>0}. For \code{pFsum} or when some \code{a<0} it inverts the characteristic function using the algorithm of Davies (1980). These algorithms are highly accurate for the lower tail probability, but they obtain the upper tail probability by subtraction from 1 and so fail completely when the upper tail probability is comparable to machine epsilon or smaller. If the \code{CompQuadForm} package is not present, a warning is given and the saddlepoint approximation is used. \code{"saddlepoint"} uses Kuonen's saddlepoint approximation. This is moderately accurate even very far out in the upper tail or with some \code{a=0} and does not require any additional packages. The relative error in the right tail is uniformly bounded for all \code{x} and decreases as p increases. This method is implemented in pure R and so is slower than the \code{"integration"} method. The distribution in \code{pFsum} is standardised so that a likelihood ratio test can use the same \code{x} value as in \code{pchisqsum}. That is, the linear combination of chi-squareds is multiplied by \code{ddf} and then divided by an independent chi-squared with \code{ddf} degrees of freedom. } \references{ Chen, T., & Lumley T. (2019). Numerical evaluation of methods approximating the distribution of a large quadratic form in normal variables. Computational Statistics and Data Analysis, 139, 75-81. Davies RB (1973). "Numerical inversion of a characteristic function" Biometrika 60:415-7 Davies RB (1980) "Algorithm AS 155: The Distribution of a Linear Combination of chi-squared Random Variables" Applied Statistics,Vol. 29, No. 3 (1980), pp. 323-333 P. Duchesne, P. Lafaye de Micheaux (2010) "Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods", Computational Statistics and Data Analysis, Volume 54, (2010), 858-862 Farebrother R.W. (1984) "Algorithm AS 204: The distribution of a Positive Linear Combination of chi-squared random variables". Applied Statistics Vol. 33, No. 3 (1984), p. 332-339 Kuonen D (1999) Saddlepoint Approximations for Distributions of Quadratic Forms in Normal Variables. Biometrika, Vol. 86, No. 4 (Dec., 1999), pp. 929-935 } \seealso{\code{\link{pchisq}}} \examples{ x <- 2.7*rnorm(1001)^2+rnorm(1001)^2+0.3*rnorm(1001)^2 x.thin<-sort(x)[1+(0:50)*20] p.invert<-pchisqsum(x.thin,df=c(1,1,1),a=c(2.7,1,.3),method="int" ,lower=FALSE) p.satt<-pchisqsum(x.thin,df=c(1,1,1),a=c(2.7,1,.3),method="satt",lower=FALSE) p.sadd<-pchisqsum(x.thin,df=c(1,1,1),a=c(2.7,1,.3),method="sad",lower=FALSE) plot(p.invert, p.satt,type="l",log="xy") abline(0,1,lty=2,col="purple") plot(p.invert, p.sadd,type="l",log="xy") abline(0,1,lty=2,col="purple") pchisqsum(20, df=c(1,1,1),a=c(2.7,1,.3), lower.tail=FALSE,method="sad") pFsum(20, df=c(1,1,1),a=c(2.7,1,.3), ddf=49,lower.tail=FALSE,method="sad") pFsum(20, df=c(1,1,1),a=c(2.7,1,.3), ddf=1000,lower.tail=FALSE,method="sad") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{distribution}% __ONLY ONE__ keyword per line survey/man/svyranktest.Rd0000644000176200001440000000537314226370750015264 0ustar liggesusers\name{svyranktest} \alias{svyranktest} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Design-based rank tests } \description{ Design-based versions of k-sample rank tests. The built-in tests are all for location hypotheses, but the user could specify others. } \usage{ svyranktest(formula, design, test = c("wilcoxon", "vanderWaerden", "median","KruskalWallis"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ Model formula \code{y~g} for outcome variable \code{y} and group \code{g} } \item{design}{ A survey design object } \item{test}{ Which rank test to use: Wilcoxon, van der Waerden's normal-scores test, Mood's test for the median, or a function \code{f(r,N)} where \code{r} is the rank and \code{N} the estimated population size. "KruskalWallis" is a synonym for "wilcoxon" for more than two groups. } \item{\dots}{ for future expansion} } \details{These tests are for the null hypothesis that the population or superpopulation distributions of the response variable are different between groups, targeted at population or superpopulation alternatives. The 'ranks' are defined as quantiles of the pooled distribution of the variable, so they do not just go from 1 to N; the null hypothesis does not depend on the weights, but the ranks do. The tests reduce to the usual Normal approximations to the usual rank tests under iid sampling. Unlike the traditional rank tests, they are not exact in small samples. } \value{ Object of class \code{htest} Note that with more than two groups the \code{statistic} element of the return value holds the numerator degrees of freedom and the \code{parameter} element holds the test statistic. } \references{ Lumley, T., & Scott, A. J. (2013). Two-sample rank tests under complex sampling. BIOMETRIKA, 100 (4), 831-842. } \seealso{ \code{\link{svyttest}}, \code{\link{svylogrank}} } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data=apiclus1) svyranktest(ell~comp.imp, dclus1) svyranktest(ell~comp.imp, dclus1, test="median") svyranktest(ell~stype, dclus1) svyranktest(ell~stype, dclus1, test="median") str(svyranktest(ell~stype, dclus1)) ## upper quartile svyranktest(ell~comp.imp, dclus1, test=function(r,N) as.numeric(r>0.75*N)) quantiletest<-function(p){ rval<-function(r,N) as.numeric(r>(N*p)) attr(rval,"name")<-paste(p,"quantile") rval } svyranktest(ell~comp.imp, dclus1, test=quantiletest(0.5)) svyranktest(ell~comp.imp, dclus1, test=quantiletest(0.75)) ## replicate weights rclus1<-as.svrepdesign(dclus1) svyranktest(ell~stype, rclus1) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{htest}% __ONLY ONE__ keyword per line survey/man/salamander.Rd0000644000176200001440000000364214145646540014776 0ustar liggesusers\name{salamander} \docType{data} \alias{salamander} \concept{generalized linear mixed model} \title{Salamander mating data set from McCullagh and Nelder (1989)} \description{This data set presents the outcome of three experiments conducted at the University of Chicago in 1986 to study interbreeding between populations of mountain dusky salamanders (McCullagh and Nelder, 1989, Section 14.5). The analysis here is from Lumley (1998, section 5.3)} \usage{data(salamander)} \format{ A data frame with the following columns: \describe{ \item{Mate}{Whether the salamanders mated (1) or did not mate (0).} \item{Cross}{Cross between female and male type. A factor with four levels: \code{R/R},\code{R/W},\code{W/R}, and \code{W/W}. The type of the female salamander is listed first and the male is listed second. Rough Butt is represented by R and White Side is represented by W. For example, \code{Cross=W/R} indicates a White Side female was crossed with a Rough Butt male.} \item{Male}{Identification number of the male salamander. A factor.} \item{Female}{Identification number of the female salamander. A factor.} } } \references{McCullagh P. and Nelder, J. A. (1989) \emph{Generalized Linear Models}. Chapman and Hall/CRC. Lumley T (1998) PhD thesis, University of Washington } \examples{ data(salamander) salamander$mixed<-with(salamander, Cross=="W/R" | Cross=="R/W") salamander$RWvsWR<-with(salamander, ifelse(mixed, ((Cross=="R/W")-(Cross=="W/R"))/2, 0)) xsalamander<-xdesign(id=list(~Male, ~Female), data=salamander, overlap="unbiased") ## Adjacency matrix ## Blocks 1 and 2 are actually the same salamanders, but ## it's traditional to pretend they are independent. image(xsalamander$adjacency) ## R doesn't allow family=binomial(identity) success <- svyglm(Mate~mixed+RWvsWR, design=xsalamander, family=quasi(link="identity", variance="mu(1-mu)")) summary(success) } \keyword{datasets} survey/man/with.svyimputationList.Rd0000644000176200001440000000376713722115566017437 0ustar liggesusers\name{with.svyimputationList} \alias{with.svyimputationList} \alias{subset.svyimputationList} %- Also NEED an '\alias' for EACH other topic documented here. \title{Analyse multiple imputations} \description{ Performs a survey analysis on each of the designs in a \code{svyimputationList} objects and returns a list of results suitable for \code{MIcombine}. The analysis may be specified as an expression or as a function. } \usage{ \method{with}{svyimputationList}(data, expr, fun, ...,multicore=getOption("survey.multicore")) \method{subset}{svyimputationList}(x, subset,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{data,x}{A \code{svyimputationList} object } \item{expr}{An expression giving a survey analysis} \item{fun}{A function taking a survey design object as its argument } \item{\dots}{for future expansion } \item{multicore}{Use \code{multicore} package to distribute imputed data sets over multiple processors?} \item{subset}{An logical expression specifying the subset} } \value{ A list of the results from applying the analysis to each design object. } \seealso{\code{MIcombine}, in the \code{mitools} package } \examples{ library(mitools) data.dir<-system.file("dta",package="mitools") files.men<-list.files(data.dir,pattern="m.\\\\.dta$",full=TRUE) men<-imputationList(lapply(files.men, foreign::read.dta, warn.missing.labels=FALSE)) files.women<-list.files(data.dir,pattern="f.\\\\.dta$",full=TRUE) women<-imputationList(lapply(files.women, foreign::read.dta, warn.missing.labels=FALSE)) men<-update(men, sex=1) women<-update(women,sex=0) all<-rbind(men,women) designs<-svydesign(id=~id, strata=~sex, data=all) designs results<-with(designs, svymean(~drkfre)) MIcombine(results) summary(MIcombine(results)) repdesigns<-as.svrepdesign(designs, type="boot", replicates=50) MIcombine(with(repdesigns, svymean(~drkfre))) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey }% __ONLY ONE__ keyword per line survey/man/svysmooth.Rd0000644000176200001440000000623013517345016014732 0ustar liggesusers\name{svysmooth} \alias{svysmooth} \alias{svysmooth.default} \alias{plot.svysmooth} \alias{print.svysmooth} \alias{lines.svysmooth} \alias{make.panel.svysmooth} %- Also NEED an '\alias' for EACH other topic documented here. \title{Scatterplot smoothing and density estimation} \description{ Scatterplot smoothing and density estimation for probability-weighted data. } \usage{ svysmooth(formula, design, ...) \method{svysmooth}{default}(formula, design, method = c("locpoly", "quantreg"), bandwidth = NULL, quantile, df = 4, ...) \method{plot}{svysmooth}(x, which=NULL, type="l", xlabs=NULL, ylab=NULL,...) \method{lines}{svysmooth}(x,which=NULL,...) make.panel.svysmooth(design,bandwidth=NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{One-sided formula for density estimation, two-sided for smoothing} \item{design}{Survey design object} \item{method}{local polynomial smoothing for the mean or regression splines for quantiles} \item{bandwidth}{Smoothing bandwidth for "locpoly" or \code{NULL} for automatic choice} \item{quantile}{quantile to be estimated for "quantreg"} \item{df}{Degrees of freedom for "quantreg"} \item{which}{Which plots to show (default is all)} \item{type}{as for \code{plot}} \item{xlabs}{Optional vector of x-axis labels} \item{ylab}{Optional y-axis label} \item{\dots}{More arguments} \item{x}{Object of class \code{svysmooth}} } \details{ \code{svysmooth} does one-dimensional smoothing. If \code{formula} has multiple predictor variables a separate one-dimensional smooth is performed for each one. For \code{method="locpoly"} the extra arguments are passed to \code{locpoly} from the KernSmooth package, for \code{method="quantreg"} they are passed to \code{rq} from the quantreg package. The automatic choice of bandwidth for \code{method="locpoly"} uses the default settings for \code{dpik} and \code{dpill} in the KernSmooth package. \code{make.panel.svysmooth()} makes a function that plots points and draws a weighted smooth curve through them, a weighted replacement for \code{\link{panel.smooth}} that can be passed to functions such as \code{\link{termplot}} or \code{\link{plot.lm}}. The resulting function has a \code{span} argument that will set the bandwidth; if this is not specified the automatic choice will be used. } \value{ An object of class \code{svysmooth}, a list of lists, each with \code{x} and \code{y} components. } \seealso{\code{\link{svyhist}} for histograms} \examples{ data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) smth<-svysmooth(api00~api99+ell,dstrat) dens<-svysmooth(~api99, dstrat,bandwidth=30) dens1<-svysmooth(~api99, dstrat) qsmth<-svysmooth(api00~ell,dstrat, quantile=0.75, df=3,method="quantreg") plot(smth) plot(smth, which="ell",lty=2,ylim=c(500,900)) lines(qsmth, col="red") svyhist(~api99,design=dstrat) lines(dens,col="purple",lwd=3) lines(dens1, col="forestgreen",lwd=2) m<-svyglm(api00~sin(api99/100)+stype, design=dstrat) termplot(m, data=model.frame(dstrat), partial.resid=TRUE, se=TRUE, smooth=make.panel.svysmooth(dstrat)) } \keyword{hplot}% at least one, from doc/KEYWORDS \keyword{survey} survey/man/svychisq.Rd0000755000176200001440000002030614230114357014526 0ustar liggesusers\name{svytable} \alias{svreptable} \alias{svytable} \alias{svytable.svyrep.design} \alias{svytable.survey.design} \alias{svychisq} \alias{svychisq.survey.design} \alias{svychisq.svyrep.design} \alias{summary.svytable} \alias{print.summary.svytable} \alias{summary.svreptable} \alias{degf} \alias{degf.svyrep.design} \alias{degf.survey.design2} \alias{degf.twophase} %- Also NEED an '\alias' for EACH other topic documented here. \title{Contingency tables for survey data} \description{ Contingency tables and chisquared tests of association for survey data. } \usage{ \method{svytable}{survey.design}(formula, design, Ntotal = NULL, round = FALSE,...) \method{svytable}{svyrep.design}(formula, design, Ntotal = sum(weights(design, "sampling")), round = FALSE,...) \method{svychisq}{survey.design}(formula, design, statistic = c("F", "Chisq","Wald","adjWald","lincom", "saddlepoint","wls-score"),na.rm=TRUE,...) \method{svychisq}{svyrep.design}(formula, design, statistic = c("F", "Chisq","Wald","adjWald","lincom", "saddlepoint","wls-score"),na.rm=TRUE,...) \method{summary}{svytable}(object, statistic = c("F","Chisq","Wald","adjWald","lincom","saddlepoint"),...) degf(design, ...) \method{degf}{survey.design2}(design, ...) \method{degf}{svyrep.design}(design, tol=1e-5,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{Model formula specifying margins for the table (using \code{+} only)} \item{design}{survey object} \item{statistic}{See Details below} \item{Ntotal}{A population total or set of population stratum totals to normalise to.} \item{round}{Should the table entries be rounded to the nearest integer?} \item{na.rm}{Remove missing values} \item{object}{Output from \code{svytable}} \item{...}{For \code{svytable} these are passed to \code{xtabs}. Use \code{exclude=NULL}, \code{na.action=na.pass} to include \code{NA}s in the table} \item{tol}{Tolerance for \code{\link{qr}} in computing the matrix rank} } \details{ The \code{svytable} function computes a weighted crosstabulation. This is especially useful for producing graphics. It is sometimes easier to use \code{\link{svytotal}} or \code{\link{svymean}}, which also produce standard errors, design effects, etc. The frequencies in the table can be normalised to some convenient total such as 100 or 1.0 by specifying the \code{Ntotal} argument. If the formula has a left-hand side the mean or sum of this variable rather than the frequency is tabulated. The \code{Ntotal} argument can be either a single number or a data frame whose first column gives the (first-stage) sampling strata and second column the population size in each stratum. In this second case the \code{svytable} command performs `post-stratification': tabulating and scaling to the population within strata and then adding up the strata. As with other \code{xtabs} objects, the output of \code{svytable} can be processed by \code{ftable} for more attractive display. The \code{summary} method for \code{svytable} objects calls \code{svychisq} for a test of independence. \code{svychisq} computes first and second-order Rao-Scott corrections to the Pearson chisquared test, and two Wald-type tests. The default (\code{statistic="F"}) is the Rao-Scott second-order correction. The p-values are computed with a Satterthwaite approximation to the distribution and with denominator degrees of freedom as recommended by Thomas and Rao (1990). The alternative \code{statistic="Chisq"} adjusts the Pearson chisquared statistic by a design effect estimate and then compares it to the chisquared distribution it would have under simple random sampling. The \code{statistic="Wald"} test is that proposed by Koch et al (1975) and used by the SUDAAN software package. It is a Wald test based on the differences between the observed cells counts and those expected under independence. The adjustment given by \code{statistic="adjWald"} reduces the statistic when the number of PSUs is small compared to the number of degrees of freedom of the test. Thomas and Rao (1987) compare these tests and find the adjustment benefical. \code{statistic="lincom"} replaces the numerator of the Rao-Scott F with the exact asymptotic distribution, which is a linear combination of chi-squared variables (see \code{\link{pchisqsum}}, and \code{statistic="saddlepoint"} uses a saddlepoint approximation to this distribution. The \code{CompQuadForm} package is needed for \code{statistic="lincom"} but not for \code{statistic="saddlepoint"}. The saddlepoint approximation is especially useful when the p-value is very small (as in large-scale multiple testing problems). \code{statistic="wls-score"} is an experimental implementation of the weighted least squares score test of Lipsitz et al (2015). It is not identical to that paper, for example, I think the denominator degrees of freedom need to be reduced by JK for a JxK table, not (J-1)(K-1). And it's very close to the "adjWald" test. For designs using replicate weights the code is essentially the same as for designs with sampling structure, since the necessary variance computations are done by the appropriate methods of \code{\link{svytotal}} and \code{\link{svymean}}. The exception is that the degrees of freedom is computed as one less than the rank of the matrix of replicate weights (by \code{degf}). At the moment, \code{svychisq} works only for 2-dimensional tables. } \value{ The table commands return an \code{xtabs} object, \code{svychisq} returns a \code{htest} object. } \references{ Davies RB (1973). "Numerical inversion of a characteristic function" Biometrika 60:415-7 P. Duchesne, P. Lafaye de Micheaux (2010) "Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods", Computational Statistics and Data Analysis, Volume 54, 858-862 Koch, GG, Freeman, DH, Freeman, JL (1975) "Strategies in the multivariate analysis of data from complex surveys" International Statistical Review 43: 59-78 Stuart R. Lipsitz, Garrett M. Fitzmaurice, Debajyoti Sinha, Nathanael Hevelone, Edward Giovannucci, and Jim C. Hu (2015) "Testing for independence in JxK contingency tables with complex sample survey data" Biometrics 71(3): 832-840 Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contigency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. Sribney WM (1998) "Two-way contingency tables for survey or clustered data" Stata Technical Bulletin 45:33-49. Thomas, DR, Rao, JNK (1987) "Small-sample comparison of level and power for simple goodness-of-fit statistics under cluster sampling" JASA 82:630-636 } \note{Rao and Scott (1984) leave open one computational issue. In computing `generalised design effects' for these tests, should the variance under simple random sampling be estimated using the observed proportions or the the predicted proportions under the null hypothesis? \code{svychisq} uses the observed proportions, following simulations by Sribney (1998), and the choices made in Stata} \seealso{\code{\link{svytotal}} and \code{\link{svymean}} report totals and proportions by category for factor variables. See \code{\link{svyby}} and \code{\link{ftable.svystat}} to construct more complex tables of summary statistics. See \code{\link{svyloglin}} for loglinear models. See \code{\link{regTermTest}} for Rao-Scott tests in regression models. See \url{https://notstatschat.rbind.io/2019/06/08/design-degrees-of-freedom-brief-note/} for an explanation of the design degrees of freedom with replicate weights. } \examples{ data(api) xtabs(~sch.wide+stype, data=apipop) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) summary(dclus1) (tbl <- svytable(~sch.wide+stype, dclus1)) plot(tbl) fourfoldplot(svytable(~sch.wide+comp.imp+stype,design=dclus1,round=TRUE), conf.level=0) svychisq(~sch.wide+stype, dclus1) summary(tbl, statistic="Chisq") svychisq(~sch.wide+stype, dclus1, statistic="adjWald") rclus1 <- as.svrepdesign(dclus1) summary(svytable(~sch.wide+stype, rclus1)) svychisq(~sch.wide+stype, rclus1, statistic="adjWald") } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{category}% __ONLY ONE__ keyword per line \keyword{htest}% __ONLY ONE__ keyword per line survey/man/surveyoptions.Rd0000644000176200001440000001035414462643117015635 0ustar liggesusers\name{surveyoptions} \alias{surveyoptions} \alias{survey.lonely.psu} \alias{survey.ultimate.cluster} \alias{survey.adjust.domain.lonely} \alias{survey.want.obsolete} \alias{survey.drop.replicates} \alias{survey.multicore} \alias{survey.replicates.mse} \alias{survey.use_rcpp} \title{Options for the survey package} \description{ This help page documents the options that control the behaviour of the survey package. } \details{ All the options for the survey package have names beginning with "survey". Four of them control standard error estimation. \code{options("survey.replicates.mse")} controls the default in \code{svrepdesign} and \code{as.svrepdesign} for computing variances. When \code{options("survey.replicates.mse")} is \code{TRUE}, the default is to create replicate weight designs that compute variances centered at the point estimate, rather than at the mean of the replicates. The option can be overridden by specifying the \code{mse} argument explicitly in \code{svrepdesign} and \code{as.svrepdesign}. The default is \code{FALSE}. When \code{options("survey.ultimate.cluster")} is \code{TRUE}, standard error estimation is based on independence of PSUs at the first stage of sampling, without using any information about subsequent stages. When \code{FALSE}, finite population corrections and variances are estimated recursively. See \code{\link{svyrecvar}} for more information. This option makes no difference unless first-stage finite population corrections are specified, in which case setting the option to \code{TRUE} gives the wrong answer for a multistage study. The only reason to use \code{TRUE} is for compatibility with other software that gives the wrong answer. Handling of strata with a single PSU that are not certainty PSUs is controlled by \code{options("survey.lonely.psu")}. The default setting is \code{"fail"}, which gives an error. Use \code{"remove"} to ignore that PSU for variance computation, \code{"adjust"} to center the stratum at the population mean rather than the stratum mean, and \code{"average"} to replace the variance contribution of the stratum by the average variance contribution across strata. As of version 3.4-2 \code{as.svrepdesign} also uses this option. The variance formulas for domain estimation give well-defined, positive results when a stratum contains only one PSU with observations in the domain, but are not unbiased. If \code{options("survey.adjust.domain.lonely")} is \code{TRUE} and \code{options("survey.lonely.psu")} is \code{"average"} or \code{"adjust"} the same adjustment for lonely PSUs will be used within a domain. Note that this adjustment is not available for replicate-weight designs, nor (currently) for raked, post-stratified, or calibrated designs. The fourth option is \code{options("survey.want.obsolete")}. This controls the warnings about using the deprecated pre-2.9.0 survey design objects. The behaviour of replicate-weight designs for self-representing strata is controlled by \code{options("survey.drop.replicates")}. When \code{TRUE}, various optimizations are used that take advantage of the fact that these strata do not contribute to the variance. The only reason ever to use \code{FALSE} is if there is a bug in the code for these optimizations. The fifth option controls the use of multiple processors with the \code{multicore} package. This option should not affect the values computed by any of the survey functions. If \code{TRUE}, all functions that are able to use multiple processors will do so by default. Using multiple processors may speed up calculations, but need not, especially if the computer is short on memory. The best strategy is probably to experiment with explicitly requesting \code{multicore=TRUE} in functions that support it, to see if there is an increase in speed before setting the global option. \code{survey.use_rcpp} controls whether the new C++ code for standard errors is used (vs the old R code). The factory setting is \code{TRUE} and the only reason to use \code{FALSE} is for comparisons. } \keyword{survey} survey/man/svyolr.Rd0000644000176200001440000000431714423551650014221 0ustar liggesusers\name{svyolr} \alias{svyolr} \alias{svyolr.survey.design2} \alias{svyolr.svyrep.design} \alias{predict.svyolr} %- Also NEED an '\alias' for EACH other topic documented here. \title{Proportional odds and related models } \description{ Fits cumulative link models: proportional odds, probit, complementary log-log, and cauchit. } \usage{ svyolr(formula, design, ...) \method{svyolr}{survey.design2}(formula, design, start, subset=NULL,..., na.action = na.omit,method = c("logistic", "probit", "cloglog", "cauchit")) \method{svyolr}{svyrep.design}(formula,design,subset=NULL,...,return.replicates=FALSE, multicore=getOption("survey.multicore")) \method{predict}{svyolr}(object, newdata, type = c("class", "probs"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{Formula: the response must be a factor with at least three levels} \item{design}{survey design object } \item{subset}{subset of the design to use; \code{NULL} for all of it} \item{\dots}{dots} \item{start}{Optional starting values for optimization} \item{na.action}{handling of missing values} \item{multicore}{Use \code{multicore} package to distribute computation of replicates across multiple processors?} \item{method}{Link function} \item{return.replicates}{return the individual replicate-weight estimates} \item{object}{object of class \code{svyolr}} \item{newdata}{new data for predictions} \item{type}{return vector of most likely class or matrix of probabilities} } \value{ An object of class \code{svyolr} } \author{The code is based closely on polr() from the MASS package of Venables and Ripley.} \seealso{\code{\link{svyglm}}, \code{\link{regTermTest}} } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) dclus1<-update(dclus1, mealcat=cut(meals,c(0,25,50,75,100))) m<-svyolr(mealcat~avg.ed+mobility+stype, design=dclus1) m ## Use regTermTest for testing multiple parameters regTermTest(m, ~avg.ed+stype, method="LRT") ## predictions summary(predict(m, newdata=apiclus2)) summary(predict(m, newdata=apiclus2, type="probs")) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/oldsvyquantile.Rd0000755000176200001440000001633613707712045015756 0ustar liggesusers\name{oldsvyquantile} \alias{oldsvyquantile} \alias{oldsvyquantile.survey.design} \alias{oldsvyquantile.svyrep.design} \alias{print.svyquantile} %- Also NEED an `\alias' for EACH other topic documented here. \title{Deprecated implementation of quantiles} \description{ Compute quantiles for data from complex surveys. \code{oldsvyquantile} is the version of the function from before version 4.1 of the package, available for backwards compatibility. See \code{\link{svyquantile}} for the current version } \usage{ \method{oldsvyquantile}{survey.design}(x, design, quantiles, alpha=0.05, ci=FALSE, method = "linear", f = 1, interval.type=c("Wald","score","betaWald"), na.rm=FALSE,se=ci, ties=c("discrete","rounded"), df=NULL,...) \method{oldsvyquantile}{svyrep.design}(x, design, quantiles, method ="linear", interval.type=c("probability","quantile"), f = 1, return.replicates=FALSE, ties=c("discrete","rounded"),na.rm=FALSE, alpha=0.05,df=NULL,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{A formula, vector or matrix} \item{design}{\code{survey.design} or \code{svyrep.design} object} \item{quantiles}{Quantiles to estimate} \item{method}{see \code{\link{approxfun}}} \item{f}{see \code{\link{approxfun}}} \item{ci}{Compute a confidence interval? (relatively slow; needed for \code{\link{svyby}})} \item{se}{Compute standard errors from the confidence interval length?} \item{alpha}{Level for confidence interval} \item{interval.type}{See Details below} \item{ties}{See Details below} \item{df}{Degrees of freedom for a t-distribution. \code{Inf} requests a Normal distribution, \code{NULL} uses \code{\link{degf}}. Not relevant for \code{type="betaWald"}} \item{return.replicates}{Return the replicate means?} \item{na.rm}{Remove \code{NA}s?} \item{...}{arguments for future expansion} } \details{ The definition of the CDF and thus of the quantiles is ambiguous in the presence of ties. With \code{ties="discrete"} the data are treated as genuinely discrete, so the CDF has vertical steps at tied observations. With \code{ties="rounded"} all the weights for tied observations are summed and the CDF interpolates linearly between distinct observed values, and so is a continuous function. Combining \code{interval.type="betaWald"} and \code{ties="discrete"} is (close to) the proposal of Shah and Vaish(2006) used in some versions of SUDAAN. Interval estimation for quantiles is complicated, because the influence function is not continuous. Linearisation cannot be used directly, and computing the variance of replicates is valid only for some designs (eg BRR, but not jackknife). The \code{interval.type} option controls how the intervals are computed. For \code{survey.design} objects the default is \code{interval.type="Wald"}. A 95\% Wald confidence interval is constructed for the proportion below the estimated quantile. The inverse of the estimated CDF is used to map this to a confidence interval for the quantile. This is the method of Woodruff (1952). For \code{"betaWald"} the same procedure is used, but the confidence interval for the proportion is computed using the exact binomial cdf with an effective sample size proposed by Korn & Graubard (1998). If \code{interval.type="score"} we use a method described by Binder (1991) and due originally to Francisco and Fuller (1986), which corresponds to inverting a robust score test. At the upper and lower limits of the confidence interval, a test of the null hypothesis that the cumulative distribution function is equal to the target quantile just rejects. This was the default before version 2.9. It is much slower than \code{"Wald"}, and Dorfman & Valliant (1993) suggest it is not any more accurate. Standard errors are computed from these confidence intervals by dividing the confidence interval length by \code{2*qnorm(alpha/2)}. For replicate-weight designs, ordinary replication-based standard errors are valid for BRR and Fay's method, and for some bootstrap-based designs, but not for jackknife-based designs. \code{interval.type="quantile"} gives these replication-based standard errors. The default, \code{interval.type="probability"} computes confidence on the probability scale and then transforms back to quantiles, the equivalent of \code{interval.type="Wald"} for \code{survey.design} objects (with \code{alpha=0.05}). There is a \code{confint} method for \code{svyquantile} objects; it simply extracts the pre-computed confidence interval. } \value{ returns a list whose first component is the quantiles and second component is the confidence intervals. For replicate weight designs, returns an object of class \code{svyrepstat}. } \author{Thomas Lumley} \seealso{ \code{\link{svykm}} for quantiles of survival curves \code{\link{svyciprop}} for confidence intervals on proportions. } \references{ Binder DA (1991) Use of estimating functions for interval estimation from complex surveys. \emph{Proceedings of the ASA Survey Research Methods Section} 1991: 34-42 Dorfman A, Valliant R (1993) Quantile variance estimators in complex surveys. Proceedings of the ASA Survey Research Methods Section. 1993: 866-871 Korn EL, Graubard BI. (1998) Confidence Intervals For Proportions With Small Expected Number of Positive Counts Estimated From Survey Data. Survey Methodology 23:193-201. Francisco CA, Fuller WA (1986) Estimation of the distribution function with a complex survey. Technical Report, Iowa State University. Shao J, Tu D (1995) \emph{The Jackknife and Bootstrap}. Springer. Shah BV, Vaish AK (2006) Confidence Intervals for Quantile Estimation from Complex Survey Data. Proceedings of the Section on Survey Research Methods. Woodruff RS (1952) Confidence intervals for medians and other position measures. JASA 57, 622-627. } \examples{ data(api) ## population quantile(apipop$api00,c(.25,.5,.75)) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) oldsvyquantile(~api00, dclus1, c(.25,.5,.75),ci=TRUE) oldsvyquantile(~api00, dclus1, c(.25,.5,.75),ci=TRUE,interval.type="betaWald") oldsvyquantile(~api00, dclus1, c(.25,.5,.75),ci=TRUE,df=NULL) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) (qapi<-oldsvyquantile(~api00, dclus1, c(.25,.5,.75),ci=TRUE, interval.type="score")) SE(qapi) #stratified sample dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) oldsvyquantile(~api00, dstrat, c(.25,.5,.75),ci=TRUE) #stratified sample, replicate weights # interval="probability" is necessary for jackknife weights rstrat<-as.svrepdesign(dstrat) oldsvyquantile(~api00, rstrat, c(.25,.5,.75), interval.type="probability") # BRR method data(scd) repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights) oldsvyquantile(~arrests+alive, design=scdrep, quantile=0.5, interval.type="quantile") oldsvyquantile(~arrests+alive, design=scdrep, quantile=0.5, interval.type="quantile",df=NULL) } \keyword{univar}% at least one, from doc/KEYWORDS \keyword{survey}% __ONLY ONE__ keyword per line survey/man/as.fpc.Rd0000644000176200001440000000227513517345016014036 0ustar liggesusers\name{as.fpc} \alias{as.fpc} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Package sample and population size data} \description{ This function creates an object to store the number of clusters sampled within each stratum (at each stage of multistage sampling) and the number of clusters available in the population. It is called by \code{svydesign}, not directly by the user. } \usage{ as.fpc(df, strata, ids,pps=FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{df}{A data frame or matrix with population size information} \item{strata}{A data frame giving strata at each stage} \item{ids}{A data frame giving cluster ids at each stage} \item{pps}{if \code{TRUE}, fpc information may vary within a stratum and must be specified as a proportion rather than a population sizes} } \details{ The population size information may be specified as the number of clusters in the population or as the proportion of clusters sampled. } \value{ An object of class \code{survey_fpc} } \seealso{\code{\link{svydesign}},\code{\link{svyrecvar}}} \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/svyratio.Rd0000755000176200001440000001164513640531200014535 0ustar liggesusers\name{svyratio} \alias{svyratio} \alias{print.svyratio} \alias{print.svyratio_separate} \alias{svyratio.svyrep.design} \alias{svyratio.survey.design} \alias{svyratio.survey.design2} \alias{svyratio.twophase} \alias{coef.svyratio} \alias{SE.svyratio} \alias{predict.svyratio} \alias{predict.svyratio_separate} \alias{confint.svyratio} %- Also NEED an `\alias' for EACH other topic documented here. \title{Ratio estimation} \description{ Ratio estimation and estimates of totals based on ratios for complex survey samples. Estimating domain (subpopulation) means can be done more easily with \code{\link{svymean}}. } \usage{ \method{svyratio}{survey.design2}(numerator=formula, denominator, design,separate=FALSE, na.rm=FALSE,formula, covmat=FALSE, deff=FALSE,influence=FALSE,...) \method{svyratio}{svyrep.design}(numerator=formula, denominator, design, na.rm=FALSE,formula, covmat=FALSE,return.replicates=FALSE,deff=FALSE, ...) \method{svyratio}{twophase}(numerator=formula, denominator, design, separate=FALSE, na.rm=FALSE,formula,...) \method{predict}{svyratio}(object, total, se=TRUE,...) \method{predict}{svyratio_separate}(object, total, se=TRUE,...) \method{SE}{svyratio}(object,...,drop=TRUE) \method{coef}{svyratio}(object,...,drop=TRUE) \method{confint}{svyratio}(object, parm, level = 0.95,df =Inf,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{numerator,formula}{formula, expression, or data frame giving numerator variable(s)} \item{denominator}{formula, expression, or data frame giving denominator variable(s) } \item{design}{survey design object} \item{object}{result of \code{svyratio}} \item{total}{vector of population totals for the denominator variables in \code{object}, or list of vectors of population stratum totals if \code{separate=TRUE}} \item{se}{Return standard errors?} \item{separate}{Estimate ratio separately for strata} \item{na.rm}{Remove missing values?} \item{covmat}{Compute the full variance-covariance matrix of the ratios} \item{deff}{Compute design effects} \item{return.replicates}{Return replicate estimates of ratios} \item{influence}{Return influence functions} \item{drop}{Return a vector rather than a matrix} \item{parm}{a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.} \item{level}{the confidence level required.} \item{df}{degrees of freedom for t-distribution in confidence interval, use \code{degf(design)} for number of PSUs minus number of strata} \item{...}{Other unused arguments for other methods} } \details{ The separate ratio estimate of a total is the sum of ratio estimates in each stratum. If the stratum totals supplied in the \code{total} argument and the strata in the design object both have names these names will be matched. If they do not have names it is important that the sample totals are supplied in the correct order, the same order as shown in the output of \code{summary(design)}. When \code{design} is a two-phase design, stratification will be on the second phase. } \value{ \code{svyratio} returns an object of class \code{svyratio}. The \code{predict} method returns a matrix of population totals and optionally a matrix of standard errors. } \references{Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley} \author{Thomas Lumley} \seealso{\code{\link{svydesign}} \code{\link{svymean}} for estimating proportions and domain means \code{\link{calibrate}} for estimators related to the separate ratio estimator. } \examples{ data(scd) ## survey design objects scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE, fpc=rep(5,6)) scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE) # convert to BRR replicate weights scd2brr <- as.svrepdesign(scdnofpc, type="BRR") # use BRR replicate weights from Levy and Lemeshow repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights) # ratio estimates svyratio(~alive, ~arrests, design=scddes) svyratio(~alive, ~arrests, design=scdnofpc) svyratio(~alive, ~arrests, design=scd2brr) svyratio(~alive, ~arrests, design=scdrep) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) ## domain means are ratio estimates, but available directly svyratio(~I(api.stu*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dstrat) svymean(~api.stu, subset(dstrat, comp.imp=="Yes")) ## separate and combined ratio estimates of total (sep<-svyratio(~api.stu,~enroll, dstrat,separate=TRUE)) (com<-svyratio(~api.stu, ~enroll, dstrat)) stratum.totals<-list(E=1877350, H=1013824, M=920298) predict(sep, total=stratum.totals) predict(com, total=sum(unlist(stratum.totals))) SE(com) coef(com) coef(com, drop=FALSE) confint(com) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/withPV.survey.design.Rd0000644000176200001440000000407413517345016016710 0ustar liggesusers\name{withPV.survey.design} \alias{withPV.survey.design} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Analyse plausible values in surveys } \description{ Repeats an analysis for each of a set of 'plausible values' in a survey data set, returning a list suitable for \code{mitools::MIcombine}. The default method works for both standard and replicate-weight designs but not for two-phase designs. } \usage{ \S3method{withPV}{survey.design}(mapping, data, action, rewrite=TRUE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{mapping}{ A formula or list of formulas describing each variable in the analysis that has plausible values. The left-hand side of the formula is the name to use in the analysis; the right-hand side gives the names in the dataset. } \item{data}{ A survey design object, as created by \code{svydesign} or \code{svrepdesign} } \item{action}{ With \code{rewrite=TRUE}, a function taking a survey design object as its only argument, or a quoted expression. With \code{rewrite=TRUE} a function taking a survey design object as its only argument, or a quoted expression with \code{.DESIGN} referring to the survey design object to be used. } \item{rewrite}{ Rewrite \code{action} before evaluating it (versus constructing new data sets) } \item{\dots}{ For methods } } \value{ A list of the results returned by each evaluation of \code{action}, with the call as an attribute. } \seealso{ \code{\link{with.svyimputationList}} } \examples{ if(require(mitools)){ data(pisamaths, package="mitools") des<-svydesign(id=~SCHOOLID+STIDSTD, strata=~STRATUM, nest=TRUE, weights=~W_FSCHWT+condwt, data=pisamaths) oo<-options(survey.lonely.psu="remove") results<-withPV(list(maths~PV1MATH+PV2MATH+PV3MATH+PV4MATH+PV5MATH), data=des, action=quote(svyglm(maths~ST04Q01*(PCGIRLS+SMRATIO)+MATHEFF+OPENPS, design=des)), rewrite=TRUE) summary(MIcombine(results)) options(oo) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey}% use one of RShowDoc("KEYWORDS")survey/man/anova.svyglm.Rd0000644000176200001440000001510114462330731015276 0ustar liggesusers\name{anova.svyglm} \alias{anova.svyglm} \alias{AIC.svyglm} \alias{BIC.svyglm} \alias{extractAIC.svyglm} \alias{extractAIC.svrepglm} \alias{anova.svycoxph} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Model comparison for glms. } \description{ A method for the \code{\link{anova}} function, for use on \code{\link{svyglm}} and \code{\link{svycoxph}} objects. With a single model argument it produces a sequential anova table, with two arguments it compares the two models. } \usage{ \method{anova}{svyglm}(object, object2 = NULL, test = c("F", "Chisq"), method = c("LRT", "Wald"), tolerance = 1e-05, ..., force = FALSE) \method{anova}{svycoxph}(object, object2=NULL,test=c("F","Chisq"), method=c("LRT","Wald"),tolerance=1e-5,...,force=FALSE) \method{AIC}{svyglm}(object,...,k=2, null_has_intercept=TRUE) \method{BIC}{svyglm}(object,...,maximal) \method{extractAIC}{svyglm}(fit,scale,k=2,..., null_has_intercept=TRUE) \method{extractAIC}{svrepglm}(fit,scale,k=2,..., null_has_intercept=TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object,fit}{ A \code{\link{svyglm}} or\code{\link{svycoxph}} object. } \item{object2}{ Optionally, another \code{\link{svyglm}} or \code{\link{svycoxph}} object. } \item{test}{ Use (linear combination of) F or chi-squared distributions for p-values. F is usually preferable. } \item{method}{ Use weighted deviance difference (LRT) or Wald tests to compare models } \item{tolerance}{ For models that are not symbolically nested, the tolerance for deciding that a term is common to the models. } \item{\dots}{ For \code{AIC} and \code{BIC}, optionally more \code{svyglm} objects } \item{scale}{not used} \item{null_has_intercept}{Does the null model for AIC have an intercept or not? Must be \code{FALSE} if any of the models are intercept-only. } \item{force}{ Force the tests to be done by explicit projection even if the models are symbolically nested (eg, for debugging) } \item{maximal}{A \code{svyglm} model that \code{object} (and \dots if supplied) are nested in.} \item{k}{Multiplier for effective df in AIC. Usually 2. There is no choice of \code{k} that will give BIC} } \details{ The reference distribution for the LRT depends on the misspecification effects for the parameters being tested (Rao and Scott, 1984). If the models are symbolically nested, so that the relevant parameters can be identified just by manipulating the model formulas, \code{anova} is equivalent to \code{\link{regTermTest}}.If the models are nested but not symbolically nested, more computation using the design matrices is needed to determine the projection matrix on to the parameters being tested. In the examples below, \code{model1} and \code{model2} are symbolically nested in \code{model0} because \code{model0} can be obtained just by deleting terms from the formulas. On the other hand, \code{model2} is nested in \code{model1} but not symbolically nested: knowing that the model is nested requires knowing what design matrix columns are produced by \code{stype} and \code{as.numeric(stype)}. Other typical examples of models that are nested but not symbolically nested are linear and spline models for a continuous covariate, or models with categorical versions of a variable at different resolutions (eg, smoking yes/no or smoking never/former/current). A saddlepoint approximation is used for the LRT with numerator df greater than 1. \code{AIC} is defined using the Rao-Scott approximation to the weighted loglikelihood (Lumley and Scott, 2015). It replaces the usual penalty term p, which is the null expectation of the log likelihood ratio, by the trace of the generalised design effect matrix, which is the expectation under complex sampling. For computational reasons everything is scaled so the weights sum to the sample size. \code{BIC} is a BIC for the (approximate) multivariate Gaussian models on regression coefficients from the maximal model implied by each submodel (ie, the models that say some coefficients in the maximal model are zero) (Lumley and Scott, 2015). It corresponds to comparing the models with a Wald test and replacing the sample size in the penalty by an effective sample size. For computational reasons, the models must not only be nested, the names of the coefficients must match. \code{extractAIC} for a model with a Gaussian link uses the actual AIC based on maximum likelihood estimation of the variance parameter as well as the regression parameters. } \value{ Object of class \code{seqanova.svyglm} if one model is given, otherwise of class \code{regTermTest} or \code{regTermTestLRT} } \note{ At the moment, \code{AIC} works only for models including an intercept. } \references{ Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. Lumley, T., & Scott, A. (2014). "Tests for Regression Models Fitted to Survey Data". Australian and New Zealand Journal of Statistics, 56 (1), 1-14. Lumley T, Scott AJ (2015) "AIC and BIC for modelling with complex survey data" J Surv Stat Methodol 3 (1): 1-18. } \seealso{ \code{\link{regTermTest}}, \code{\link{pchisqsum}} } \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) model0<-svyglm(I(sch.wide=="Yes")~ell+meals+mobility, design=dclus2, family=quasibinomial()) model1<-svyglm(I(sch.wide=="Yes")~ell+meals+mobility+as.numeric(stype), design=dclus2, family=quasibinomial()) model2<-svyglm(I(sch.wide=="Yes")~ell+meals+mobility+stype, design=dclus2, family=quasibinomial()) anova(model2) anova(model0,model2) anova(model1, model2) anova(model1, model2, method="Wald") AIC(model0,model1, model2) BIC(model0, model2,maximal=model2) ## AIC for linear model is different because it considers the variance ## parameter model0<-svyglm(api00~ell+meals+mobility, design=dclus2) model1<-svyglm(api00~ell+meals+mobility+as.numeric(stype), design=dclus2) model2<-svyglm(api00~ell+meals+mobility+stype, design=dclus2) modelnull<-svyglm(api00~1, design=dclus2) AIC(model0, model1, model2) AIC(model0, model1, model2,modelnull, null_has_intercept=FALSE) ## from ?twophase data(nwtco) dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco) a<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcchs) b<-update(a, .~.-I(age/12)+poly(age,3)) ## not symbolically nested models anova(a,b) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{regression}% __ONLY ONE__ keyword per line survey/man/rake.Rd0000755000176200001440000000773613517345016013620 0ustar liggesusers\name{rake} \alias{rake} %- Also NEED an '\alias' for EACH other topic documented here. \title{Raking of replicate weight design} \description{ Raking uses iterative post-stratification to match marginal distributions of a survey sample to known population margins. } \usage{ rake(design, sample.margins, population.margins, control = list(maxit = 10, epsilon = 1, verbose=FALSE), compress=NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{design}{A survey object } \item{sample.margins}{list of formulas or data frames describing sample margins, which must not contain missing values} \item{population.margins}{list of tables or data frames describing corresponding population margins } \item{control}{\code{maxit} controls the number of iterations. Convergence is declared if the maximum change in a table entry is less than \code{epsilon}. If \code{epsilon<1} it is taken to be a fraction of the total sampling weight. } \item{compress}{If \code{design} has replicate weights, attempt to compress the new replicate weight matrix? When \code{NULL}, will attempt to compress if the original weight matrix was compressed} } \details{ The \code{sample.margins} should be in a format suitable for \code{\link{postStratify}}. Raking (aka iterative proportional fitting) is known to converge for any table without zeros, and for any table with zeros for which there is a joint distribution with the given margins and the same pattern of zeros. The `margins' need not be one-dimensional. The algorithm works by repeated calls to \code{\link{postStratify}} (iterative proportional fitting), which is efficient for large multiway tables. For small tables \code{\link{calibrate}} will be faster, and also allows raking to population totals for continuous variables, and raking with bounded weights. } \value{ A raked survey design. } \seealso{ \code{\link{postStratify}}, \code{\link{compressWeights}} \code{\link{calibrate}} for other ways to use auxiliary information. } \examples{ data(api) dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1 <- as.svrepdesign(dclus1) svymean(~api00, rclus1) svytotal(~enroll, rclus1) ## population marginal totals for each stratum pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) rclus1r <- rake(rclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) svymean(~api00, rclus1r) svytotal(~enroll, rclus1r) ## marginal totals correspond to population xtabs(~stype, apipop) svytable(~stype, rclus1r, round=TRUE) xtabs(~sch.wide, apipop) svytable(~sch.wide, rclus1r, round=TRUE) ## joint totals don't correspond xtabs(~stype+sch.wide, apipop) svytable(~stype+sch.wide, rclus1r, round=TRUE) ## Do it for a design without replicate weights dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) svymean(~api00, dclus1r) svytotal(~enroll, dclus1r) ## compare to raking with calibrate() dclus1gr<-calibrate(dclus1, ~stype+sch.wide, pop=c(6194, 755,1018,5122), calfun="raking") svymean(~stype+api00, dclus1r) svymean(~stype+api00, dclus1gr) ## compare to joint post-stratification ## (only possible if joint population table is known) ## pop.table <- xtabs(~stype+sch.wide,apipop) rclus1ps <- postStratify(rclus1, ~stype+sch.wide, pop.table) svytable(~stype+sch.wide, rclus1ps, round=TRUE) svymean(~api00, rclus1ps) svytotal(~enroll, rclus1ps) ## Example of raking with partial joint distributions pop.imp<-data.frame(comp.imp=c("No","Yes"),Freq=c(1712,4482)) dclus1r2<-rake(dclus1, list(~stype+sch.wide, ~comp.imp), list(pop.table, pop.imp)) svymean(~api00, dclus1r2) ## compare to calibrate() syntax with tables dclus1r2<-calibrate(dclus1, formula=list(~stype+sch.wide, ~comp.imp), population=list(pop.table, pop.imp),calfun="raking") svymean(~api00, dclus1r2) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip} survey/man/postStratify.Rd0000755000176200001440000000751513517345016015404 0ustar liggesusers\name{postStratify} \alias{postStratify} \alias{postStratify.twophase} \alias{postStratify.svyrep.design} \alias{postStratify.survey.design} %- Also NEED an '\alias' for EACH other topic documented here. \title{Post-stratify a survey } \description{ Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Use \code{\link{rake}} when the full joint distribution is not available. } \usage{ postStratify(design, strata, population, partial = FALSE, ...) \method{postStratify}{svyrep.design}(design, strata, population, partial = FALSE, compress=NULL,...) \method{postStratify}{survey.design}(design, strata, population, partial = FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{design}{A survey design with replicate weights} \item{strata}{A formula or data frame of post-stratifying variables, which must not contain missing values. } \item{population}{A \code{\link{table}}, \code{\link{xtabs}} or \code{data.frame} with population frequencies } \item{partial}{if \code{TRUE}, ignore population strata not present in the sample} \item{compress}{Attempt to compress the replicate weight matrix? When \code{NULL} will attempt to compress if the original weight matrix was compressed} \item{...}{arguments for future expansion} } \details{ The \code{population} totals can be specified as a table with the strata variables in the margins, or as a data frame where one column lists frequencies and the other columns list the unique combinations of strata variables (the format produced by \code{as.data.frame} acting on a \code{table} object). A table must have named dimnames to indicate the variable names. Compressing the replicate weights will take time and may even increase memory use if there is actually little redundancy in the weight matrix (in particular if the post-stratification variables have many values and cut across PSUs). If a \code{svydesign} object is to be converted to a replication design the post-stratification should be performed after conversion. The variance estimate for replication designs follows the same procedure as Valliant (1993) described for estimating totals. Rao et al (2002) describe this procedure for estimating functions (and also the GREG or g-calibration procedure, see \code{\link{calibrate}}) } \note{ If the sampling weights are already post-stratified there will be no change in point estimates after \code{postStratify} but the standard error estimates will decrease to correctly reflect the post-stratification. } \value{ A new survey design object. } \references{ Valliant R (1993) Post-stratification and conditional variance estimation. JASA 88: 89-96 Rao JNK, Yung W, Hidiroglou MA (2002) Estimating equations for the analysis of survey data using poststratification information. Sankhya 64 Series A Part 2, 364-378. } \seealso{ \code{\link{rake}}, \code{\link{calibrate}} for other things to do with auxiliary information \code{\link{compressWeights}} for information on compressing weights} \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) svymean(~api00, rclus1) svytotal(~enroll, rclus1) # post-stratify on school type pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) #or: pop.types <- xtabs(~stype, data=apipop) #or: pop.types <- table(stype=apipop$stype) rclus1p<-postStratify(rclus1, ~stype, pop.types) summary(rclus1p) svymean(~api00, rclus1p) svytotal(~enroll, rclus1p) ## and for svydesign objects dclus1p<-postStratify(dclus1, ~stype, pop.types) summary(dclus1p) svymean(~api00, dclus1p) svytotal(~enroll, dclus1p) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip} survey/man/svyrecvar.Rd0000755000176200001440000001101614462643117014707 0ustar liggesusers\name{svyrecvar} \alias{svyrecvar} \alias{multistage} \alias{multistage_rcpp} %- Also NEED an '\alias' for EACH other topic documented here. \title{Variance estimation for multistage surveys} \description{ Compute the variance of a total under multistage sampling, using a recursive descent algorithm. } \usage{ svyrecvar(x, clusters, stratas,fpcs, postStrata = NULL, lonely.psu = getOption("survey.lonely.psu"), one.stage=getOption("survey.ultimate.cluster")) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{Matrix of data or estimating functions} \item{clusters}{Data frame or matrix with cluster ids for each stage} \item{stratas}{Strata for each stage } \item{fpcs}{Information on population and sample size for each stage, created by \code{\link{as.fpc}}} \item{postStrata}{post-stratification information as created by \code{\link{postStratify}} or \code{\link{calibrate}} } \item{lonely.psu}{How to handle strata with a single PSU} \item{one.stage}{If \code{TRUE}, compute a one-stage (ultimate-cluster) estimator} } \details{ The main use of this function is to compute the variance of the sum of a set of estimating functions under multistage sampling. The sampling is assumed to be simple or stratified random sampling within clusters at each stage except perhaps the last stage. The variance of a statistic is computed from the variance of estimating functions as described by Binder (1983). Use \code{one.stage=FALSE} for compatibility with other software that does not perform multi-stage calculations, and set \code{options(survey.ultimate.cluster=TRUE)} to make this the default. The idea of a recursive algorithm is due to Bellhouse (1985). Texts such as Cochran (1977) and Sarndal et al (1991) describe the decomposition of the variance into a single-stage between-cluster estimator and a within-cluster estimator, and this is applied recursively. If \code{one.stage} is a positive integer it specifies the number of stages of sampling to use in the recursive estimator. If \code{pps="brewer"}, standard errors are estimated using Brewer's approximation for PPS without replacement, option 2 of those described by Berger (2004). The \code{fpc} argument must then be specified in terms of sampling fractions, not population sizes (or omitted, but then the \code{pps} argument would have no effect and the with-replacement standard errors would be correct). } \value{ A covariance matrix } \references{ Bellhouse DR (1985) Computing Methods for Variance Estimation in Complex Surveys. Journal of Official Statistics. Vol.1, No.3, 1985 Berger, Y.G. (2004), A Simple Variance Estimator for Unequal Probability Sampling Without Replacement. Journal of Applied Statistics, 31, 305-315. Binder, David A. (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review, 51, 279-292. Brewer KRW (2002) Combined Survey Sampling Inference (Weighing Basu's Elephants) [Chapter 9] Cochran, W. (1977) Sampling Techniques. 3rd edition. Wiley. Sarndal C-E, Swensson B, Wretman J (1991) Model Assisted Survey Sampling. Springer. } \note{ A simple set of finite population corrections will only be exactly correct when each successive stage uses simple or stratified random sampling without replacement. A correction under general unequal probability sampling (eg PPS) would require joint inclusion probabilities (or, at least, sampling probabilities for units not included in the sample), information not generally available. The quality of Brewer's approximation is excellent in Berger's simulations, but the accuracy may vary depending on the sampling algorithm used. } \seealso{ \code{\link{svrVar}} for replicate weight designs \code{\link{svyCprod}} for a description of how variances are estimated at each stage } \examples{ data(mu284) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) svytotal(~y1, dmu284) data(api) # two-stage cluster sample dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) summary(dclus2) svymean(~api00, dclus2) svytotal(~enroll, dclus2,na.rm=TRUE) # bootstrap for multistage sample mrbclus2<-as.svrepdesign(dclus2, type="mrb", replicates=100) svytotal(~enroll, mrbclus2, na.rm=TRUE) # two-stage `with replacement' dclus2wr<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) summary(dclus2wr) svymean(~api00, dclus2wr) svytotal(~enroll, dclus2wr,na.rm=TRUE) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/svyfactanal.Rd0000644000176200001440000000502113517345016015167 0ustar liggesusers\name{svyfactanal} \Rdversion{1.1} \alias{svyfactanal} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Factor analysis in complex surveys (experimental). } \description{ This function fits a factor analysis model or SEM, by maximum weighted likelihood. } \usage{ svyfactanal(formula, design, factors, n = c("none", "sample", "degf","effective", "min.effective"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ Model formula specifying the variables to use } \item{design}{ Survey design object } \item{factors}{ Number of factors to estimate } \item{n}{ Sample size to be used for testing: see below} \item{\dots}{ Other arguments to pass to \code{\link{factanal}}. } } \details{ The population covariance matrix is estimated by \code{\link{svyvar}} and passed to \code{\link{factanal}} Although fitting these models requires only the estimated covariance matrix, inference requires a sample size. With \code{n="sample"}, the sample size is taken to be the number of observations; with \code{n="degf"}, the survey degrees of freedom as returned by \code{\link{degf}}. Using \code{"sample"} corresponds to standardizing weights to have mean 1, and is known to result in anti-conservative tests. The other two methods estimate an effective sample size for each variable as the sample size where the standard error of a variance of a Normal distribution would match the design-based standard error estimated by \code{\link{svyvar}}. With \code{n="min.effective"} the minimum sample size across the variables is used; with \code{n="effective"} the harmonic mean is used. For \code{svyfactanal} the test of model adequacy is optional, and the default choice, \code{n="none"}, does not do the test. } \value{ An object of class \code{factanal} } \references{ . } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{factanal}} The \code{lavaan.survey} package fits structural equation models to complex samples using similar techniques. } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svyfactanal(~api99+api00+hsg+meals+ell+emer, design=dclus1, factors=2) svyfactanal(~api99+api00+hsg+meals+ell+emer, design=dclus1, factors=2, n="effective") ##Population dat for comparison factanal(~api99+api00+hsg+meals+ell+emer, data=apipop, factors=2) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{multivariate}% __ONLY ONE__ keyword per line survey/man/svykappa.Rd0000644000176200001440000000217413641210710014506 0ustar liggesusers\name{svykappa} \alias{svykappa} %- Also NEED an '\alias' for EACH other topic documented here. \title{Cohen's kappa for agreement} \description{ Computes the unweighted kappa measure of agreement between two raters and the standard error. The measurements must both be factor variables in the survey design object. } \usage{ svykappa(formula, design, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{one-sided formula giving two measurements} \item{design}{survey design object} \item{\dots}{passed to \code{svymean} internally (such as \code{return.replicates} or \code{influence})} } \value{ Object of class \code{svystat} } \seealso{ \code{\link{svycontrast}}} \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svykappa(~comp.imp+sch.wide, dclus1) dclus1<-update(dclus1, stypecopy=stype) svykappa(~stype+stypecopy,dclus1) (kappas<-svyby(~comp.imp+sch.wide,~stype,design=dclus1, svykappa, covmat=TRUE)) svycontrast(kappas, quote(E/H)) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/ftable.svystat.Rd0000755000176200001440000000337213517345016015637 0ustar liggesusers\name{ftable.svystat} \alias{ftable.svystat} \alias{ftable.svrepstat} \alias{ftable.svyby} %- Also NEED an '\alias' for EACH other topic documented here. \title{Lay out tables of survey statistics} \description{ Reformat the output of survey computations to a table. } \usage{ \method{ftable}{svystat}(x, rownames,...) \method{ftable}{svrepstat}(x, rownames,...) \method{ftable}{svyby}(x,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{Output of functions such as \code{svymean},\code{svrepmean}, \code{svyby}} \item{rownames}{List of vectors of strings giving dimension names for the resulting table (see examples)} \item{...}{Arguments for future expansion} } \value{ An object of class \code{"ftable"} } \seealso{ \code{\link{ftable}}} \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) a<-svymean(~interaction(stype,comp.imp), design=dclus1) b<-ftable(a, rownames=list(stype=c("E","H","M"),comp.imp=c("No","Yes"))) b a<-svymean(~interaction(stype,comp.imp), design=dclus1, deff=TRUE) b<-ftable(a, rownames=list(stype=c("E","H","M"),comp.imp=c("No","Yes"))) round(100*b,1) rclus1<-as.svrepdesign(dclus1) a<-svytotal(~interaction(stype,comp.imp), design=rclus1) b<-ftable(a, rownames=list(stype=c("E","H","M"),comp.imp=c("No","Yes"))) b round(b) a<-svyby(~api99 + api00, ~stype + sch.wide, rclus1, svymean, keep.var=TRUE) ftable(a) print(ftable(a),digits=2) b<-svyby(~api99 + api00, ~stype + sch.wide, rclus1, svymean, keep.var=TRUE, deff=TRUE) print(ftable(b),digits=2) d<-svyby(~api99 + api00, ~stype + sch.wide, rclus1, svymean, keep.var=TRUE, vartype=c("se","cvpct")) round(ftable(d),1) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/svyttest.Rd0000644000176200001440000000243514066261522014566 0ustar liggesusers\name{svyttest} \alias{svyttest} \alias{confint.svyttest} %- Also NEED an '\alias' for EACH other topic documented here. \title{Design-based t-test} \description{ One-sample or two-sample t-test. This function is a wrapper for \code{\link{svymean}} in the one-sample case and for \code{\link{svyglm}} in the two-sample case. Degrees of freedom are \code{degf(design)-1} for the one-sample test and \code{degf(design)-2} for the two-sample case. } \usage{ svyttest(formula, design, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{Formula, \code{outcome~group} for two-sample, \code{outcome~0} or \code{outcome~1} for one-sample. The \code{group} variable must be a factor or character with two levels, or be coded 0/1 or 1/2} \item{design}{survey design object} \item{\dots}{for methods } } \value{ Object of class \code{htest} } \seealso{ \code{\link{t.test}}} \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) tt<-svyttest(enroll~comp.imp, dclus2) tt confint(tt, level=0.9) svyttest(enroll~I(stype=="E"),dclus2) svyttest(I(api00-api99)~0, dclus2) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{htest}% __ONLY ONE__ keyword per line survey/man/poisson_sampling.Rd0000644000176200001440000000230014565003231016227 0ustar liggesusers\name{poisson_sampling} \alias{poisson_sampling} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Specify Poisson sampling design } \description{ Specify a design where units are sampled independently from the population, with known probabilities. This design is often used theoretically, but is rarely used in practice because the sample size is variable. This function calls \code{\link{ppscov}} to specify a sparse sampling covariance matrix. } \usage{ poisson_sampling(p) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{p}{ Vector of sampling probabilities } } \value{ Object of class \code{ppsdcheck} } \seealso{ \code{\link{ppscov}}, \code{\link{svydesign}} } \examples{ data(api) apipop$prob<-with(apipop, 200*api00/sum(api00)) insample<-as.logical(rbinom(nrow(apipop),1,apipop$prob)) apipois<-apipop[insample,] despois<-svydesign(id=~1, prob=~prob, pps=poisson_sampling(apipois$prob), data=apipois) svytotal(~api00, despois) ## SE formula sqrt(sum( (apipois$api00*weights(despois))^2*(1-apipois$prob))) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory (show via RShowDoc("KEYWORDS")): % \keyword{survey } survey/man/svycralpha.Rd0000644000176200001440000000212113517345016015026 0ustar liggesusers\name{svycralpha} \alias{svycralpha} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Cronbach's alpha } \description{ Compute Cronbach's alpha coefficient of reliability from survey data. The formula is equation (2) of Cronbach (1951) only with design-based estimates of the variances. } \usage{ svycralpha(formula, design, na.rm = FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ One-sided formula giving the variables that make up the total score } \item{design}{ survey design object } \item{na.rm}{ \code{TRUE} to remove missing values } } \value{ A number } \references{ Cronbach LJ (1951). "Coefficient alpha and the internal structure of tests". Psychometrika. 16 (3): 297-334. doi:10.1007/bf02310555. } \examples{ data(api) dstrat<-svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) svycralpha(~ell+mobility+avg.ed+emer+meals, dstrat) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey}% use one of RShowDoc("KEYWORDS") survey/man/svyivreg.Rd0000644000176200001440000000341013517613332014531 0ustar liggesusers\name{svyivreg} \alias{svyivreg} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Two-stage least-squares for instrumental variable regression } \description{ Estimates regressions with endogenous covariates using two-stage least squares. The function uses \code{ivreg} from the \code{AER} package for the main computations, and follows the syntax of that function. } \usage{ svyivreg(formula, design, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{formula specification(s) of the regression relationship and the instruments. See Details for details} \item{design}{ A survey design object } \item{\dots}{ For future expansion } } \details{ Regressors and instruments for \code{svyivreg} are specified in a formula with two parts on the right-hand side, e.g., \code{y ~ x1 + x2 | z1 + z2 + z3}, where \code{x1} and \code{x2} are the regressors and \code{z1}, \code{z2}, and \code{z3} are the instruments. Note that exogenous regressors have to be included as instruments for themselves. For example, if there is one exogenous regressor \code{ex} and one endogenous regressor \code{en} with instrument \code{in}, the appropriate formula would be \code{y ~ ex + en | ex + in}. Equivalently, this can be specified as \code{y ~ ex + en | . - en + in}, i.e., by providing an update formula with a \code{.} in the second part of the formula. } \value{ An object of class \code{svyivreg} } \references{ \url{https://notstatschat.rbind.io/2019/07/16/adding-new-functions-to-the-survey-package/} } \seealso{ \code{\link[AER]{ivreg}}} % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey }% use one of RShowDoc("KEYWORDS") survey/man/paley.Rd0000644000176200001440000000462714572663035014007 0ustar liggesusers\name{paley} \alias{paley} \alias{is.hadamard} %- Also NEED an '\alias' for EACH other topic documented here. \title{Paley-type Hadamard matrices} \description{ Computes a Hadamard matrix of dimension \eqn{(p+1)\times 2^k}{(p+1)*2^k}, where p is a prime, and p+1 is a multiple of 4, using the Paley construction. Used by \code{\link{hadamard}}. } \usage{ paley(n, nmax = 2 * n, prime=NULL, check=!is.null(prime)) is.hadamard(H, style=c("0/1","+-"), full.orthogonal.balance=TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{n}{Minimum size for matrix} \item{nmax}{Maximum size for matrix. Ignored if \code{prime} is specified.} \item{prime}{Optional. A prime at least as large as \code{n}, such that \code{prime+1} is divisible by 4.} \item{check}{Check that the resulting matrix is of Hadamard type} \item{H}{Matrix} \item{style}{\code{"0/1"} for a matrix of 0s and 1s, \code{"+-"} for a matrix of \eqn{\pm 1}{+/-1}.} \item{full.orthogonal.balance}{Require full orthogonal balance?} } \value{ For \code{paley}, a matrix of zeros and ones, or \code{NULL} if no matrix smaller than \code{nmax} can be found. For \code{is.hadamard}, \code{TRUE} if \code{H} is a Hadamard matrix. } \details{ The Paley construction gives a Hadamard matrix of order p+1 if p is prime and p+1 is a multiple of 4. This is then expanded to order \eqn{(p+1)\times 2^k}{(p+1)*2^k} using the Sylvester construction. \code{paley} knows primes up to 7919. The user can specify a prime with the \code{prime} argument, in which case a matrix of order \eqn{p+1}{p+1} is constructed. If \code{check=TRUE} the code uses \code{is.hadamard} to check that the resulting matrix really is of Hadamard type, in the same way as in the example below. As this test takes \eqn{n^3}{n^3} time it is preferable to just be sure that \code{prime} really is prime. A Hadamard matrix including a row of 1s gives BRR designs where the average of the replicates for a linear statistic is exactly the full sample estimate. This property is called full orthogonal balance. } \references{ Cameron PJ (2005) Hadamard Matrices. In: The Encyclopedia of Design Theory } \seealso{ \code{\link{hadamard}}} \examples{ M<-paley(11) is.hadamard(M) ## internals of is.hadamard(M) H<-2*M-1 ## HH^T is diagonal for any Hadamard matrix H\%*\%t(H) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{algebra} survey/man/regTermTest.Rd0000755000176200001440000000752213517345016015134 0ustar liggesusers\name{regTermTest} \alias{regTermTest} \alias{print.regTermTest} %- Also NEED an '\alias' for EACH other topic documented here. \title{Wald test for a term in a regression model} \description{ Provides Wald test and working Wald and working likelihood ratio (Rao-Scott) test of the hypothesis that all coefficients associated with a particular regression term are zero (or have some other specified values). Particularly useful as a substitute for \code{\link{anova}} when not fitting by maximum likelihood. } \usage{ regTermTest(model, test.terms, null=NULL,df=NULL, method=c("Wald","WorkingWald","LRT"), lrt.approximation="saddlepoint") } %- maybe also 'usage' for other objects documented here. \arguments{ \item{model}{A model object with \code{\link{coef}} and \code{\link{vcov}} methods} \item{test.terms}{Character string or one-sided formula giving name of term or terms to test} \item{null}{Null hypothesis values for parameters. Default is zeros} \item{df}{Denominator degrees of freedom for an F test. If \code{NULL} these are estimated from the model. Use \code{Inf} for a chi-squared test.} \item{method}{If \code{"Wald"}, the Wald-type test; if \code{"LRT"} the Rao-Scott test based on the estimated log likelihood ratio; If \code{"WorkingWald"} the Wald-type test using the variance matrix under simple random sampling} \item{lrt.approximation}{method for approximating the distribution of the LRT and Working Wald statistic; see \code{\link{pchisqsum}}. } } \value{ An object of class \code{regTermTest} or \code{regTermTestLRT}. } \details{ The Wald test uses a chisquared or F distribution. The two working-model tests come from the (misspecified) working model where the observations are independent and the weights are frequency weights. For categorical data, this is just the model fitted to the estimated population crosstabulation. The Rao-Scott LRT statistic is the likelihood ratio statistic in this model. The working Wald test statistic is the Wald statistic in this model. The working-model tests do not have a chi-squared sampling distribution: we use a linear combination of chi-squared or F distributions as in \code{\link{pchisqsum}}. I believe the working Wald test is what SUDAAN refers to as a "Satterthwaite adjusted Wald test". To match other software you will typically need to use \code{lrt.approximation="satterthwaite"} } \references{ Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. Lumley T, Scott A (2012) "Partial likelihood ratio tests for the Cox model under complex sampling" Statistics in Medicine 17 JUL 2012. DOI: 10.1002/sim.5492 Lumley T, Scott A (2014) "Tests for Regression Models Fitted to Survey Data" Australian and New Zealand Journal of Statistics 56:1-14 DOI: 10.1111/anzs.12065 } \note{ The \code{"LRT"} method will not work if the model had starting values supplied for the regression coefficients. Instead, fit the two models separately and use \code{anova(model1, model2, force=TRUE)} } \seealso{\code{\link{anova}}, \code{\link{vcov}}, \code{\link{contrasts}},\code{\link{pchisqsum}}} \examples{ data(esoph) model1 <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp, data = esoph, family = binomial()) anova(model1) regTermTest(model1,"tobgp") regTermTest(model1,"tobgp:alcgp") regTermTest(model1, ~alcgp+tobgp:alcgp) data(api) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) model2<-svyglm(I(sch.wide=="Yes")~ell+meals+mobility, design=dclus2, family=quasibinomial()) regTermTest(model2, ~ell) regTermTest(model2, ~ell,df=NULL) regTermTest(model2, ~ell, method="LRT", df=Inf) regTermTest(model2, ~ell+meals, method="LRT", df=NULL) regTermTest(model2, ~ell+meals, method="WorkingWald", df=NULL) } \keyword{regression}% at least one, from doc/KEYWORDS survey/man/confint.svyglm.Rd0000644000176200001440000000345513753316711015647 0ustar liggesusers\name{confint.svyglm} \alias{confint.svyglm} %- Also NEED an '\alias' for EACH other topic documented here. \title{Confidence intervals for regression parameters } \description{ Computes confidence intervals for regression parameters in \code{\link{svyglm}} objects. The default is a Wald-type confidence interval, adding and subtracting a multiple of the standard error. The \code{method="likelihood"} is an interval based on inverting the Rao-Scott likelihood ratio test. That is, it is an interval where the working model deviance is lower than the threshold for the Rao-Scott test at the specified level. } \usage{ \method{confint}{svyglm}(object, parm, level = 0.95, method = c("Wald", "likelihood"), ddf = NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{\code{svyglm} object} \item{parm}{numeric or character vector indicating which parameters to construct intervals for.} \item{level}{desired coverage} \item{method}{See description above } \item{ddf}{Denominator degrees of freedom for \code{"likelihood"} method, to use a t distribution rather than norma. If \code{NULL}, use \code{object$df.residual}} \item{\dots}{for future expansion} } \value{ A matrix of confidence intervals } \references{ J. N. K. Rao and Alistair J. Scott (1984) On Chi-squared Tests For Multiway Contigency Tables with Proportions Estimated From Survey Data. Annals of Statistics 12:46-60 } \seealso{\code{\link{confint}} } \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) m<-svyglm(I(comp.imp=="Yes")~stype*emer+ell, design=dclus2, family=quasibinomial) confint(m) confint(m, method="like",ddf=NULL, parm=c("ell","emer")) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/hadamard.Rd0000755000176200001440000000425413517345016014427 0ustar liggesusers\name{hadamard} \alias{hadamard} %- Also NEED an '\alias' for EACH other topic documented here. \title{Hadamard matrices } \description{ Returns a Hadamard matrix of dimension larger than the argument. } \usage{ hadamard(n) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{n}{lower bound for size } } \value{ A Hadamard matrix } \details{ For most \code{n} the matrix comes from \code{\link{paley}}. The \eqn{36\times 36}{36x36} matrix is from Plackett and Burman (1946) and the \eqn{28\times 28}{28x28} is from Sloane's library of Hadamard matrices. Matrices of dimension every multiple of 4 are thought to exist, but this function doesn't know about all of them, so it will sometimes return matrices that are larger than necessary. The excess is at most 4 for \eqn{n<180}{n<180} and at most 5\% for \eqn{n>100}{n>100}. } \note{Strictly speaking, a Hadamard matrix has entries +1 and -1 rather than 1 and 0, so \code{2*hadamard(n)-1} is a Hadamard matrix} \references{ Sloane NJA. A Library of Hadamard Matrices \url{http://neilsloane.com/hadamard/} Plackett RL, Burman JP. (1946) The Design of Optimum Multifactorial Experiments Biometrika, Vol. 33, No. 4 pp. 305-325 Cameron PJ (2005) Hadamard Matrices \url{http://designtheory.org/library/encyc/topics/had.pdf}. In: The Encyclopedia of Design Theory \url{http://designtheory.org/library/encyc/} } \seealso{\code{\link{brrweights}}, \code{\link{paley}}} \examples{ par(mfrow=c(2,2)) ## Sylvester-type image(hadamard(63),main=quote("Sylvester: "*64==2^6)) ## Paley-type image(hadamard(59),main=quote("Paley: "*60==59+1)) ## from NJ Sloane's library image(hadamard(27),main=quote("Stored: "*28)) ## For n=90 we get 96 rather than the minimum possible size, 92. image(hadamard(90),main=quote("Constructed: "*96==2^3\%*\%(11+1))) par(mfrow=c(1,1)) plot(2:150,sapply(2:150,function(i) ncol(hadamard(i))),type="S", ylab="Matrix size",xlab="n",xlim=c(1,150),ylim=c(1,150)) abline(0,1,lty=3) lines(2:150, 2:150-(2:150 \%\% 4)+4,col="purple",type="S",lty=2) legend(c(x=10,y=140),legend=c("Actual size","Minimum possible size"), col=c("black","purple"),bty="n",lty=c(1,2)) } \keyword{survey} survey/man/svyCprod.Rd0000755000176200001440000001151613517345016014476 0ustar liggesusers\name{svyCprod} \alias{svyCprod} \alias{onestage} \alias{onestrat} %- Also NEED an `\alias' for EACH other topic documented here. \title{Computations for survey variances} \description{ Computes the sum of products needed for the variance of survey sample estimators. \code{svyCprod} is used for survey design objects from before version 2.9, \code{onestage} is called by \code{\link{svyrecvar}} for post-2.9 design objects. } \usage{ svyCprod(x, strata, psu, fpc, nPSU,certainty=NULL, postStrata=NULL, lonely.psu=getOption("survey.lonely.psu")) onestage(x, strata, clusters, nPSU, fpc, lonely.psu=getOption("survey.lonely.psu"),stage=0,cal) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{A vector or matrix} \item{strata}{A vector of stratum indicators (may be \code{NULL} for \code{svyCprod})} \item{psu}{A vector of cluster indicators (may be \code{NULL})} \item{clusters}{A vector of cluster indicators } \item{fpc}{A data frame (\code{svyCprod}) or vector (\code{onestage}) of population stratum sizes, or \code{NULL}} \item{nPSU}{Table (\code{svyprod}) or vector (\code{onestage}) of original sample stratum sizes (or \code{NULL})} \item{certainty}{logical vector with stratum names as names. If \code{TRUE} and that stratum has a single PSU it is a certainty PSU} \item{postStrata}{Post-stratification variables} \item{lonely.psu}{One of \code{"remove"}, \code{"adjust"}, \code{"fail"}, \code{"certainty"}, \code{"average"}. See Details below} \item{stage}{Used internally to track the depth of recursion} \item{cal}{Used to pass calibration information at stages below the population} } \details{ The observations for each cluster are added, then centered within each stratum and the outer product is taken of the row vector resulting for each cluster. This is added within strata, multiplied by a degrees-of-freedom correction and by a finite population correction (if supplied) and added across strata. If there are fewer clusters (PSUs) in a stratum than in the original design extra rows of zeroes are added to \code{x} to allow the correct subpopulation variance to be computed. See \code{\link{postStratify}} for information about post-stratification adjustments. The variance formula gives 0/0 if a stratum contains only one sampling unit. If the \code{certainty} argument specifies that this is a PSU sampled with probability 1 (a "certainty" PSU) then it does not contribute to the variance (this is correct only when there is no subsampling within the PSU -- otherwise it should be defined as a pseudo-stratum). If \code{certainty} is \code{FALSE} for this stratum or is not supplied the result depends on \code{lonely.psu}. The options are \code{"fail"} to give an error, \code{"remove"} or \code{"certainty"} to give a variance contribution of 0 for the stratum, \code{"adjust"} to center the stratum at the grand mean rather than the stratum mean, and \code{"average"} to assign strata with one PSU the average variance contribution from strata with more than one PSU. The choice is controlled by setting \code{options(survey.lonely.psu)}. If this is not done the factory default is \code{"fail"}. Using \code{"adjust"} is conservative, and it would often be better to combine strata in some intelligent way. The properties of \code{"average"} have not been investigated thoroughly, but it may be useful when the lonely PSUs are due to a few strata having PSUs missing completely at random. The \code{"remove"}and \code{"certainty"} options give the same result, but \code{"certainty"} is intended for situations where there is only one PSU in the population stratum, which is sampled with certainty (also called `self-representing' PSUs or strata). With \code{"certainty"} no warning is generated for strata with only one PSU. Ordinarily, \code{svydesign} will detect certainty PSUs, making this option unnecessary. For strata with a single PSU in a subset (domain) the variance formula gives a value that is well-defined and positive, but not typically correct. If \code{options("survey.adjust.domain.lonely")} is \code{TRUE} and \code{options("survey.lonely.psu")} is \code{"adjust"} or \code{"average"}, and no post-stratification or G-calibration has been done, strata with a single PSU in a subset will be treated like those with a single PSU in the sample. I am not aware of any theoretical study of this procedure, but it should at least be conservative. } \value{ A covariance matrix } \author{Thomas Lumley} \references{Binder, David A. (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review, 51, 279- 292. } \seealso{\code{\link{svydesign}}, \code{\link{svyrecvar}}, \code{\link{surveyoptions}}, \code{\link{postStratify}} } \keyword{utilities}% at least one, from doc/KEYWORDS \keyword{survey}% __ONLY ONE__ keyword per line survey/man/svyhist.Rd0000644000176200001440000000457413517345016014401 0ustar liggesusers\name{svyhist} \alias{svyhist} \alias{svyboxplot} %- Also NEED an '\alias' for EACH other topic documented here. \title{Histograms and boxplots} \description{ Histograms and boxplots weighted by the sampling weights. } \usage{ svyhist(formula, design, breaks = "Sturges", include.lowest = TRUE, right = TRUE, xlab = NULL, main = NULL, probability = TRUE, freq = !probability, ...) svyboxplot(formula, design, all.outliers=FALSE,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{One-sided formula for \code{svyhist}, two-sided for \code{svyboxplot}} \item{design}{A survey design object} \item{xlab}{x-axis label} \item{main}{Main title} \item{probability,freq}{Y-axis is probability density or frequency} \item{all.outliers}{Show all outliers in the boxplot, not just extremes} \item{breaks, include.lowest, right}{As for \code{\link{hist}}} \item{\dots}{Other arguments to \code{\link{hist}} or \code{\link{bxp}}} } \details{ The histogram breakpoints are computed as if the sample were a simple random sample of the same size. The grouping variable in \code{svyboxplot}, if present, must be a factor. The boxplot whiskers go to the maximum and minimum observations or to 1.5 interquartile ranges beyond the end of the box, whichever is closer. The maximum and minimum are plotted as outliers if they are beyond the ends of the whiskers, but other outlying points are not plotted unless \code{all.outliers=TRUE}. \code{svyboxplot} requires a two-sided formula; use \code{variable~1} for a single boxplot. } \value{ As for \code{hist}, except that when \code{probability=FALSE}, the return value includes a component \code{count_scale} giving a scale factor between density and counts, assuming equal bin widths. } \seealso{ \code{\link{svyplot}}} \examples{ data(api) dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) opar<-par(mfrow=c(1,3)) svyhist(~enroll, dstrat, main="Survey weighted",col="purple",ylim=c(0,1.3e-3)) hist(apistrat$enroll, main="Sample unweighted",col="purple",prob=TRUE,ylim=c(0,1.3e-3)) hist(apipop$enroll, main="Population",col="purple",prob=TRUE,ylim=c(0,1.3e-3)) par(mfrow=c(1,1)) svyboxplot(enroll~stype,dstrat,all.outliers=TRUE) svyboxplot(enroll~1,dstrat) par(opar) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{hplot}% __ONLY ONE__ keyword per line survey/man/nonresponse.Rd0000755000176200001440000000627313517345016015242 0ustar liggesusers\name{nonresponse} \alias{nonresponse} \alias{sparseCells} \alias{neighbours} \alias{joinCells} \alias{weights.nonresponse} \alias{print.nonresponse} \alias{print.nonresponseSubset} \alias{[.nonresponse} %- Also NEED an '\alias' for EACH other topic documented here. \title{Experimental: Construct non-response weights} \description{ Functions to simplify the construction of non-reponse weights by combining strata with small numbers or large weights. } \usage{ nonresponse(sample.weights, sample.counts, population) sparseCells(object, count=0,totalweight=Inf, nrweight=1.5) neighbours(index,object) joinCells(object,a,...) \method{weights}{nonresponse}(object,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{sample.weights}{table of sampling weight by stratifying variables} \item{sample.counts}{table of sample counts by stratifying variables} \item{population}{table of population size by stratifying variables} \item{object}{object of class \code{"nonresponse"}} \item{count}{Cells with fewer sampled units than this are "sparse"} \item{nrweight}{Cells with higher non-response weight than this are "sparse"} \item{totalweight}{Cells with average sampling weight times non-response weight higher than this are "sparse"} \item{index}{Number of a cell whose neighbours are to be found} \item{a,...}{Cells to join} } \details{ When a stratified survey is conducted with imperfect response it is desirable to rescale the sampling weights to reflect the nonresponse. If some strata have small sample size, high non-response, or already had high sampling weights it may be desirable to get less variable non-response weights by averaging non-response across strata. Suitable strata to collapse may be similar on the stratifying variables and/or on the level of non-response. \code{nonresponse()} combines stratified tables of population size, sample size, and sample weight into an object. \code{sparseCells} identifies cells that may need combining. \code{neighbours} describes the cells adjacent to a specified cell, and \code{joinCells} collapses the specified cells. When the collapsing is complete, use \code{weights()} to extract the nonresponse weights. } \value{ \code{nonresponse} and \code{joinCells} return objects of class \code{"nonresponse"}, \code{neighbours} and \code{sparseCells} return objects of class \code{"nonresponseSubset"} } \examples{ data(api) ## pretend the sampling was stratified on three variables poptable<-xtabs(~sch.wide+comp.imp+stype,data=apipop) sample.count<-xtabs(~sch.wide+comp.imp+stype,data=apiclus1) sample.weight<-xtabs(pw~sch.wide+comp.imp+stype, data=apiclus1) ## create a nonresponse object nr<-nonresponse(sample.weight,sample.count, poptable) ## sparse cells sparseCells(nr) ## Look at neighbours neighbours(3,nr) neighbours(11,nr) ## Collapse some contiguous cells nr1<-joinCells(nr,3,5,7) ## sparse cells now sparseCells(nr1) nr2<-joinCells(nr1,3,11,8) nr2 ## one relatively sparse cell sparseCells(nr2) ## but nothing suitable to join it to neighbours(3,nr2) ## extract the weights weights(nr2) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/svyplot.Rd0000755000176200001440000001026213517345016014402 0ustar liggesusers\name{svyplot} \alias{svyplot} \alias{svyplot.default} %- Also NEED an '\alias' for EACH other topic documented here. \title{Plots for survey data } \description{ Because observations in survey samples may represent very different numbers of units in the population ordinary plots can be misleading. The \code{svyplot} function produces scatterplots adjusted in various ways for sampling weights. } \usage{ svyplot(formula, design,...) \method{svyplot}{default}(formula, design, style = c("bubble", "hex", "grayhex","subsample","transparent"), sample.size = 500, subset = NULL, legend = 1, inches = 0.05, amount=NULL, basecol="black", alpha=c(0, 0.8),xbins=30,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{A model formula} \item{design}{ A survey object (svydesign or svrepdesign)} \item{style}{See Details below} \item{sample.size}{For \code{style="subsample"}} \item{subset}{expression using variables in the design object} \item{legend}{For \code{style="hex"} or \code{"grayhex"}} \item{inches}{Scale for bubble plots} \item{amount}{list with \code{x} and \code{y} components for amount of jittering to use in subsample plots, or \code{NULL} for the default amount} \item{basecol}{base color for transparent plots, or a function to compute the color (see below), or color for bubble plots} \item{alpha}{minimum and maximum opacity for transparent plots} \item{xbins}{Number of (x-axis) bins for hexagonal binning} \item{\dots}{Passed to \code{plot} methods} } \details{ Bubble plots are scatterplots with circles whose area is proportional to the sampling weight. The two "hex" styles produce hexagonal binning scatterplots, and require the \code{hexbin} package from Bioconductor. The "transparent" style plots points with opacity proportional to sampling weight. The \code{subsample} method uses the sampling weights to create a sample from approximately the population distribution and passes this to \code{\link{plot}} Bubble plots are suited to small surveys, hexagonal binning and transparency to large surveys where plotting all the points would result in too much overlap. \code{basecol} can be a function taking one data frame argument, which will be passed the data frame of variables from the survey object. This could be memory-intensive for large data sets. } \value{ None } \seealso{ \code{\link{symbols}} for other options (such as colour) for bubble plots. \code{\link{svytable}} for plots of discrete data. } \references{ Korn EL, Graubard BI (1998) "Scatterplots with Survey Data" The American Statistician 52: 58-69 Lumley T, Scott A (2017) "Fitting Regression Models to Survey Data" Statistical Science 32: 265-278 } \examples{ data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) svyplot(api00~api99, design=dstrat, style="bubble") svyplot(api00~api99, design=dstrat, style="transparent",pch=19) ## these two require the hexbin package svyplot(api00~api99, design=dstrat, style="hex", xlab="1999 API",ylab="2000 API") svyplot(api00~api99, design=dstrat, style="grayhex",legend=0) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2, fpc=~fpc1+fpc2) svyplot(api00~api99, design=dclus2, style="subsample") svyplot(api00~api99, design=dclus2, style="subsample", amount=list(x=25,y=25)) svyplot(api00~api99, design=dstrat, basecol=function(df){c("goldenrod","tomato","sienna")[as.numeric(df$stype)]}, style="transparent",pch=19,alpha=c(0,1)) legend("topleft",col=c("goldenrod","tomato","sienna"), pch=19, legend=c("E","H","M")) ## For discrete data, estimate a population table and plot the table. plot(svytable(~sch.wide+comp.imp+stype,design=dstrat)) fourfoldplot(svytable(~sch.wide+comp.imp+stype,design=dstrat,round=TRUE)) ## To draw on a hexbin plot you need grid graphics, eg, library(grid) h<-svyplot(api00~api99, design=dstrat, style="hex", xlab="1999 API",ylab="2000 API") s<-svysmooth(api00~api99,design=dstrat) grid.polyline(s$api99$x,s$api99$y,vp=h$plot.vp@hexVp.on,default.units="native", gp=gpar(col="red",lwd=2)) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{hplot}% __ONLY ONE__ keyword per line survey/man/svydesign.Rd0000755000176200001440000002306114423650257014701 0ustar liggesusers\name{svydesign} \alias{svydesign} \alias{svydesign.default} \alias{svydesign.imputationList} \alias{svydesign.character} \alias{na.omit.survey.design} \alias{na.exclude.survey.design} \alias{na.fail.survey.design} \title{Survey sample analysis.} \description{ Specify a complex survey design. } \usage{ svydesign(ids, probs=NULL, strata = NULL, variables = NULL, fpc=NULL, data = NULL, nest = FALSE, check.strata = !nest, weights=NULL,pps=FALSE,...) \method{svydesign}{default}(ids, probs=NULL, strata = NULL, variables = NULL, fpc=NULL,data = NULL, nest = FALSE, check.strata = !nest, weights=NULL, pps=FALSE,calibrate.formula=NULL,variance=c("HT","YG"),...) \method{svydesign}{imputationList}(ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE, calibrate.formula=NULL,...) \method{svydesign}{character}(ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE, calibrate.formula=NULL,dbtype = "SQLite", dbname, ...) } %- maybe also `usage' for other objects documented here.'` \arguments{ \item{ids}{Formula or data frame specifying cluster ids from largest level to smallest level, \code{~0} or \code{~1} is a formula for no clusters.} \item{probs}{Formula or data frame specifying cluster sampling probabilities} \item{strata}{Formula or vector specifying strata, use \code{NULL} for no strata} \item{variables}{Formula or data frame specifying the variables measured in the survey. If \code{NULL}, the \code{data} argument is used.} \item{fpc}{Finite population correction: see Details below} \item{weights}{Formula or vector specifying sampling weights as an alternative to \code{prob}} \item{data}{Data frame to look up variables in the formula arguments, or database table name, or \code{imputationList} object, see below} \item{nest}{If \code{TRUE}, relabel cluster ids to enforce nesting within strata} \item{check.strata}{If \code{TRUE}, check that clusters are nested in strata}. \item{pps}{\code{"brewer"} to use Brewer's approximation for PPS sampling without replacement. \code{"overton"} to use Overton's approximation. An object of class \code{\link{HR}} to use the Hartley-Rao approximation. An object of class \code{\link{ppsmat}} to use the Horvitz-Thompson estimator.} \item{calibrate.formula}{model formula specifying how the weights are *already* calibrated (raked, post-stratified).} \item{dbtype}{name of database driver to pass to \code{dbDriver}} \item{dbname}{name of database (eg file name for SQLite)} \item{variance}{For \code{pps} without replacement, use \code{variance="YG"} for the Yates-Grundy estimator instead of the Horvitz-Thompson estimator} \item{\dots}{for future expansion} } \details{ The \code{svydesign} object combines a data frame and all the survey design information needed to analyse it. These objects are used by the survey modelling and summary functions. The \code{id} argument is always required, the \code{strata}, \code{fpc}, \code{weights} and \code{probs} arguments are optional. If these variables are specified they must not have any missing values. By default, \code{svydesign} assumes that all PSUs, even those in different strata, have a unique value of the \code{id} variable. This allows some data errors to be detected. If your PSUs reuse the same identifiers across strata then set \code{nest=TRUE}. The finite population correction (fpc) is used to reduce the variance when a substantial fraction of the total population of interest has been sampled. It may not be appropriate if the target of inference is the process generating the data rather than the statistics of a particular finite population. The finite population correction can be specified either as the total population size in each stratum or as the fraction of the total population that has been sampled. In either case the relevant population size is the sampling units. That is, sampling 100 units from a population stratum of size 500 can be specified as 500 or as 100/500=0.2. The exception is for PPS sampling without replacement, where the sampling probability (which will be different for each PSU) must be used. If population sizes are specified but not sampling probabilities or weights, the sampling probabilities will be computed from the population sizes assuming simple random sampling within strata. For multistage sampling the \code{id} argument should specify a formula with the cluster identifiers at each stage. If subsequent stages are stratified \code{strata} should also be specified as a formula with stratum identifiers at each stage. The population size for each level of sampling should also be specified in \code{fpc}. If \code{fpc} is not specified then sampling is assumed to be with replacement at the top level and only the first stage of cluster is used in computing variances. If \code{fpc} is specified but for fewer stages than \code{id}, sampling is assumed to be complete for subsequent stages. The variance calculations for multistage sampling assume simple or stratified random sampling within clusters at each stage except possibly the last. For PPS sampling without replacement it is necessary to specify the probabilities for each stage of sampling using the \code{fpc} arguments, and an overall \code{weight} argument should not be given. At the moment, multistage or stratified PPS sampling without replacement is supported only with \code{pps="brewer"}, or by giving the full joint probability matrix using \code{\link{ppsmat}}. [Cluster sampling is supported by all methods, but not subsampling within clusters]. The \code{dim}, \code{"["}, \code{"[<-"} and na.action methods for \code{survey.design} objects operate on the dataframe specified by \code{variables} and ensure that the design information is properly updated to correspond to the new data frame. With the \code{"[<-"} method the new value can be a \code{survey.design} object instead of a data frame, but only the data frame is used. See also \code{\link{subset.survey.design}} for a simple way to select subpopulations. The \code{model.frame} method extracts the observed data. If the strata with only one PSU are not self-representing (or they are, but \code{svydesign} cannot tell based on \code{fpc}) then the handling of these strata for variance computation is determined by \code{options("survey.lonely.psu")}. See \code{\link{svyCprod}} for details. \code{data} may be a character string giving the name of a table or view in a relational database that can be accessed through the \code{DBI} interfaces. For DBI interfaces \code{dbtype} should be the name of the database driver and \code{dbname} should be the name by which the driver identifies the specific database (eg file name for SQLite). The appropriate database interface package must already be loaded (eg \code{RSQLite} for SQLite). The survey design object will contain only the design meta-data, and actual variables will be loaded from the database as needed. Use \code{\link[=close.DBIsvydesign]{close}} to close the database connection and \code{\link[=open.DBIsvydesign]{open}} to reopen the connection, eg, after loading a saved object. The database interface does not attempt to modify the underlying database and so can be used with read-only permissions on the database. If \code{data} is an \code{imputationList} object (from the "mitools" package), \code{svydesign} will return a \code{svyimputationList} object containing a set of designs. Use \code{\link{with.svyimputationList}} to do analyses on these designs and \code{MIcombine} to combine the results. } \value{ An object of class \code{survey.design}. } \author{Thomas Lumley} \seealso{ \code{\link{as.svrepdesign}} for converting to replicate weight designs, \code{\link{subset.survey.design}} for domain estimates, \code{\link{update.survey.design}} to add variables. \code{mitools} package for using multiple imputations \code{\link{svyrecvar}} for details of variance estimation \code{\link{election}} for examples of PPS sampling without replacement. } \examples{ data(api) # stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) # one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) # two-stage cluster sample: weights computed from population sizes. dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) ## multistage sampling has no effect when fpc is not given, so ## these are equivalent. dclus2wr<-svydesign(id=~dnum+snum, weights=weights(dclus2), data=apiclus2) dclus2wr2<-svydesign(id=~dnum, weights=weights(dclus2), data=apiclus2) ## syntax for stratified cluster sample ##(though the data weren't really sampled this way) svydesign(id=~dnum, strata=~stype, weights=~pw, data=apistrat, nest=TRUE) ## PPS sampling without replacement data(election) dpps<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") ##database example: requires RSQLite \dontrun{ library(RSQLite) dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey")) } ## pre-calibrated weights cdclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc, calibration.formula=~1) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{univar}% __ONLY ONE__ keyword per line \keyword{manip}% __ONLY ONE__ keyword per line survey/man/brrweights.Rd0000755000176200001440000001211013517345016015034 0ustar liggesusers\name{brrweights} \alias{jk1weights} \alias{jknweights} \alias{brrweights} %- Also NEED an `\alias' for EACH other topic documented here. \title{Compute replicate weights } \description{ Compute replicate weights from a survey design. These functions are usually called from \code{\link{as.svrepdesign}} rather than directly by the user. } \usage{ brrweights(strata, psu, match = NULL, small = c("fail","split","merge"), large = c("split", "merge", "fail"), fay.rho=0, only.weights=FALSE, compress=TRUE, hadamard.matrix=NULL) jk1weights(psu,fpc=NULL, fpctype=c("population","fraction","correction"), compress=TRUE) jknweights(strata,psu, fpc=NULL, fpctype=c("population","fraction","correction"), compress=TRUE, lonely.psu=getOption("survey.lonely.psu")) } %- maybe also `usage' for other objects documented here. \arguments{ \item{strata}{Stratum identifiers } \item{psu}{PSU (cluster) identifier } \item{match}{Optional variable to use in matching. } \item{small}{How to handle strata with only one PSU} \item{large}{How to handle strata with more than two PSUs} \item{fpc}{Optional population (stratum) size or finite population correction } \item{fpctype}{How \code{fpc} is coded.} \item{fay.rho}{Parameter for Fay's extended BRR method} \item{only.weights}{If \code{TRUE} return only the matrix of replicate weights} \item{compress}{If \code{TRUE}, store the replicate weights in compressed form} \item{hadamard.matrix}{Optional user-supplied Hadamard matrix for \code{brrweights}} \item{lonely.psu}{Handling of non-certainty single-PSU strata} } \details{ JK1 and JKn are jackknife schemes for unstratified and stratified designs respectively. The finite population correction may be specified as a single number, a vector with one entry per stratum, or a vector with one entry per observation (constant within strata). When \code{fpc} is a vector with one entry per stratum it may not have names that differ from the stratum identifiers (it may have no names, in which case it must be in the same order as \code{unique(strata)}). To specify population stratum sizes use \code{fpctype="population"}, to specify sampling fractions use \code{fpctype="fraction"} and to specify the correction directly use \code{fpctype="correction"} The only reason not to use \code{compress=TRUE} is that it is new and there is a greater possibility of bugs. It reduces the number of rows of the replicate weights matrix from the number of observations to the number of PSUs. In BRR variance estimation each stratum is split in two to give half-samples. Balanced replicated weights are needed, where observations in two different strata end up in the same half stratum as often as in different half-strata.BRR, strictly speaking, is defined only when each stratum has exactly two PSUs. A stratum with one PSU can be merged with another such stratum, or can be split to appear in both half samples with half weight. The latter approach is appropriate for a PSU that was deterministically sampled. A stratum with more than two PSUs can be split into multiple smaller strata each with two PSUs or the PSUs can be merged to give two superclusters within the stratum. When merging small strata or grouping PSUs in large strata the \code{match} variable is used to sort PSUs before merging, to give approximate matching on this variable. If you want more control than this you should probably construct your own weights using the Hadamard matrices produced by \code{\link{hadamard}} } \value{ For \code{brrweights} with \code{only.weights=FALSE} a list with elements \item{weights}{two-column matrix indicating the weight for each half-stratum in one particular set of split samples} \item{wstrata}{New stratum variable incorporating merged or split strata} \item{strata}{Original strata for distinct PSUs} \item{psu}{Distinct PSUs} \item{npairs}{Dimension of Hadamard matrix used in BRR construction} \item{sampler}{function returning replicate weights} \item{compress}{Indicates whether the \code{sampler} returns per PSU or per observation weights} For \code{jk1weights} and \code{jknweights} a data frame of replicate weights and the \code{scale} and \code{rscale} arguments to \code{\link{svrVar}}. } \references{Levy and Lemeshow "Sampling of Populations". Wiley. Shao and Tu "The Jackknife and Bootstrap". Springer. } \seealso{\code{\link{hadamard}}, \code{\link{as.svrepdesign}}, \code{\link{svrVar}}, \code{\link{surveyoptions}}} \examples{ data(scd) scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE) ## convert to BRR replicate weights scd2brr <- as.svrepdesign(scdnofpc, type="BRR") svymean(~alive, scd2brr) svyratio(~alive, ~arrests, scd2brr) ## with user-supplied hadamard matrix scd2brr1 <- as.svrepdesign(scdnofpc, type="BRR", hadamard.matrix=paley(11)) svymean(~alive, scd2brr1) svyratio(~alive, ~arrests, scd2brr1) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/svystandardize.Rd0000644000176200001440000000554514074414734015744 0ustar liggesusers\name{svystandardize} \alias{svystandardize} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Direct standardization within domains } \description{ In health surveys it is often of interest to standardize domains to have the same distribution of, eg, age as in a target population. The operation is similar to post-stratification, except that the totals for the domains are fixed at the current estimates, not at known population values. This function matches the estimates produced by the (US) National Center for Health Statistics. } \usage{ svystandardize(design, by, over, population, excluding.missing = NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{design}{ survey design object } \item{by}{ A one-sided formula specifying the variables whose distribution will be standardised } \item{over}{ A one-sided formula specifying the domains within which the standardisation will occur, or \code{~1} to use the whole population. } \item{population}{ Desired population totals or proportions for the levels of combinations of variables in \code{by} } \item{excluding.missing}{ Optionally, a one-sided formula specifying variables whose missing values should be dropped before calculating the domain totals. } } \value{ A new survey design object of the same type as the input. } \references{ National Center for Health Statistics \verb{https://www.cdc.gov/nchs/tutorials/NHANES/NHANESAnalyses/agestandardization/age_standardization_intro.htm}} \note{ The standard error estimates do not exactly match the NCHS estimates } \seealso{ \code{\link{postStratify}}, \code{\link{svyby}} } \examples{ ## matches http://www.cdc.gov/nchs/data/databriefs/db92_fig1.png data(nhanes) popage <- c( 55901 , 77670 , 72816 , 45364 ) design<-svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, data=nhanes, nest=TRUE) stdes<-svystandardize(design, by=~agecat, over=~race+RIAGENDR, population=popage, excluding.missing=~HI_CHOL) svyby(~HI_CHOL, ~race+RIAGENDR, svymean, design=subset(stdes, agecat!="(0,19]")) data(nhanes) nhanes_design <- svydesign(ids = ~ SDMVPSU, strata = ~ SDMVSTRA, weights = ~ WTMEC2YR, nest = TRUE, data = nhanes) ## These are the same nhanes_adj <- svystandardize(update(nhanes_design, all_adults = "1"), by = ~ agecat, over = ~ all_adults, population = c(55901, 77670, 72816, 45364), excluding.missing = ~ HI_CHOL) svymean(~I(HI_CHOL == 1), nhanes_adj, na.rm = TRUE) nhanes_adj <- svystandardize(nhanes_design, by = ~ agecat, over = ~ 1, population = c(55901, 77670, 72816, 45364), excluding.missing = ~ HI_CHOL) svymean(~I(HI_CHOL == 1), nhanes_adj, na.rm = TRUE) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/svy.varcoef.Rd0000755000176200001440000000132713517345016015131 0ustar liggesusers\name{svy.varcoef} \alias{svy.varcoef} %- Also NEED an `\alias' for EACH other topic documented here. \title{Sandwich variance estimator for glms} \description{ Computes the sandwich variance estimator for a generalised linear model fitted to data from a complex sample survey. Designed to be used internally by \code{\link{svyglm}}. } \usage{ svy.varcoef(glm.object, design) } \arguments{ \item{glm.object}{A \code{\link{glm}} object} \item{design}{A \code{survey.design} object } } \value{ A variance matrix } \author{ Thomas Lumley} \seealso{\code{\link{svyglm}},\code{\link{svydesign}}, \code{\link{svyCprod}} } \keyword{regression}% at least one, from doc/KEYWORDS \keyword{survey}% __ONLY ONE__ keyword per line survey/man/bootweights.Rd0000644000176200001440000000621613517345016015221 0ustar liggesusers\name{bootweights} \alias{bootweights} \alias{subbootweights} \alias{mrbweights} \alias{bootstratum} %- Also NEED an '\alias' for EACH other topic documented here. \title{Compute survey bootstrap weights } \description{ Bootstrap weights for infinite populations ('with replacement' sampling) are created by sampling with replacement from the PSUs in each stratum. \code{subbootweights()} samples \code{n-1} PSUs from the \code{n} available (Rao and Wu), \code{bootweights} samples \code{n} (Canty and Davison). For multistage designs or those with large sampling fractions, \code{mrbweights} implements Preston's multistage rescaled bootstrap. The multistage rescaled bootstrap is still useful for single-stage designs with small sampling fractions, where it reduces to a half-sample replicate method. } \usage{ bootweights(strata, psu, replicates = 50, fpc = NULL, fpctype = c("population", "fraction", "correction"), compress = TRUE) subbootweights(strata, psu, replicates = 50, compress = TRUE) mrbweights(clusters, stratas, fpcs, replicates=50, multicore=getOption("survey.multicore")) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{strata}{Identifier for sampling strata (top level only)} \item{stratas}{data frame of strata for all stages of sampling} \item{psu}{Identifier for primary sampling units} \item{clusters}{data frame of identifiers for sampling units at each stage} \item{replicates}{Number of bootstrap replicates} \item{fpc}{Finite population correction (top level only) } \item{fpctype}{Is \code{fpc} the population size, sampling fraction, or 1-sampling fraction?} \item{fpcs}{\code{survey_fpc} object with population and sample size at each stage} \item{compress}{Should the replicate weights be compressed?} \item{multicore}{Use the \code{multicore} package to generate the replicates in parallel} } \value{ A set of replicate weights } \section{warning}{With \code{multicore=TRUE} the resampling procedure does not use the current random seed, so the results cannot be exactly reproduced even by using \code{set.seed()}} \note{ These bootstraps are strictly appropriate only when the first stage of sampling is a simple or stratified random sample of PSUs with or without replacement, and not (eg) for PPS sampling. The functions will not enforce simple random sampling, so they can be used (approximately) for data that have had non-response corrections and other weight adjustments. It is preferable to apply these adjustments after creating the bootstrap replicate weights, but that may not be possible with public-use data. } \references{Canty AJ, Davison AC. (1999) Resampling-based variance estimation for labour force surveys. The Statistician 48:379-391 Judkins, D. (1990), "Fay's Method for Variance Estimation" Journal of Official Statistics, 6, 223-239. Preston J. (2009) Rescaled bootstrap for stratified multistage sampling. Survey Methodology 35(2) 227-234 Rao JNK, Wu CFJ. Bootstrap inference for sample surveys. Proc Section on Survey Research Methodology. 1993 (866--871) } \seealso{\code{\link{as.svrepdesign}}} \keyword{survey}% at least one, from doc/KEYWORDS survey/man/svylogrank.Rd0000644000176200001440000000534713714425162015066 0ustar liggesusers\name{svylogrank} \alias{svylogrank} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Compare survival distributions } \description{ Computes a weighted version of the logrank test for comparing two or more survival distributions. The generalization to complex samples is based on the characterization of the logrank test as the score test in a Cox model. Under simple random sampling with replacement, this function with \code{rho=0} and \code{gamma=0} is almost identical to the robust score test in the survival package. The \code{rho=0} and \code{gamma=0} version was proposed by Rader (2014). } \usage{ svylogrank(formula, design, rho=0,gamma=0,method=c("small","large","score"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ Model formula with a single predictor. The predictor must be a factor if it has more than two levels. } \item{design}{ A survey design object } \item{rho,gamma}{Coefficients for the Harrington/Fleming G-rho-gamma tests. The default is the logrank test, \code{rho=1} gives a generalised Wilcoxon test} \item{method}{\code{"small"} works faster when a matrix with dimension number of events by number of people fits easily in memory; \code{"large"} works faster for large data sets; \code{"score"} works by brute-force construction of an expanded data set, and is for debugging} \item{\dots}{ for future expansion. } } \value{ A vector containing the z-statistic for comparing each level of the variable to the lowest, the chisquared statistic for the logrank test, and the p-value. } \references{ Rader, Kevin Andrew. 2014. Methods for Analyzing Survival and Binary Data in Complex Surveys. Doctoral dissertation, Harvard University.\url{http://nrs.harvard.edu/urn-3:HUL.InstRepos:12274283} } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{svykm}}, \code{\link{svycoxph}}. } \examples{ library("survival") data(nwtco) ## stratified on case status dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco, method="simple") svylogrank(Surv(edrel,rel)~factor(stage),design=dcchs) data(pbc, package="survival") pbc$randomized <- with(pbc, !is.na(trt) & trt>0) biasmodel<-glm(randomized~age*edema,data=pbc) pbc$randprob<-fitted(biasmodel) dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized)) svylogrank(Surv(time,status==2)~trt,design=dpbc) svylogrank(Surv(time,status==2)~trt,design=dpbc,rho=1) rpbc<-as.svrepdesign(dpbc) svylogrank(Surv(time,status==2)~trt,design=rpbc) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{survival}% __ONLY ONE__ keyword per line survey/man/SE.Rd0000755000176200001440000000120313517345016013164 0ustar liggesusers\name{SE} \alias{SE} \alias{SE.default} \alias{SE.svrepstat} %- Also NEED an '\alias' for EACH other topic documented here. \title{Extract standard errors } \description{ Extracts standard errors from an object. The default method is for objects with a \code{\link{vcov}} method. } \usage{ SE(object, ...) \method{SE}{default}(object,...) \method{SE}{svrepstat}(object,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{An object} \item{\dots}{Arguments for future expansion } } \value{ Vector of standard errors. } \seealso{ \code{\link{vcov}}} \keyword{models}% at least one, from doc/KEYWORDS survey/man/svyloglin.Rd0000644000176200001440000000651513517345016014713 0ustar liggesusers\name{svyloglin} \alias{svyloglin} \alias{anova.svyloglin} \alias{update.svyloglin} \alias{coef.svyloglin} \alias{print.anova.svyloglin} %- Also NEED an '\alias' for EACH other topic documented here. \title{Loglinear models } \description{ Fit and compare hierarchical loglinear models for complex survey data. } \usage{ svyloglin(formula, design, ...) \method{update}{svyloglin}(object,formula,...) \method{anova}{svyloglin}(object,object1,...,integrate=FALSE) \method{print}{anova.svyloglin}(x,pval=c("F","saddlepoint","lincom","chisq"),...) \method{coef}{svyloglin}(object,...,intercept=FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{Model formula} \item{design}{survey design object} \item{object,object1}{loglinear model from \code{svyloglin}} \item{pval}{p-value approximation: see Details} \item{integrate}{Compute the exact asymptotic p-value (slow)?} \item{\dots}{not used } \item{intercept}{Report the intercept?} \item{x}{anova object} } \details{ The loglinear model is fitted to a multiway table with probabilities estimated by \code{\link{svymean}} and with the sample size equal to the observed sample size, treating the resulting table as if it came from iid multinomial sampling, as described by Rao and Scott. The variance-covariance matrix does not include the intercept term, and so by default neither does the \code{coef} method. A Newton-Raphson algorithm is used, rather than iterative proportional fitting, so starting values are not needed. The \code{anova} method computes the quantities that would be the score (Pearson) and likelihood ratio chi-squared statistics if the data were an iid sample. It computes four p-values for each of these, based on the exact asymptotic distribution (see \code{\link{pchisqsum}}), a saddlepoint approximateion to this distribution, a scaled chi-squared distribution, and a scaled F-distribution. When testing the two-way interaction model against the main-effects model in a two-way table the score statistic and p-values match the Rao-Scott tests computed by \code{\link{svychisq}}. The \code{anova} method can only compare two models if they are for exactly the same multiway table (same variables and same order). The \code{update} method will help with this. It is also much faster to use \code{update} than \code{svyloglin} for a large data set: its time complexity depends only on the size of the model, not on the size of the data set. It is not possible to fit a model using a variable created inline, eg \code{I(x<10)}, since the multiway table is based on all variables used in the formula. } \value{ Object of class \code{"svyloglin"} } \references{ Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. } \seealso{\code{\link{svychisq}}, \code{\link{svyglm}},\code{\link{pchisqsum}}} \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) a<-svyloglin(~stype+comp.imp,dclus1) b<-update(a,~.^2) an<-anova(a,b) an print(an, pval="saddlepoint") ## Wald test regTermTest(b, ~stype:comp.imp) ## linear-by-linear association d<-update(a,~.+as.numeric(stype):as.numeric(comp.imp)) an1<-anova(a,d) an1 } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/svyprcomp.Rd0000644000176200001440000000520313517345016014720 0ustar liggesusers\name{svyprcomp} \Rdversion{1.1} \alias{svyprcomp} \alias{biplot.svyprcomp} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Sampling-weighted principal component analysis } \description{ Computes principal components using the sampling weights. } \usage{ svyprcomp(formula, design, center = TRUE, scale. = FALSE, tol = NULL, scores = FALSE, ...) \method{biplot}{svyprcomp}(x, cols=c("black","darkred"),xlabs=NULL, weight=c("transparent","scaled","none"), max.alpha=0.5,max.cex=0.5,xlim=NULL,ylim=NULL,pc.biplot=FALSE, expand=1,xlab=NULL,ylab=NULL, arrow.len=0.1, ...) } \arguments{ \item{formula}{ model formula describing variables to be used } \item{design}{ survey design object. } \item{center}{ Center data before analysis? } \item{scale.}{ Scale to unit variance before analysis? } \item{tol}{ Tolerance for omitting components from the results; a proportion of the standard deviation of the first component. The default is to keep all components. } \item{scores}{ Return scores on each component? These are needed for \code{biplot}. } \item{x}{ A \code{svyprcomp} object } \item{cols}{ Base colors for observations and variables respectively } \item{xlabs}{ Formula, or character vector, giving labels for each observation } \item{weight}{ How to display the sampling weights: \code{"scaled"} changes the size of the point label, \code{"transparent"} uses opacity proportional to sampling weight, \code{"none"} changes neither. } \item{max.alpha}{ Opacity for the largest sampling weight, or for all points if \code{weight!="transparent"} } \item{max.cex}{ Character size (as a multiple of \code{par("cex")}) for the largest sampling weight, or for all points if \code{weight!="scaled"} } \item{xlim,ylim,xlab,ylab}{Graphical parameters} \item{expand,arrow.len}{See \code{\link{biplot}}} \item{pc.biplot}{See \code{link{biplot.prcomp}}} \item{\dots}{ Other arguments to \code{\link{prcomp}}, or graphical parameters for \code{biplot} } } \value{ \code{svyprcomp} returns an object of class \code{svyprcomp}, similar to class \code{prcomp} but including design information } \seealso{ \code{\link{prcomp}}, \code{\link{biplot.prcomp}} } \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) pc <- svyprcomp(~api99+api00+ell+hsg+meals+emer, design=dclus2,scale=TRUE,scores=TRUE) pc biplot(pc, xlabs=~dnum, weight="none") biplot(pc, xlabs=~dnum,max.alpha=1) biplot(pc, weight="scaled",max.cex=1.5, xlabs=~dnum) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{hplot}% __ONLY ONE__ keyword per line \keyword{multivariate}survey/man/as.svrepdesign.Rd0000755000176200001440000001072114113106500015577 0ustar liggesusers\name{as.svrepdesign} \alias{as.svrepdesign} \alias{as.svrepdesign.default} \alias{as.svrepdesign.svyimputationList} %- Also NEED an `\alias' for EACH other topic documented here. \title{Convert a survey design to use replicate weights} \description{ Creates a replicate-weights survey design object from a traditional strata/cluster survey design object. \code{JK1} and \code{JKn} are jackknife methods, \code{BRR} is Balanced Repeated Replicates and \code{Fay} is Fay's modification of this, \code{bootstrap} is Canty and Davison's bootstrap, \code{subbootstrap} is Rao and Wu's \eqn{(n-1)} bootstrap, and \code{mrbbootstrap} is Preston's multistage rescaled bootstrap. With a \code{svyimputationList} object, the same replicate weights will be used for each imputation if the sampling weights are all the same and \code{separate.replicates=FALSE}. } \usage{ as.svrepdesign(design,...) \method{as.svrepdesign}{default}(design, type=c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap","mrbbootstrap","Fay"), fay.rho = 0, fpc=NULL,fpctype=NULL,..., compress=TRUE, mse=getOption("survey.replicates.mse")) \method{as.svrepdesign}{svyimputationList}(design, type=c("auto", "JK1", "JKn", "BRR", "bootstrap", "subbootstrap","mrbbootstrap","Fay"), fay.rho = 0, fpc=NULL,fpctype=NULL, separate.replicates=FALSE, ..., compress=TRUE, mse=getOption("survey.replicates.mse")) } %- maybe also `usage' for other objects documented here. \arguments{ \item{design}{Object of class \code{survey.design} or \code{svyimputationList}. Must not have been post-stratified/raked/calibrated in R} \item{type}{Type of replicate weights. \code{"auto"} uses JKn for stratified, JK1 for unstratified designs} \item{fay.rho}{Tuning parameter for Fay's variance method } \item{fpc,fpctype,\dots}{Passed to \code{jk1weights}, \code{jknweights}, \code{brrweights}, \code{bootweights}, \code{subbootweights}, or \code{mrbweights}.} \item{separate.replicates}{Compute replicate weights separately for each design (useful for the bootstrap types, which are not deterministic} \item{compress}{Use a compressed representation of the replicate weights matrix.} \item{mse}{if \code{TRUE}, compute variances from sums of squares around the point estimate, rather than the mean of the replicates} } \value{ Object of class \code{svyrep.design}. } \references{ Canty AJ, Davison AC. (1999) Resampling-based variance estimation for labour force surveys. The Statistician 48:379-391 Judkins, D. (1990), "Fay's Method for Variance Estimation," Journal of Official Statistics, 6, 223-239. Preston J. (2009) Rescaled bootstrap for stratified multistage sampling. Survey Methodology 35(2) 227-234 Rao JNK, Wu CFJ. Bootstrap inference for sample surveys. Proc Section on Survey Research Methodology. 1993 (866--871) } \seealso{\code{\link{brrweights}}, \code{\link{svydesign}}, \code{\link{svrepdesign}}, \code{\link{bootweights}}, \code{\link{subbootweights}}, \code{\link{mrbweights}} } \examples{ data(scd) scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE, fpc=rep(5,6)) scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE) # convert to BRR replicate weights scd2brr <- as.svrepdesign(scdnofpc, type="BRR") scd2fay <- as.svrepdesign(scdnofpc, type="Fay",fay.rho=0.3) # convert to JKn weights scd2jkn <- as.svrepdesign(scdnofpc, type="JKn") # convert to JKn weights with finite population correction scd2jknf <- as.svrepdesign(scddes, type="JKn") ## with user-supplied hadamard matrix scd2brr1 <- as.svrepdesign(scdnofpc, type="BRR", hadamard.matrix=paley(11)) svyratio(~alive, ~arrests, design=scd2brr) svyratio(~alive, ~arrests, design=scd2brr1) svyratio(~alive, ~arrests, design=scd2fay) svyratio(~alive, ~arrests, design=scd2jkn) svyratio(~alive, ~arrests, design=scd2jknf) data(api) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) ## convert to JK1 jackknife rclus1<-as.svrepdesign(dclus1) ## convert to bootstrap bclus1<-as.svrepdesign(dclus1,type="bootstrap", replicates=100) svymean(~api00, dclus1) svytotal(~enroll, dclus1) svymean(~api00, rclus1) svytotal(~enroll, rclus1) svymean(~api00, bclus1) svytotal(~enroll, bclus1) dclus2<-svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2) mrbclus2<-as.svrepdesign(dclus2, type="mrb",replicates=100) svytotal(~api00+stype, dclus2) svytotal(~api00+stype, mrbclus2) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/scd.Rd0000755000176200001440000000362213517345016013435 0ustar liggesusers\name{scd} \alias{scd} \non_function{} \title{Survival in cardiac arrest} \usage{data(scd)} \description{ These data are from Section 12.2 of Levy and Lemeshow. They describe (a possibly apocryphal) study of survival in out-of-hospital cardiac arrest. Two out of five ambulance stations were sampled from each of three emergency service areas. } \format{ This data frame contains the following columns: \describe{ \item{ESA}{Emergency Service Area (strata)} \item{ambulance}{Ambulance station (PSU)} \item{arrests}{estimated number of cardiac arrests} \item{alive}{number reaching hospital alive} } } \source{ Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley. } \examples{ data(scd) ## survey design objects scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE, fpc=rep(5,6)) scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE) # convert to BRR replicate weights scd2brr <- as.svrepdesign(scdnofpc, type="BRR") # or to Rao-Wu bootstrap scd2boot <- as.svrepdesign(scdnofpc, type="subboot") # use BRR replicate weights from Levy and Lemeshow repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights) # ratio estimates svyratio(~alive, ~arrests, design=scddes) svyratio(~alive, ~arrests, design=scdnofpc) svyratio(~alive, ~arrests, design=scd2brr) svyratio(~alive, ~arrests, design=scd2boot) svyratio(~alive, ~arrests, design=scdrep) # or a logistic regression summary(svyglm(cbind(alive,arrests-alive)~1, family=quasibinomial, design=scdnofpc)) summary(svyglm(cbind(alive,arrests-alive)~1, family=quasibinomial, design=scdrep)) # Because no sampling weights are given, can't compute design effects # without replacement: use deff="replace" svymean(~alive+arrests, scddes, deff=TRUE) svymean(~alive+arrests, scddes, deff="replace") } \keyword{datasets} survey/man/svyciprop.Rd0000644000176200001440000000764413517345016014727 0ustar liggesusers\name{svyciprop} \alias{svyciprop} %- Also NEED an '\alias' for EACH other topic documented here. \title{Confidence intervals for proportions } \description{ Computes confidence intervals for proportions using methods that may be more accurate near 0 and 1 than simply using \code{confint(svymean())}. } \usage{ svyciprop(formula, design, method = c("logit", "likelihood", "asin", "beta", "mean","xlogit"), level = 0.95, df=degf(design),...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{Model formula specifying a single binary variable} \item{design}{ survey design object} \item{method}{ See Details below. Partial matching is done on the argument.} \item{level}{Confidence level for interval} \item{df}{denominator degrees of freedom, for all methods except \code{"beta"}. Use \code{Inf} for confidence intervals based on a Normal distribution, and for \code{"likelihood"} and \code{"logit"} use \code{NULL} for the default method in glms (currently \code{degf(design)-1}, but this may be improved in the future)} \item{\dots}{For \code{"mean"} and \code{"asin"}, this is passed to \code{\link{confint.svystat}} } } \details{ The \code{"logit"} method fits a logistic regression model and computes a Wald-type interval on the log-odds scale, which is then transformed to the probability scale. The \code{"likelihood"} method uses the (Rao-Scott) scaled chi-squared distribution for the loglikelihood from a binomial distribution. The \code{"asin"} method uses the variance-stabilising transformation for the binomial distribution, the arcsine square root, and then back-transforms the interval to the probability scale The \code{"beta"} method uses the incomplete beta function as in \code{\link{binom.test}}, with an effective sample size based on the estimated variance of the proportion. (Korn and Graubard, 1998) The \code{"xlogit"} method uses a logit transformation of the mean and then back-transforms to the probablity scale. This appears to be the method used by SUDAAN and SPSS COMPLEX SAMPLES. The \code{"mean"} method is a Wald-type interval on the probability scale, the same as \code{confint(svymean())} All methods undercover for probabilities close enough to zero or one, but \code{"beta"}, \code{"likelihood"}, \code{"logit"}, and \code{"logit"} are noticeably better than the other two. None of the methods will work when the observed proportion is exactly 0 or 1. The \code{confint} method extracts the confidence interval; the \code{vcov} and \code{SE} methods just report the variance or standard error of the mean. } \value{ The point estimate of the proportion, with the confidence interval as an attribute } \references{ Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. Korn EL, Graubard BI. (1998) Confidence Intervals For Proportions With Small Expected Number of Positive Counts Estimated From Survey Data. Survey Methodology 23:193-201. } \seealso{ \code{\link{svymean}}, \code{\link{yrbs}} } \examples{ data(api) dclus1<-svydesign(id=~dnum, fpc=~fpc, data=apiclus1) svyciprop(~I(ell==0), dclus1, method="li") svyciprop(~I(ell==0), dclus1, method="lo") svyciprop(~I(ell==0), dclus1, method="as") svyciprop(~I(ell==0), dclus1, method="be") svyciprop(~I(ell==0), dclus1, method="me") svyciprop(~I(ell==0), dclus1, method="xl") ## reproduces Stata svy: mean svyciprop(~I(ell==0), dclus1, method="me", df=degf(dclus1)) ## reproduces Stata svy: prop svyciprop(~I(ell==0), dclus1, method="lo", df=degf(dclus1)) rclus1<-as.svrepdesign(dclus1) svyciprop(~I(emer==0), rclus1, method="li") svyciprop(~I(emer==0), rclus1, method="lo") svyciprop(~I(emer==0), rclus1, method="as") svyciprop(~I(emer==0), rclus1, method="be") svyciprop(~I(emer==0), rclus1, method="me") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/compressWeights.Rd0000755000176200001440000000320213517345016016044 0ustar liggesusers\name{compressWeights} \alias{compressWeights} \alias{compressWeights.default} \alias{compressWeights.repweights_compressed} \alias{[.repweights_compressed} \alias{dim.repweights_compressed} \alias{dimnames.repweights_compressed} \alias{as.matrix.repweights_compressed} \alias{as.matrix.repweights} \alias{as.vector.repweights_compressed} \alias{compressWeights.svyrep.design} %- Also NEED an '\alias' for EACH other topic documented here. \title{Compress replicate weight matrix} \description{ Many replicate weight matrices have redundant rows, such as when weights are the same for all observations in a PSU. This function produces a compressed form. Methods for \code{as.matrix} and \code{as.vector} extract and expand the weights. } \usage{ compressWeights(rw, ...) \method{compressWeights}{svyrep.design}(rw,...) \method{as.matrix}{repweights_compressed}(x,...) \method{as.vector}{repweights_compressed}(x,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{rw}{A set of replicate weights or a \code{svyrep.design} object} \item{x}{A compressed set of replicate weights} \item{\dots}{For future expansion} } \value{ An object of class \code{repweights_compressed} or a \code{svyrep.design} object with \code{repweights} element of class \code{repweights_compressed} } \seealso{\code{\link{jknweights}},\code{\link{as.svrepdesign}}} \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1c<-as.svrepdesign(dclus1,compress=TRUE) rclus1<-as.svrepdesign(dclus1,compress=FALSE) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/svycontrast.Rd0000644000176200001440000000765514461114655015274 0ustar liggesusers\name{svycontrast} \alias{svycontrast} %- Also NEED an '\alias' for EACH other topic documented here. \title{Linear and nonlinearconstrasts of survey statistics } \description{ Computes linear or nonlinear contrasts of estimates produced by survey functions (or any object with \code{coef} and \code{vcov} methods). } \usage{ svycontrast(stat, contrasts, add=FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{stat}{object of class \code{svrepstat} or \code{svystat} } \item{contrasts}{A vector or list of vectors of coefficients, or a call or list of calls } \item{add}{keep all the coefficients of the input in the output?} \item{\dots}{For future expansion} } \value{ Object of class \code{svrepstat} or \code{svystat} or \code{svyvar} } \details{ If \code{contrasts} is a list, the element names are used as names for the returned statistics. If an element of \code{contrasts} is shorter than \code{coef(stat)} and has names, the names are used to match up the vectors and the remaining elements of \code{contrasts} are assumed to be zero. If the names are not legal variable names (eg \code{0.1}) they must be quoted (eg \code{"0.1"}) If \code{contrasts} is a \code{"call"} or list of \code{"call"s}, and \code{stat} is a \code{svrepstat} object including replicates, the replicates are transformed and used to compute the variance. If \code{stat} is a \code{svystat} object or a \code{svrepstat} object without replicates, the delta-method is used to compute variances, and the calls must use only functions that \code{\link{deriv}} knows how to differentiate. If the names are not legal variable names they must be quoted with backticks (eg \code{`0.1`}). If \code{stats} is a \code{svyvar} object, the estimates are elements of a matrix and the names are the row and column names pasted together with a colon. } \seealso{\code{\link{regTermTest}}, \code{svyglm}} \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) a <- svytotal(~api00+enroll+api99, dclus1) svycontrast(a, list(avg=c(0.5,0,0.5), diff=c(1,0,-1))) ## if contrast vectors have names, zeroes may be omitted svycontrast(a, list(avg=c(api00=0.5,api99=0.5), diff=c(api00=1,api99=-1))) ## nonlinear contrasts svycontrast(a, quote(api00/api99)) svyratio(~api00, ~api99, dclus1) ## Example: standardised skewness coefficient moments<-svymean(~I(api00^3)+I(api00^2)+I(api00), dclus1) svycontrast(moments, quote((`I(api00^3)`-3*`I(api00^2)`*`I(api00)`+ 3*`I(api00)`*`I(api00)`^2-`I(api00)`^3)/ (`I(api00^2)`-`I(api00)`^2)^1.5)) ## Example: geometric means ## using delta method meanlogs <- svymean(~log(api00)+log(api99), dclus1) svycontrast(meanlogs, list(api00=quote(exp(`log(api00)`)), api99=quote(exp(`log(api99)`)))) ## using delta method rclus1<-as.svrepdesign(dclus1) meanlogs <- svymean(~log(api00)+log(api99), rclus1) svycontrast(meanlogs, list(api00=quote(exp(`log(api00)`)), api99=quote(exp(`log(api99)`)))) ## why is add=TRUE useful? (totals<-svyby(~enroll,~stype,design=dclus1,svytotal,covmat=TRUE)) totals1<-svycontrast(totals, list(total=c(1,1,1)), add=TRUE) svycontrast(totals1, list(quote(E/total), quote(H/total), quote(M/total))) totals2<-svycontrast(totals, list(total=quote(E+H+M)), add=TRUE) all.equal(as.matrix(totals1),as.matrix(totals2)) ## more complicated svyby means <- svyby(~api00+api99, ~stype+sch.wide, design=dclus1, svymean,covmat=TRUE) svycontrast(means, quote(`E.No:api00`-`E.No:api99`)) svycontrast(means, quote(`E.No:api00`/`E.No:api99`)) ## transforming replicates meanlogs_r <- svymean(~log(api00)+log(api99), rclus1, return.replicates=TRUE) svycontrast(meanlogs_r, list(api00=quote(exp(`log(api00)`)), api99=quote(exp(`log(api99)`)))) ## converting covariances to correlations vmat <-svyvar(~api00+ell,dclus1) print(vmat,cov=TRUE) cov2cor(as.matrix(vmat))[1,2] svycontrast(vmat, quote(`api00:ell`/sqrt(`api00:api00`*`ell:ell`))) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/weights.survey.design.Rd0000755000176200001440000000254513517345016017145 0ustar liggesusers\name{weights.survey.design} \alias{weights.survey.design} \alias{weights.svyrep.design} \alias{weights.survey_fpc} %- Also NEED an `\alias' for EACH other topic documented here. \title{Survey design weights} \description{ Extract weights from a survey design object. } \usage{ \method{weights}{survey.design}(object, ...) \method{weights}{svyrep.design}(object, type=c("replication","sampling","analysis"), ...) \method{weights}{survey_fpc}(object,final=TRUE,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{object}{Survey design object} \item{type}{Type of weights: \code{"analysis"} combines sampling and replication weights.} \item{final}{If \code{FALSE} return a data frame with sampling weights at each stage of sampling.} \item{\dots}{Other arguments ignored } } \value{ vector or matrix of weights } \seealso{\code{\link{svydesign}}, \code{\link{svrepdesign}}, \code{\link{as.fpc}} } \examples{ data(scd) scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE, fpc=rep(5,6)) repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights) weights(scdrep) weights(scdrep, type="sampling") weights(scdrep, type="analysis") weights(scddes) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/smoothArea.Rd0000644000176200001440000001123014567546455014775 0ustar liggesusers\name{smoothArea} \alias{svysmoothArea} \title{Small area estimation via basic area level model} \usage{ svysmoothArea( formula, domain, design = NULL, adj.mat = NULL, X.domain = NULL, direct.est = NULL, domain.size = NULL, transform = c("identity", "logit", "log"), pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3, level = 0.95, n.sample = 250, var.tol = 1e-10, return.samples = FALSE,... ) } \arguments{ \item{formula}{An object of class 'formula' describing the model to be fitted. If direct.est is specified, the right hand side of the formula is not necessary.} \item{domain}{One-sided formula specifying factors containing domain labels} \item{design}{An object of class "svydesign" containing the data for the model} \item{adj.mat}{Adjacency matrix with rownames matching the domain labels. If set to \code{NULL}, the IID spatial effect will be used.} \item{X.domain}{Data frame of areal covariates. One of the column names needs to match the name of the domain variable, in order to be linked to the data input. Currently only supporting time-invariant covariates.} \item{direct.est}{Data frame of direct estimates, with first column containing the domain variable, second column containing direct estimate, and third column containing the variance of direct estimate.} \item{domain.size}{Data frame of domain sizes. One of the column names needs to match the name of the \code{domain} variable, in order to be linked to the data input and there must be a column names 'size' containing domain sizes.} \item{transform}{Optional transformation applied to the direct estimates before fitting area level model. The default option is no transformation, but logit and log are implemented.} \item{pc.u}{Hyperparameter U for the PC prior on precisions. See the INLA documentation for more details on the parameterization.} \item{pc.alpha}{Hyperparameter alpha for the PC prior on precisions.} \item{pc.u.phi}{Hyperparameter U for the PC prior on the mixture probability phi in BYM2 model.} \item{pc.alpha.phi}{Hyperparameter alpha for the PC prior on the mixture probability phi in BYM2 model.} \item{level}{The specified level for the posterior credible intervals} \item{n.sample}{Number of draws from posterior used to compute summaries} \item{var.tol}{Tolerance parameter; if variance of an area's direct estimator is below this value, that direct estimator is dropped from model} \item{return.samples}{If TRUE, return matrix of posterior samples of area level quantities} \item{\dots}{for future methods} } \value{ A \code{svysae} object } \description{ Generates small area estimates by smoothing direct estimates using an area level model } \details{ The basic area level model is a Bayesian version of the Fay-Herriot model (Fay & Herriot,1979). It treats direct estimates of small area quantities as response data and explicitly models differences between areas using covariate information and random effects. The Fay-Herriot model can be viewed as a two-stage model: in the first stage, a sampling model represents the sampling variability of a direct estimator and in the second stage, a linking model describes the between area differences in small area quantities. More detail is given in section 4 of Mercer et al (2015). } \seealso{The \code{survey-sae} vignette} \references{ Fay, Robert E., and Roger A. Herriot. (1979). Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data. Journal of the American Statistical Association 74 (366a): 269-77. Mercer LD, Wakefield J, Pantazis A, Lutambi AM, Masanja H, Clark S. Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality. Ann Appl Stat. 2015 Dec;9(4):1889-1905. \url{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959836/} } \examples{ ## artificial data from SUMMER package ## Uses too many cores for a CRAN example \dontrun{ hasSUMMER<-tryCatch({ data("DemoData2",package="SUMMER") data("DemoMap2", package="SUMMER") }, error=function(e) FALSE) if (!isFALSE(hasSUMMER)){ library(survey) des0 <- svydesign(ids = ~clustid+id, strata = ~strata, weights = ~weights, data = DemoData2, nest = TRUE) Xmat <- aggregate(age~region, data = DemoData2, FUN = mean) cts.cov.res <- svysmoothArea(tobacco.use ~ age, domain = ~region, design = des0, adj.mat = DemoMap2$Amat, X.domain = Xmat, pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3) print(cts.cov.res) plot(cts.cov.res) } } } survey/man/nhanes.Rd0000644000176200001440000000202114073741722014127 0ustar liggesusers\name{nhanes} \alias{nhanes} \docType{data} \title{ Cholesterol data from a US survey } \description{ Data extracted from NHANES 2009-2010 on high cholesterol. } \usage{data(nhanes)} \format{ A data frame with 8591 observations on the following 7 variables. \describe{ \item{\code{SDMVPSU}}{Primary sampling units} \item{\code{SDMVSTRA}}{Sampling strata} \item{\code{WTMEC2YR}}{Sampling weights} \item{\code{HI_CHOL}}{Numeric vector: 1 for total cholesterol over 240mg/dl, 0 under 240mg/dl} \item{\code{race}}{1=Hispanic, 2=non-Hispanic white, 3=non-Hispanic black, 4=other} \item{\code{agecat}}{Age group \code{(0,19]} \code{(19,39]} \code{(39,59]} \code{(59,Inf]}} \item{\code{RIAGENDR}}{Gender: 1=male, 2=female} } } \source{ Previously at \verb{https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=laboratory&CycleBeginYear=2009} } \examples{ data(nhanes) design <- svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, nest=TRUE,data=nhanes) design } \keyword{datasets} survey/man/smoothUnit.Rd0000644000176200001440000000550014567546504015042 0ustar liggesusers\name{smoothUnit} \alias{svysmoothUnit} \title{Smooth via basic unit level model} \usage{ svysmoothUnit( formula, domain, design, family = c("gaussian", "binomial"), X.pop = NULL, adj.mat = NULL, domain.size = NULL, pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3, level = 0.95, n.sample = 250, return.samples = FALSE, X.pop.weights = NULL,... ) } \arguments{ \item{formula}{An object of class 'formula' describing the model to be fitted.} \item{domain}{One-sided formula specifying factors containing domain labels} \item{design}{An object of class "survey.design" containing the data for the model} \item{family}{of the response variable, currently supports 'binomial' (default with logit link function) or 'gaussian'.} \item{X.pop}{Data frame of population unit-level covariates. One of the column name needs to match the domain specified, in order to be linked to the data input. Currently only supporting time-invariant covariates.} \item{adj.mat}{Adjacency matrix with rownames matching the domain labels. If set to NULL, the IID spatial effect will be used.} \item{domain.size}{Data frame of domain sizes. One of the column names needs to match the name of the domain variable, in order to be linked to the data input and there must be a column names 'size' containing domain sizes. The default option is no transformation, but logit and log are implemented.} \item{pc.u}{Hyperparameter U for the PC prior on precisions. See the INLA documentation for more details on the parameterization.} \item{pc.alpha}{Hyperparameter alpha for the PC prior on precisions.} \item{pc.u.phi}{Hyperparameter U for the PC prior on the mixture probability phi in BYM2 model.} \item{pc.alpha.phi}{Hyperparameter alpha for the PC prior on the mixture probability phi in BYM2 model.} \item{level}{The specified level for the posterior credible intervals} \item{n.sample}{Number of draws from posterior used to compute summaries} \item{return.samples}{If TRUE, return matrix of posterior samples of area level quantities} \item{X.pop.weights}{Optional vector of weights to use when aggregating unit level predictions} \item{\dots}{for future expansion} } \value{ A \code{svysae} object } \description{ Generates small area estimates by smoothing direct estimates using a basic unit level model. This model assumes sampling is ignorable (no selection bias). It's a Bayesian linear (\code{family="gaussian"}) or generalised linear (\code{family="binomial"}) mixed model for the unit-level data with individual-level covariates and area-level random effects. } \seealso{The \code{survey-sae} vignette} \references{Battese, G. E., Harter, R. M., & Fuller, W. A. (1988). An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data. Journal of the American Statistical Association, 83(401), 28-36. }survey/man/svyquantile.Rd0000644000176200001440000001444014572663272015256 0ustar liggesusers\name{newsvyquantile} \alias{svyquantile} \alias{svyquantile.survey.design} \alias{svyquantile.svyrep.design} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Quantiles under complex sampling. } \description{ Estimates quantiles and confidence intervals for them. This function was completely re-written for version 4.1 of the survey package, and has a wider range of ways to define the quantile. See the vignette for a list of them. } \usage{ svyquantile(x, design, quantiles, ...) \method{svyquantile}{survey.design}(x, design, quantiles, alpha = 0.05, interval.type = c("mean", "beta","xlogit", "asin","score"), na.rm = FALSE, ci=TRUE, se = ci, qrule=c("math","school","shahvaish","hf1","hf2","hf3", "hf4","hf5","hf6","hf7","hf8","hf9"), df = NULL, ...) \method{svyquantile}{svyrep.design}(x, design, quantiles, alpha = 0.05, interval.type = c("mean", "beta","xlogit", "asin","quantile"), na.rm = FALSE, ci = TRUE, se=ci, qrule=c("math","school","shahvaish","hf1","hf2","hf3", "hf4","hf5","hf6","hf7","hf8","hf9"), df = NULL, return.replicates=FALSE,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ A one-sided formula describing variables to be used } \item{design}{ Design object } \item{quantiles}{ Numeric vector specifying which quantiles are requested } \item{alpha}{Specified confidence interval coverage} \item{interval.type}{See Details below} \item{na.rm}{Remove missing values?} \item{ci,se}{Return an estimated confidence interval and standard error?} \item{qrule}{Rule for defining the quantiles: either a character string specifying one of the built-in rules, or a function} \item{df}{Degrees of freedom for confidence interval estimation: \code{NULL} specifies \code{degf(design)}} \item{return.replicates}{Return replicate estimates of the quantile (only for \code{interval.type="quantile"})} \item{\dots}{ For future expansion } } \details{ The \code{p}th quantile is defined as the value where the estimated cumulative distribution function is equal to \code{p}. As with quantiles in unweighted data, this definition only pins down the quantile to an interval between two observations, and a rule is needed to interpolate. The default is the mathematical definition, the lower end of the quantile interval; \code{qrule="school"} uses the midpoint of the quantile interval; \code{"hf1"} to \code{"hf9"} are weighted analogues of \code{type=1} to \code{9} in \code{\link{quantile}}. See the vignette "Quantile rules" for details and for how to write your own. By default, confidence intervals are estimated using Woodruff's (1952) method, which involves computing the quantile, estimating a confidence interval for the proportion of observations below the quantile, and then transforming that interval using the estimated CDF. In that context, the \code{interval.type} argument specifies how the confidence interval for the proportion is computed, matching \code{\link{svyciprop}}. In contrast to \code{oldsvyquantile}, \code{NaN} is returned if a confidence interval endpoint on the probability scale falls outside \code{[0,1]}. There are two exceptions. For \code{svydesign} objects, \code{interval.type="score"} asks for the Francisco & Fuller confidence interval based on inverting a score test. According to Dorfmann & Valliant, this interval has inferior performance to the \code{"beta"} and \code{"logit"} intervals; it is provided for compatibility. For replicate-weight designs, \code{interval.type="quantile"} ask for an interval based directly on the replicates of the quantile. This interval is not valid for jackknife-type replicates, though it should perform well for bootstrap-type replicates, BRR, and SDR. The \code{df} argument specifies degrees of freedom for a t-distribution approximation to distributions of means. The default is the design degrees of freedom. Specify \code{df=Inf} to use a Normal distribution (eg, for compatibility). When the standard error is requested, it is estimated by dividing the confidence interval length by the number of standard errors in a t confidence interval with the specified \code{alpha}. For example, with \code{alpha=0.05} and \code{df=Inf} the standard error is estimated as the confidence interval length divided by \code{2*1.96}. } \value{ An object of class \code{"newsvyquantile"}, except that with a replicate-weights design and \code{interval.type="quantile"} and \code{return.replicates=TRUE} it's an object of class \code{"svrepstat"} } \references{ Dorfman A, Valliant R (1993) Quantile variance estimators in complex surveys. Proceedings of the ASA Survey Research Methods Section. 1993: 866-871 Francisco CA, Fuller WA (1986) Estimation of the distribution function with a complex survey. Technical Report, Iowa State University. Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical packages, The American Statistician 50, 361-365. Shah BV, Vaish AK (2006) Confidence Intervals for Quantile Estimation from Complex Survey Data. Proceedings of the Section on Survey Research Methods. Woodruff RS (1952) Confidence intervals for medians and other position measures. JASA 57, 622-627. } \seealso{ \code{vignette("qrule", package = "survey")} \code{\link{oldsvyquantile}} \code{\link{quantile}} } \examples{ data(api) ## population quantile(apipop$api00,c(.25,.5,.75)) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) bclus1<-as.svrepdesign(dclus1,type="boot") svyquantile(~api00, dclus1, c(.25,.5,.75)) svyquantile(~api00, dclus1, c(.25,.5,.75),interval.type="beta") svyquantile(~api00, rclus1, c(.25,.5,.75)) svyquantile(~api00, rclus1, c(.25,.5,.75),interval.type="quantile") svyquantile(~api00, bclus1, c(.25,.5,.75),interval.type="quantile") svyquantile(~api00+ell, dclus1, c(.25,.5,.75), qrule="math") svyquantile(~api00+ell, dclus1, c(.25,.5,.75), qrule="school") svyquantile(~api00+ell, dclus1, c(.25,.5,.75), qrule="hf8") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey}% use one of RShowDoc("KEYWORDS") \keyword{univar}% at least one, from doc/KEYWORDS survey/man/svyqqplot.Rd0000644000176200001440000000401313710466023014733 0ustar liggesusers\name{svyqqplot} \alias{svyqqplot} \alias{svyqqmath} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Quantile-quantile plots for survey data } \description{ Quantile-quantile plots either against a specified distribution function or comparing two variables from the same or different designs. } \usage{ svyqqplot(formula, design, designx = NULL, na.rm = TRUE, qrule = "hf8", xlab = NULL, ylab = NULL, ...) svyqqmath(x, design, null=qnorm, na.rm=TRUE, xlab="Expected",ylab="Observed",...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x,formula}{ A one-sided formula for \code{svyqqmath} or a two-sided formula for \code{svyqqplot} } \item{design}{ Survey design object to look up variables } \item{designx}{ Survey design object to look up the RHS variable in \code{svyqqplot}, if different from the LHS variable } \item{null}{Quantile function to compare the data quantiles to} \item{na.rm}{ Remove missing values } \item{qrule}{ How to define quantiles for \code{svyqqplot} -- see \code{\link{svyquantile}} for possible values } \item{xlab,ylab}{ Passed to \code{plot}. For \code{svyqqplot}, if these are \code{NULL} they are replaced by the variable names } \item{\dots}{ Graphical options to be passed to \code{plot} } } \value{ None } \seealso{ \code{\link{quantile}} \code{\link{qqnorm}} \code{\link{qqplot}} } \examples{ data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) svyqqmath(~api99, design=dstrat) svyqqplot(api00~api99, design=dstrat) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) opar<-par(mfrow=c(1,2)) ## sample distributions very different qqplot(apiclus1$enroll, apistrat$enroll); abline(0,1) ## estimated population distributions much more similar svyqqplot(enroll~enroll, design=dstrat,designx=dclus1,qrule=survey:::qrule_hf8); abline(0,1) par(opar) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey }% use one of RShowDoc("KEYWORDS") survey/man/withReplicates.Rd0000755000176200001440000001337013730746661015664 0ustar liggesusers\name{withReplicates} \alias{withReplicates} \alias{withReplicates.svyrep.design} \alias{withReplicates.svrepvar} \alias{withReplicates.svrepstat} \alias{withReplicates.svyimputationList} \alias{vcov.svyrep.design} %- Also NEED an `\alias' for EACH other topic documented here. \title{Compute variances by replicate weighting} \description{ Given a function or expression computing a statistic based on sampling weights, \code{withReplicates} evaluates the statistic and produces a replicate-based estimate of variance. \code{vcov.svrep.design} produces the variance estimate from a set of replicates and the design object. } \usage{ withReplicates(design, theta,..., return.replicates=FALSE) \method{withReplicates}{svyrep.design}(design, theta, rho = NULL, ..., scale.weights=FALSE, return.replicates=FALSE) \method{withReplicates}{svrepvar}(design, theta, ..., return.replicates=FALSE) \method{withReplicates}{svrepstat}(design, theta, ..., return.replicates=FALSE) \method{withReplicates}{svyimputationList}(design, theta, ..., return.replicates=FALSE) \method{vcov}{svyrep.design}(object, replicates, centre,...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{design}{A survey design with replicate weights (eg from \code{\link{svrepdesign}}) or a suitable object with replicate parameter estimates} \item{theta}{A function or expression: see Details below} \item{rho}{If \code{design} uses BRR weights, \code{rho} optionally specifies the parameter for Fay's variance estimator.} \item{\dots}{Other arguments to \code{theta}} \item{scale.weights}{Divide the probability weights by their sum (can help with overflow problems)} \item{return.replicates}{Return the replicate estimates as well as the variance?} \item{object}{The replicate-weights design object used to create the replicates} \item{replicates}{A set of replicates} \item{centre}{The centering value for variance calculation. If \code{object$mse} is \code{TRUE} this is the result of estimation using the sampling weights, and must be supplied. If \code{object$mse} is \code{FALSE} the mean of the replicates is used and this argument is silently ignored. }} \details{ The method for \code{svyrep.design} objects evaluates a function or expression using the sampling weights and then each set of replicate weights. The method for \code{svrepvar} objects evaluates the function or expression on an estimated population covariance matrix and its replicates, to simplify multivariate statistics such as structural equation models. For the \code{svyrep.design} method, if \code{theta} is a function its first argument will be a vector of weights and the second argument will be a data frame containing the variables from the design object. If it is an expression, the sampling weights will be available as the variable \code{.weights}. Variables in the design object will also be in scope. It is possible to use global variables in the expression, but unwise, as they may be masked by local variables inside \code{withReplicates}. For the \code{svrepvar} method a function will get the covariance matrix as its first argument, and an expression will be evaluated with \code{.replicate} set to the variance matrix. For the \code{svrepstat} method a function will get the point estimate, and an expression will be evaluated with \code{.replicate} set to each replicate. The method can only be used when the \code{svrepstat} object includes replicates. The \code{svyimputationList} method runs \code{withReplicates} on each imputed design (which must be replicate-weight designs). } \value{ If \code{return.replicates=FALSE}, the weighted statistic, with the variance matrix as the \code{"var"} attribute. If \code{return.replicates=TRUE}, a list with elements \code{theta} for the usual return value and \code{replicates} for the replicates. } \seealso{ \code{\link{svrepdesign}}, \code{\link{as.svrepdesign}}, \code{\link{svrVar}}} \examples{ data(scd) repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights) a<-svyratio(~alive, ~arrests, design=scdrep) print(a$ratio) print(a$var) withReplicates(scdrep, quote(sum(.weights*alive)/sum(.weights*arrests))) withReplicates(scdrep, function(w,data) sum(w*data$alive)/sum(w*data$arrests)) data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) varmat<-svyvar(~api00+api99+ell+meals+hsg+mobility,rclus1,return.replicates=TRUE) withReplicates(varmat, quote( factanal(covmat=.replicate, factors=2)$unique) ) data(nhanes) nhanesdesign <- svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, nest=TRUE,data=nhanes) logistic <- svyglm(HI_CHOL~race+agecat+RIAGENDR, design=as.svrepdesign(nhanesdesign), family=quasibinomial, return.replicates=TRUE) fitted<-predict(logistic, return.replicates=TRUE, type="response") sensitivity<-function(pred,actual) mean(pred>0.1 & actual)/mean(actual) withReplicates(fitted, sensitivity, actual=logistic$y) \dontrun{ library(quantreg) data(api) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) ## convert to bootstrap bclus1<-as.svrepdesign(dclus1,type="bootstrap", replicates=100) ## median regression withReplicates(bclus1, quote(coef(rq(api00~api99, tau=0.5, weights=.weights)))) } ## pearson correlation dstrat <- svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) bstrat<- as.svrepdesign(dstrat,type="subbootstrap") v <- svyvar(~api00+api99, bstrat, return.replicates=TRUE) vcor<-cov2cor(as.matrix(v))[2,1] vreps<-v$replicates correps<-apply(vreps,1, function(v) v[2]/sqrt(v[1]*v[4])) vcov(bstrat,correps, centre=vcor) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/svynls.Rd0000644000176200001440000000374614076446322014231 0ustar liggesusers\name{svynls} \alias{svynls} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Probability-weighted nonlinear least squares } \description{ Fits a nonlinear model by probability-weighted least squares. Uses \code{nls} to do the fitting, but estimates design-based standard errors with either linearisation or replicate weights. See \code{\link{nls}} for documentation of model specification and fitting. } \usage{ svynls(formula, design, start, weights=NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ Nonlinear model specified as a formula; see \code{\link{nls}} } \item{design}{ Survey design object } \item{start}{starting values, passed to \code{\link{nls}}} \item{weights}{ Non-sampling weights, eg precision weights to give more efficient estimation in the presence of heteroscedasticity. } \item{\dots}{ Other arguments to \code{nls} (especially, \code{start}). Also supports \code{return.replicates} for replicate-weight designs and \code{influence} for other designs. } } \value{ Object of class \code{svynls}. The fitted \code{nls} object is included as the \code{fit} element. } \seealso{ \code{\link{svymle}} for maximum likelihood with linear predictors on one or more parameters } \examples{ set.seed(2020-4-3) x<-rep(seq(0,50,1),10) y<-((runif(1,10,20)*x)/(runif(1,0,10)+x))+rnorm(510,0,1) pop_model<-nls(y~a*x/(b+x), start=c(a=15,b=5)) df<-data.frame(x=x,y=y) df$p<-ifelse((y-fitted(pop_model))*(x-mean(x))>0, .4,.1) df$strata<-ifelse(df$p==.4,"a","b") in_sample<-stratsample(df$strata, round(table(df$strat)*c(0.4,0.1))) sdf<-df[in_sample,] des<-svydesign(id=~1, strata=~strata, prob=~p, data=sdf) pop_model (biased_sample<-nls(y~a*x/(b+x),data=sdf, start=c(a=15,b=5))) (corrected <- svynls(y~a*x/(b+x), design=des, start=c(a=15,b=5))) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey }% use one of RShowDoc("KEYWORDS") survey/man/xdesign.Rd0000644000176200001440000000666614146565107014341 0ustar liggesusers\name{xdesign} \alias{xdesign} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Crossed effects and other sparse correlations } \description{ Defines a design object with multiple dimensions of correlation: observations that share any of the \code{id} variables are correlated, or you can supply an adjacency matrix or Matrix to specify which are correlated. Supports crossed designs (eg multiple raters of multiple objects) and non-nested observational correlation (eg observations sharing primary school or secondary school). Has methods for \code{svymean}, \code{svytotal}, \code{svyglm} (so far). } \usage{ xdesign(id = NULL, strata = NULL, weights = NULL, data, fpc = NULL, adjacency = NULL, overlap = c("unbiased", "positive"), allow.non.binary = FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{id}{ list of formulas specifying cluster identifiers for each clustering dimension (or \code{NULL}) } \item{strata}{ Not implemented } \item{weights}{ model formula specifying (sampling) weights } \item{data}{ data frame containing all the variables } \item{fpc}{ Not implemented } \item{adjacency}{ Adjacency matrix or Matrix indicating which pairs of observations are correlated } \item{overlap}{ See details below } \item{allow.non.binary}{ If \code{FALSE} check that \code{adjacency} is a binary 0/1 or \code{TRUE}/\code{FALSE} matrix or Matrix. } } \details{ Subsetting for these objects actually drops observations; it is not equivalent to just setting weights to zero as for survey designs. So, for example, a subset of a balanced design will not be a balanced design. The \code{overlap} option controls double-counting of some variance terms. Suppose there are two clustering dimensions, \code{~a} and \code{~b}. If we compute variance matrices clustered on \code{a} and clustered on \code{b} and add them, observations that share both \code{a} and \code{b} will be counted twice, giving a positively biased estimator. We can subtract off a variance matrix clustered on combinations of \code{a} and \code{b} to give an unbiased variance estimator. However, the unbiased estimator is not guaranteed to be positive definite. In the references, Miglioretti and Heagerty use the \code{overlap="positive"} estimator and Cameron et al use the \code{overlap="unbiased"} estimator. } \value{ An object of class \code{xdesign} } \references{ Miglioretti D, Heagerty PJ (2007) Marginal modeling of nonnested multilevel data using standard software. Am J Epidemiol 165(4):453-63 Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2011). Robust Inference With Multiway Clustering. Journal of Business & Economic Statistics, 29(2), 238-249. \url{https://notstatschat.rbind.io/2021/09/18/crossed-clustering-and-parallel-invention/} } \seealso{\code{\link{salamander}}} %% ~Make other sections like Warning with \section{Warning }{....} ~ \examples{ ## With one clustering dimension, is close to the with-replacement ## survey estimator, but not identical unless clusters are equal size data(api) dclus1r<-svydesign(id=~dnum, weights=~pw, data=apiclus1) xclus1<-xdesign(id=list(~dnum), weights=~pw, data=apiclus1) xclus1 svymean(~enroll,dclus1r) svymean(~enroll,xclus1) data(salamander) xsalamander<-xdesign(id=list(~Male, ~Female), data=salamander, overlap="unbiased") xsalamander degf(xsalamander) }survey/man/mu284.Rd0000644000176200001440000000170613517345016013541 0ustar liggesusers\name{mu284} \alias{mu284} \docType{data} \title{Two-stage sample from MU284} \description{ The MU284 population comes from Sarndal et al, and the complete data are available from Statlib. These data are a two-stage sample from the population, analyzed on page 143 of the book. } \usage{data(mu284)} \format{ A data frame with 15 observations on the following 5 variables. \describe{ \item{\code{id1}}{identifier for PSU} \item{\code{n1}}{number of PSUs in population} \item{\code{id2}}{identifier for second-stage unit} \item{\code{y1}}{variable to be analysed} \item{\code{n2}}{number of second-stage units in this PSU} } } \source{ Carl Erik Sarndal, Bengt Swensson, Jan Wretman. (1991) "Model Assisted Survey Sampling" Springer. (downloaded from StatLib, which is no longer active) } \examples{ data(mu284) (dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284)) (ytotal<-svytotal(~y1, dmu284)) vcov(ytotal) } \keyword{datasets} survey/man/svyby.Rd0000755000176200001440000002172514360203326014036 0ustar liggesusers\name{svyby} \alias{svyby} \alias{svybys} \alias{svyby.default} \alias{SE.svyby} \alias{deff.svyby} \alias{coef.svyby} \alias{confint.svyby} \alias{unwtd.count} \alias{svyby.survey.design2} %- Also NEED an '\alias' for EACH other topic documented here. \title{Survey statistics on subsets} \description{ Compute survey statistics on subsets of a survey defined by factors. } \usage{ svyby(formula, by ,design,...) \method{svyby}{default}(formula, by, design, FUN, ..., deff=FALSE,keep.var = TRUE, keep.names = TRUE,verbose=FALSE, vartype=c("se","ci","ci","cv","cvpct","var"), drop.empty.groups=TRUE, covmat=FALSE, return.replicates=FALSE, na.rm.by=FALSE, na.rm.all=FALSE, stringsAsFactors=TRUE, multicore=getOption("survey.multicore")) \method{svyby}{survey.design2}(formula, by, design, FUN, ..., deff=FALSE,keep.var = TRUE, keep.names = TRUE,verbose=FALSE, vartype=c("se","ci","ci","cv","cvpct","var"), drop.empty.groups=TRUE, covmat=FALSE, influence=covmat, na.rm.by=FALSE, na.rm.all=FALSE, stringsAsFactors=TRUE, multicore=getOption("survey.multicore")) \method{SE}{svyby}(object,...) \method{deff}{svyby}(object,...) \method{coef}{svyby}(object,...) \method{confint}{svyby}(object, parm, level = 0.95,df =Inf,...) unwtd.count(x, design, ...) svybys(formula, bys, design, FUN, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula,x}{A formula specifying the variables to pass to \code{FUN} (or a matrix, data frame, or vector)} \item{by}{A formula specifying factors that define subsets, or a list of factors.} \item{design}{A \code{svydesign} or \code{svrepdesign} object} \item{FUN}{A function taking a formula and survey design object as its first two arguments.} \item{\dots}{Other arguments to \code{FUN}. NOTE: if any of the names of these are partial matches to \code{formula},\code{by}, or \code{design}, you must specify the \code{formula},\code{by}, or \code{design} argument by name, not just by position. } \item{deff}{Request a design effect from \code{FUN}} \item{keep.var}{If \code{FUN} returns a \code{svystat} object, extract standard errors from it} \item{keep.names}{Define row names based on the subsets} \item{verbose}{If \code{TRUE}, print a label for each subset as it is processed.} \item{vartype}{Report variability as one or more of standard error, confidence interval, coefficient of variation, percent coefficient of variation, or variance} \item{drop.empty.groups}{If \code{FALSE}, report \code{NA} for empty groups, if \code{TRUE} drop them from the output} \item{na.rm.by}{If true, omit groups defined by \code{NA} values of the \code{by} variables}. \item{na.rm.all}{If true, check for groups with no non-missing observations for variables defined by \code{formula} and treat these groups as empty. Doesn't make much sense without \code{na.rm=TRUE}} \item{covmat}{If \code{TRUE}, compute covariances between estimates for different subsets. Allows \code{\link{svycontrast}} to be used on output. Requires that \code{FUN} supports either \code{return.replicates=TRUE} or \code{influence=TRUE}} \item{return.replicates}{Only for replicate-weight designs. If \code{TRUE}, return all the replicates as the "replicates" attribute of the result} \item{influence}{Return the influence functions of the result} \item{multicore}{Use \code{multicore} package to distribute subsets over multiple processors?} \item{stringsAsFactors}{Convert any string variables in \code{formula} to factors before calling \code{FUN}, so that the factor levels will be the same in all groups (See Note below). Potentially slow.} \item{parm}{a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.} \item{level}{the confidence level required.} \item{df}{degrees of freedom for t-distribution in confidence interval, use \code{degf(design)} for number of PSUs minus number of strata} \item{object}{An object of class \code{"svyby"}} \item{bys}{one-sided formula with each term specifying a grouping (rather than being combined to give a grouping } } \value{ An object of class \code{"svyby"}: a data frame showing the factors and the results of \code{FUN}. For \code{unwtd.count}, the unweighted number of non-missing observations in the data matrix specified by \code{x} for the design. } \details{ The variance type "ci" asks for confidence intervals, which are produced by \code{confint}. In some cases additional options to \code{FUN} will be needed to produce confidence intervals, for example, \code{svyquantile} needs \code{ci=TRUE} or \code{keep.var=FALSE}. \code{unwtd.count} is designed to be passed to \code{svyby} to report the number of non-missing observations in each subset. Observations with exactly zero weight will also be counted as missing, since that's how subsets are implemented for some designs. Parallel processing with \code{multicore=TRUE} is useful only for fairly large problems and on computers with sufficient memory. The \code{multicore} package is incompatible with some GUIs, although the Mac Aqua GUI appears to be safe. The variant \code{svybys} creates a separate table for each term in \code{bys} rather than creating a joint table. } \note{The function works by making a lot of calls of the form \code{FUN(formula, subset(design, by==i))}, where \code{formula} is re-evaluated in each subset, so it is unwise to use data-dependent terms in \code{formula}. In particular, \code{svyby(~factor(a), ~b, design=d, svymean)}, will create factor variables whose levels are only those values of \code{a} present in each subset. If \code{a} is a character variable then \code{svyby(~a, ~b, design=d, svymean)} creates factor variables implicitly and so has the same problem. Either use \code{\link{update.survey.design}} to add variables to the design object instead or specify the levels explicitly in the call to \code{factor}. The \code{stringsAsFactors=TRUE} option converts all character variables to factors, which can be slow, set it to \code{FALSE} if you have predefined factors where necessary. } \note{ Asking for a design effect (\code{deff=TRUE}) from a function that does not produce one will cause an error or incorrect formatting of the output. The same will occur with \code{keep.var=TRUE} if the function does not compute a standard error. } \seealso{\code{\link{svytable}} and \code{\link{ftable.svystat}} for contingency tables, \code{\link{ftable.svyby}} for pretty-printing of \code{svyby} } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svyby(~api99, ~stype, dclus1, svymean) svyby(~api99, ~stype, dclus1, svyquantile, quantiles=0.5,ci=TRUE,vartype="ci") ## without ci=TRUE svyquantile does not compute standard errors svyby(~api99, ~stype, dclus1, svyquantile, quantiles=0.5, keep.var=FALSE) svyby(~api99, list(school.type=apiclus1$stype), dclus1, svymean) svyby(~api99+api00, ~stype, dclus1, svymean, deff=TRUE,vartype="ci") svyby(~api99+api00, ~stype+sch.wide, dclus1, svymean, keep.var=FALSE) ## report raw number of observations svyby(~api99+api00, ~stype+sch.wide, dclus1, unwtd.count, keep.var=FALSE) rclus1<-as.svrepdesign(dclus1) svyby(~api99, ~stype, rclus1, svymean) svyby(~api99, ~stype, rclus1, svyquantile, quantiles=0.5) svyby(~api99, list(school.type=apiclus1$stype), rclus1, svymean, vartype="cv") svyby(~enroll,~stype, rclus1,svytotal, deff=TRUE) svyby(~api99+api00, ~stype+sch.wide, rclus1, svymean, keep.var=FALSE) ##report raw number of observations svyby(~api99+api00, ~stype+sch.wide, rclus1, unwtd.count, keep.var=FALSE) ## comparing subgroups using covmat=TRUE mns<-svyby(~api99, ~stype, rclus1, svymean,covmat=TRUE) vcov(mns) svycontrast(mns, c(E = 1, M = -1)) str(svyby(~api99, ~stype, rclus1, svymean,return.replicates=TRUE)) tots<-svyby(~enroll, ~stype, dclus1, svytotal,covmat=TRUE) vcov(tots) svycontrast(tots, quote(E/H)) ## comparing subgroups uses the delta method unless replicates are present meanlogs<-svyby(~log(enroll),~stype,svymean, design=rclus1,covmat=TRUE) svycontrast(meanlogs, quote(exp(E-H))) meanlogs<-svyby(~log(enroll),~stype,svymean, design=rclus1,covmat=TRUE,return.replicates=TRUE) svycontrast(meanlogs, quote(exp(E-H))) ## extractor functions (a<-svyby(~enroll, ~stype, rclus1, svytotal, deff=TRUE, verbose=TRUE, vartype=c("se","cv","cvpct","var"))) deff(a) SE(a) cv(a) coef(a) confint(a, df=degf(rclus1)) ## ratio estimates svyby(~api.stu, by=~stype, denominator=~enroll, design=dclus1, svyratio) ratios<-svyby(~api.stu, by=~stype, denominator=~enroll, design=dclus1, svyratio,covmat=TRUE) vcov(ratios) ## empty groups svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean) svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean,drop.empty.groups=FALSE) ## Multiple tables svybys(~api00,~comp.imp+sch.wide,design=dclus1,svymean) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/svypredmeans.Rd0000644000176200001440000000577013743475476015426 0ustar liggesusers\name{svypredmeans} \alias{svypredmeans} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Predictive marginal means } \description{ Predictive marginal means for a generalised linear model, using the method of Korn and Graubard (1999) and matching the results of SUDAAN. The predictive marginal mean for one level of a factor is the probability-weighted average of the fitted values for the model on new data where all the observations are set to that level of the factor but have whatever values of adjustment variables they really have. } \usage{ svypredmeans(adjustmodel, groupfactor, predictat=NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{adjustmodel}{ A generalised linear model fit by \code{\link{svyglm}} with the adjustment variable but without the factor for which predictive means are wanted } \item{groupfactor}{ A one-sided formula specifying the factor for which predictive means are wanted. Can use, eg, \code{~interaction(race,sex)} for combining variables. This does not have to be a factor, but it will be modelled linearly if it isn't} \item{predictat}{A vector of the values of \code{groupfactor} where you want predictions. If \code{groupfactor} is a factor, these must be values in the data, but if it is numeric you can interpolate/extrapolate} } \value{ An object of class \code{svystat} with the predictive marginal means and their covariance matrix. } \references{ Graubard B, Korn E (1999) "Predictive Margins with Survey Data" Biometrics 55:652-659 Bieler, Brown, Williams, & Brogan (2010) "Estimating Model-Adjusted Risks, Risk Differences, and Risk Ratios From Complex Survey Data" Am J Epi DOI: 10.1093/aje/kwp440} \note{ It is possible to supply an adjustment model with only an intercept, but the results are then the same as \code{\link{svymean}} It makes no sense to have a variable in the adjustment model that is part of the grouping factor, and will give an error message or \code{NA}. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{svyglm}} Worked example using National Health Interview Survey data: \url{https://gist.github.com/tslumley/2e74cd0ac12a671d2724} } \examples{ data(nhanes) nhanes_design <- svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, nest=TRUE,data=nhanes) agesexmodel<-svyglm(HI_CHOL~agecat+RIAGENDR, design=nhanes_design,family=quasibinomial) ## high cholesterol by race/ethnicity, adjusted for demographic differences means<-svypredmeans(agesexmodel, ~factor(race)) means ## relative risks compared to non-Hispanic white svycontrast(means,quote(`1`/`2`)) svycontrast(means,quote(`3`/`2`)) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) demog_model <- svyglm(api00~mobility+ell+hsg+meals, design=dstrat) svypredmeans(demog_model,~enroll, predictat=c(100,300,1000,3000)) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{regression}% __ONLY ONE__ keyword per line survey/man/fpc.Rd0000755000176200001440000000306014423654147013435 0ustar liggesusers\name{fpc} \alias{fpc} \non_function{} \title{Small survey example} \usage{data(fpc)} \description{ The \code{fpc} data frame has 8 rows and 6 columns. It is artificial data to illustrate survey sampling estimators. } \format{ This data frame contains the following columns: \describe{ \item{stratid}{Stratum ids} \item{psuid}{Sampling unit ids} \item{weight}{Sampling weights} \item{nh}{number sampled per stratum} \item{Nh}{population size per stratum} \item{x}{data} } } \source{ \verb{https://www.stata-press.com/data/r7/fpc.dta} } \examples{ data(fpc) fpc withoutfpc<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, variables=~x, data=fpc, nest=TRUE) withoutfpc svymean(~x, withoutfpc) withfpc<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, fpc=~Nh, variables=~x, data=fpc, nest=TRUE) withfpc svymean(~x, withfpc) ## Other equivalent forms withfpc<-svydesign(prob=~I(1/weight), ids=~psuid, strata=~stratid, fpc=~Nh, variables=~x, data=fpc, nest=TRUE) svymean(~x, withfpc) withfpc<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, fpc=~I(nh/Nh), variables=~x, data=fpc, nest=TRUE) svymean(~x, withfpc) withfpc<-svydesign(weights=~weight, ids=~interaction(stratid,psuid), strata=~stratid, fpc=~I(nh/Nh), variables=~x, data=fpc) svymean(~x, withfpc) withfpc<-svydesign(ids=~psuid, strata=~stratid, fpc=~Nh, variables=~x,data=fpc,nest=TRUE) svymean(~x, withfpc) withfpc<-svydesign(ids=~psuid, strata=~stratid, fpc=~I(nh/Nh), variables=~x, data=fpc, nest=TRUE) svymean(~x, withfpc) } \keyword{datasets} survey/man/surveysummary.Rd0000755000176200001440000002113414227413234015632 0ustar liggesusers\name{surveysummary} \alias{svymean} \alias{svymean.survey.design} \alias{svymean.survey.design2} \alias{svymean.svyrep.design} \alias{svymean.twophase} \alias{svytotal} \alias{svytotal.twophase} \alias{svytotal.survey.design} \alias{svytotal.survey.design2} \alias{svytotal.svyrep.design} \alias{svyvar} \alias{svyvar.survey.design} \alias{svyvar.svyrep.design} \alias{coef.svystat} \alias{vcov.svystat} \alias{coef.svrepstat} \alias{vcov.svrepstat} \alias{cv.svyratio} \alias{cv.svrepratio} \alias{cv.svrepstat} \alias{cv.svystat} \alias{cv.default} \alias{cv} \alias{deff} \alias{deff.default} \alias{confint.svystat} \alias{confint.svrepstat} \alias{make.formula} %- Also NEED an `\alias' for EACH other topic documented here. \title{Summary statistics for sample surveys} \description{ Compute means, variances, ratios and totals for data from complex surveys. } \usage{ \method{svymean}{survey.design}(x, design, na.rm=FALSE,deff=FALSE,influence=FALSE,...) \method{svymean}{survey.design2}(x, design, na.rm=FALSE,deff=FALSE,influence=FALSE,...) \method{svymean}{twophase}(x, design, na.rm=FALSE,deff=FALSE,...) \method{svymean}{svyrep.design}(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE,...) \method{svyvar}{survey.design}(x, design, na.rm=FALSE,...) \method{svyvar}{svyrep.design}(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE,...,estimate.only=FALSE) \method{svytotal}{survey.design}(x, design, na.rm=FALSE,deff=FALSE,influence=FALSE,...) \method{svytotal}{survey.design2}(x, design, na.rm=FALSE,deff=FALSE,influence=FALSE,...) \method{svytotal}{twophase}(x, design, na.rm=FALSE,deff=FALSE,...) \method{svytotal}{svyrep.design}(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE,...) \method{coef}{svystat}(object,...) \method{coef}{svrepstat}(object,...) \method{vcov}{svystat}(object,...) \method{vcov}{svrepstat}(object,...) \method{confint}{svystat}(object, parm, level = 0.95,df =Inf,...) \method{confint}{svrepstat}(object, parm, level = 0.95,df =Inf,...) cv(object,...) deff(object, quietly=FALSE,...) make.formula(names) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{A formula, vector or matrix} \item{design}{\code{survey.design} or \code{svyrep.design} object} \item{na.rm}{Should cases with missing values be dropped?} \item{influence}{Should a matrix of influence functions be returned (primarily to support \code{\link{svyby}})} \item{rho}{parameter for Fay's variance estimator in a BRR design} \item{return.replicates}{Return the replicate means/totals?} \item{deff}{Return the design effect (see below)} \item{object}{The result of one of the other survey summary functions} \item{quietly}{Don't warn when there is no design effect computed} \item{estimate.only}{Don't compute standard errors (useful when \code{svyvar} is used to estimate the design effect)} \item{parm}{a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.} \item{level}{the confidence level required.} \item{df}{degrees of freedom for t-distribution in confidence interval, use \code{degf(design)} for number of PSUs minus number of strata} \item{...}{additional arguments to methods,not currently used} \item{names}{vector of character strings} } \details{ These functions perform weighted estimation, with each observation being weighted by the inverse of its sampling probability. Except for the table functions, these also give precision estimates that incorporate the effects of stratification and clustering. Factor variables are converted to sets of indicator variables for each category in computing means and totals. Combining this with the \code{\link{interaction}} function, allows crosstabulations. See \code{\link{ftable.svystat}} for formatting the output. With \code{na.rm=TRUE}, all cases with missing data are removed. With \code{na.rm=FALSE} cases with missing data are not removed and so will produce missing results. When using replicate weights and \code{na.rm=FALSE} it may be useful to set \code{options(na.action="na.pass")}, otherwise all replicates with any missing results will be discarded. The \code{svytotal} and \code{svreptotal} functions estimate a population total. Use \code{predict} on \code{\link{svyratio}} and \code{\link{svyglm}}, to get ratio or regression estimates of totals. \code{svyvar} estimates the population variance. The object returned includes the full matrix of estimated population variances and covariances, but by default only the diagonal elements are printed. To display the whole matrix use \code{as.matrix(v)} or \code{print(v, covariance=TRUE)}. The design effect compares the variance of a mean or total to the variance from a study of the same size using simple random sampling without replacement. Note that the design effect will be incorrect if the weights have been rescaled so that they are not reciprocals of sampling probabilities. To obtain an estimate of the design effect comparing to simple random sampling with replacement, which does not have this requirement, use \code{deff="replace"}. This with-replacement design effect is the square of Kish's "deft". The design effect for a subset of a design conditions on the size of the subset. That is, it compares the variance of the estimate to the variance of an estimate based on a simple random sample of the same size as the subset, taken from the subpopulation. So, for example, under stratified random sampling the design effect in a subset consisting of a single stratum will be 1.0. The \code{cv} function computes the coefficient of variation of a statistic such as ratio, mean or total. The default method is for any object with methods for \code{\link{SE}} and \code{coef}. \code{make.formula} makes a formula from a vector of names. This is useful because formulas as the best way to specify variables to the survey functions. } \value{ Objects of class \code{"svystat"} or \code{"svrepstat"}, which are vectors with a \code{"var"} attribute giving the variance and a \code{"statistic"} attribute giving the name of the statistic. These objects have methods for \code{vcov}, \code{SE}, \code{coef}, \code{confint}, \code{svycontrast}. } \author{Thomas Lumley} \seealso{ \code{\link{svydesign}}, \code{\link{as.svrepdesign}}, \code{\link{svrepdesign}} for constructing design objects. \code{\link{degf}} to extract degrees of freedom from a design. \code{\link{svyquantile}} for quantiles \code{\link{ftable.svystat}} for more attractive tables \code{\link{svyciprop}} for more accurate confidence intervals for proportions near 0 or 1. \code{\link{svyttest}} for comparing two means. \code{\link{svycontrast}} for linear and nonlinear functions of estimates. } \examples{ data(api) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svymean(~api00, dclus1, deff=TRUE) svymean(~factor(stype),dclus1) svymean(~interaction(stype, comp.imp), dclus1) svyquantile(~api00, dclus1, c(.25,.5,.75)) svytotal(~enroll, dclus1, deff=TRUE) svyratio(~api.stu, ~enroll, dclus1) v<-svyvar(~api00+api99, dclus1) v print(v, cov=TRUE) as.matrix(v) # replicate weights - jackknife (this is slower) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) jkstrat<-as.svrepdesign(dstrat) svymean(~api00, jkstrat) svymean(~factor(stype),jkstrat) svyvar(~api00+api99,jkstrat) svyquantile(~api00, jkstrat, c(.25,.5,.75)) svytotal(~enroll, jkstrat) svyratio(~api.stu, ~enroll, jkstrat) # coefficients of variation cv(svytotal(~enroll,dstrat)) cv(svyratio(~api.stu, ~enroll, jkstrat)) # extracting information from the results coef(svytotal(~enroll,dstrat)) vcov(svymean(~api00+api99,jkstrat)) SE(svymean(~enroll, dstrat)) confint(svymean(~api00+api00, dclus1)) confint(svymean(~api00+api00, dclus1), df=degf(dclus1)) # Design effect svymean(~api00, dstrat, deff=TRUE) svymean(~api00, dstrat, deff="replace") svymean(~api00, jkstrat, deff=TRUE) svymean(~api00, jkstrat, deff="replace") (a<-svytotal(~enroll, dclus1, deff=TRUE)) deff(a) ## weights that are *already* calibrated to population size sum(weights(dclus1)) nrow(apipop) cdclus1<- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc,calibrate.formula=~1) SE(svymean(~enroll, dclus1)) ## not equal to SE(mean) SE(svytotal(~enroll, dclus1))/nrow(apipop) ## equal to SE(mean) SE(svytotal(~enroll, cdclus1))/nrow(apipop) } \keyword{univar}% at least one, from doc/KEYWORDS \keyword{survey}% __ONLY ONE__ keyword per line survey/man/svysurvreg.Rd0000644000176200001440000000352113517345016015116 0ustar liggesusers\name{svysurvreg} \alias{svysurvreg} \alias{svysurvreg.survey.design} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Fit accelerated failure models to survey data } \description{ This function calls \code{survreg} from the 'survival' package to fit accelerated failure (accelerated life) models to complex survey data, and then computes correct standard errors by linearisation. It has the same arguments as \code{survreg}, except that the second argument is \code{design} rather than \code{data}. } \usage{ \method{svysurvreg}{survey.design}(formula, design, weights=NULL, subset=NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ Model formula } \item{design}{ Survey design object, including two-phase designs } \item{weights}{ Additional weights to multiply by the sampling weights. No, I don't know why you'd want to do that. } \item{subset}{ subset to use in fitting (if needed) } \item{\dots}{ Other arguments of \code{survreg} } } \value{ Object of class \code{svysurvreg}, with the same structure as a \code{survreg} object but with \code{NA} for the loglikelihood. } \note{ The \code{residuals} method is identical to that for \code{survreg} objects except the \code{weighted} option defaults to \code{TRUE} } \examples{ data(pbc, package="survival") pbc$randomized <- with(pbc, !is.na(trt) & trt>0) biasmodel<-glm(randomized~age*edema,data=pbc) pbc$randprob<-fitted(biasmodel) dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized)) model <- svysurvreg(Surv(time, status>0)~bili+protime+albumin, design=dpbc, dist="weibull") summary(model) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey}% use one of RShowDoc("KEYWORDS") \keyword{survival}% __ONLY ONE__ keyword per line survey/man/svrVar.Rd0000755000176200001440000000246613517345016014154 0ustar liggesusers\name{svrVar} \alias{svrVar} %- Also NEED an `\alias' for EACH other topic documented here. \title{Compute variance from replicates } \description{ Compute an appropriately scaled empirical variance estimate from replicates. The \code{mse} argument specifies whether the sums of squares should be centered at the point estimate (\code{mse=TRUE}) or the mean of the replicates. It is usually taken from the \code{mse} component of the design object. } \usage{ svrVar(thetas, scale, rscales, na.action=getOption("na.action"), mse=getOption("survey.replicates.mse"),coef) } %- maybe also `usage' for other objects documented here. \arguments{ \item{thetas}{matrix whose rows are replicates (or a vector of replicates)} \item{scale}{Overall scaling factor} \item{rscales}{Scaling factor for each squared deviation } \item{na.action}{How to handle replicates where the statistic could not be estimated} \item{mse}{if \code{TRUE}, center at the point estimated, if \code{FALSE} center at the mean of the replicates} \item{coef}{The point estimate, required only if \code{mse==TRUE}} } \value{ covariance matrix. } \seealso{\code{\link{svrepdesign}}, \code{\link{as.svrepdesign}}, \code{\link{brrweights}}, \code{\link{jk1weights}}, \code{\link{jknweights}}} \keyword{survey}% at least one, from doc/KEYWORDS survey/man/estweights.Rd0000644000176200001440000000716114423551650015051 0ustar liggesusers\name{estweights} \alias{estWeights} \alias{estWeights.twophase} \alias{estWeights.data.frame} %- Also NEED an '\alias' for EACH other topic documented here. \title{Estimated weights for missing data} \description{ Creates or adjusts a two-phase survey design object using a logistic regression model for second-phase sampling probability. This function should be particularly useful in reweighting to account for missing data. } \usage{ estWeights(data,formula,...) \method{estWeights}{twophase}(data,formula=NULL, working.model=NULL,...) \method{estWeights}{data.frame}(data,formula=NULL, working.model=NULL, subset=NULL, strata=NULL,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{data}{twophase design object or data frame} \item{formula}{Predictors for estimating weights} \item{working.model}{Model fitted to complete (ie phase 1) data} \item{subset}{Subset of data frame with complete data (ie phase 1). If \code{NULL} use all complete cases} \item{strata}{Stratification (if any) of phase 2 sampling} \item{\dots}{for future expansion} } \details{ If \code{data} is a data frame, \code{estWeights} first creates a two-phase design object. The \code{strata} argument is used only to compute finite population corrections, the same variables must be included in \code{formula} to compute stratified sampling probabilities. With a two-phase design object, \code{estWeights} estimates the sampling probabilities using logistic regression as described by Robins et al (1994) and adds information to the object to enable correct sandwich standard errors to be computed. An alternative to specifying \code{formula} is to specify \code{working.model}. The estimating functions from this model will be used as predictors of the sampling probabilities, which will increase efficiency to the extent that the working model and the model of interest estimate the same parameters (Kulich & Lin 2004). The effect on a two-phase design object is very similar to \code{\link{calibrate}}, and is identical when \code{formula} specifies a saturated model. } \value{ A two-phase survey design object. } \references{ Breslow NE, Lumley T, Ballantyne CM, Chambless LE, Kulich M. (2009) Using the Whole Cohort in the Analysis of Case-Cohort Data. Am J Epidemiol. 2009 Jun 1;169(11):1398-405. Robins JM, Rotnitzky A, Zhao LP. (1994) Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89, 846-866. Kulich M, Lin DY (2004). Improving the Efficiency of Relative-Risk Estimation in Case-Cohort Studies. Journal of the American Statistical Association, Vol. 99, pp.832-844 Lumley T, Shaw PA, Dai JY (2011) "Connections between survey calibration estimators and semiparametric models for incomplete data" International Statistical Review. 79:200-220. (with discussion 79:221-232) } \seealso{ \code{\link{postStratify}}, \code{\link{calibrate}}, \code{\link{twophase}}} \examples{ data(airquality) ## ignoring missingness, using model-based standard error summary(lm(log(Ozone)~Temp+Wind, data=airquality)) ## Without covariates to predict missingness we get ## same point estimates, but different (sandwich) standard errors daq<-estWeights(airquality, formula=~1,subset=~I(!is.na(Ozone))) summary(svyglm(log(Ozone)~Temp+Wind,design=daq)) ## Reweighting based on weather, month d2aq<-estWeights(airquality, formula=~Temp+Wind+Month, subset=~I(!is.na(Ozone))) summary(svyglm(log(Ozone)~Temp+Wind,design=d2aq)) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/as.svydesign2.Rd0000755000176200001440000000202213517345016015354 0ustar liggesusers\name{as.svydesign2} \alias{as.svydesign2} \alias{.svycheck} %- Also NEED an '\alias' for EACH other topic documented here. \title{Update to the new survey design format} \description{ The structure of survey design objects changed in version 2.9, to allow standard errors based on multistage sampling. \code{as.svydesign} converts an object to the new structure and \code{.svycheck} warns if an object does not have the new structure. You can set \code{options(survey.want.obsolete=TRUE)} to suppress the warnings produced by \code{.svycheck} and \code{options(survey.ultimate.cluster=TRUE)} to always compute variances based on just the first stage of sampling. } \usage{ as.svydesign2(object) .svycheck(object) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{produced by \code{svydesign}} } \value{ Object of class \code{survey.design2} } \seealso{\code{\link{svydesign}}, \code{\link{svyrecvar}}} \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/open.DBIsvydesign.Rd0000644000176200001440000000252613517650711016156 0ustar liggesusers\name{open.DBIsvydesign} \alias{open.DBIsvydesign} \alias{close.DBIsvydesign} %- Also NEED an '\alias' for EACH other topic documented here. \title{Open and close DBI connections } \description{ A database-backed survey design object contains a connection to a database. This connection will be broken if the object is saved and reloaded, and the connection should ideally be closed with \code{close} before quitting R (although it doesn't matter for SQLite connections). The connection can be reopened with \code{open}. } \usage{ \method{open}{DBIsvydesign}(con, ...) \method{close}{DBIsvydesign}(con, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{con}{Object of class \code{DBIsvydesign} } \item{\dots}{Other options, to be passed to \code{dbConnect} or \code{dbDisconnect}.} } \value{ The same survey design object with the connection opened or closed. } \seealso{\code{\link{svydesign}} DBI package } \examples{ \dontrun{ library(RSQLite) dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey")) dbclus1 close(dbclus1) dbclus1 try(svymean(~api00, dbclus1)) dbclus1<-open(dbclus1) open(dbclus1) svymean(~api00, dbclus1) } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/svycoxph.Rd0000755000176200001440000001172314141100455014536 0ustar liggesusers\name{svycoxph} \alias{svycoxph} \alias{svycoxph.survey.design2} \alias{svycoxph.survey.design} \alias{svycoxph.svyrep.design} \alias{predict.svycoxph} %\alias{print.svycoxph} %\alias{model.frame.svycoxph} %\alias{summary.svycoxph} %\alias{anova.svycoxph} \alias{extractAIC.svycoxph} \alias{AIC.svycoxph} %\alias{survfit.svycoxph} %- Also NEED an `\alias' for EACH other topic documented here. \title{Survey-weighted Cox models.} \description{ Fit a proportional hazards model to data from a complex survey design. } \usage{ svycoxph(formula, design,subset=NULL, rescale=TRUE, ...) \method{predict}{svycoxph}(object, newdata, se=FALSE, type=c("lp", "risk", "terms","curve"),...) \method{AIC}{svycoxph}(object, ..., k = 2) } %- maybe also `usage' for other objects documented here. \arguments{ \item{formula}{Model formula. Any \code{cluster()} terms will be ignored.} \item{design}{ \code{survey.design} object. Must contain all variables in the formula} \item{subset}{Expression to select a subpopulation} \item{rescale}{Rescale weights to improve numerical stability} \item{object}{A \code{svycoxph} object} \item{newdata}{New data for prediction} \item{se}{Compute standard errors? This takes a lot of memory for \code{type="curve"}} \item{type}{"curve" does predicted survival curves. The other values are passed to \code{predict.coxph()}} \item{\dots}{For \code{AIC}, more models to compare the AIC of. For \code{svycoxph}, other arguments passed to \code{coxph}. } \item{k}{The penalty per parameter that would be used under independent sampling: AIC has \code{k=2}} } \details{ The main difference between \code{svycoxph} function and the \code{robust=TRUE} option to \code{\link{coxph}} in the survival package is that this function accounts for the reduction in variance from stratified sampling and the increase in variance from having only a small number of clusters. Note that \code{strata} terms in the model formula describe subsets that have a separate baseline hazard function and need not have anything to do with the stratification of the sampling. The \code{AIC} method uses the same approach as \code{\link{AIC.svyglm}}, though the relevance of the criterion this optimises is a bit less clear than for generalised linear models. The standard errors for predicted survival curves are available only by linearization, not by replicate weights (at the moment). Use \code{\link{withReplicates}} to get standard errors with replicate weights. Predicted survival curves are not available for stratified Cox models. The standard errors use the delta-method approach of Williams (1995) for the Nelson-Aalen estimator, modified to handle the Cox model following Tsiatis (1981). The standard errors agree closely with \code{survfit.coxph} for independent sampling when the model fits well, but are larger when the model fits poorly. I believe the standard errors are equivalent to those of Lin (2000), but I don't know of any implementation that would allow a check. } \value{ An object of class \code{svycoxph} for \code{svycoxph}, an object of class \code{svykm} or \code{svykmlist} for \code{predict(,type="curve")}. } \section{Warning}{ The standard error calculation for survival curves uses memory proportional to the sample size times the square of the number of events. } \author{Thomas Lumley} \references{Binder DA. (1992) Fitting Cox's proportional hazards models from survey data. Biometrika 79: 139-147 Lin D-Y (2000) On fitting Cox's proportional hazards model to survey data. Biometrika 87: 37-47 Tsiatis AA (1981) A Large Sample Study of Cox's Regression Model. Annals of Statistics 9(1) 93-108 Williams RL (1995) "Product-Limit Survival Functions with Correlated Survival Times" Lifetime Data Analysis 1: 171--186 } \seealso{ \code{\link{coxph}}, \code{\link{predict.coxph}} \code{\link{svykm}} for estimation of Kaplan-Meier survival curves and for methods that operate on survival curves. \code{\link{regTermTest}} for Wald and (Rao-Scott) likelihood ratio tests for one or more parameters. } \examples{ ## Somewhat unrealistic example of nonresponse bias. data(pbc, package="survival") pbc$randomized<-with(pbc, !is.na(trt) & trt>0) biasmodel<-glm(randomized~age*edema,data=pbc,family=binomial) pbc$randprob<-fitted(biasmodel) if (is.null(pbc$albumin)) pbc$albumin<-pbc$alb ##pre2.9.0 dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized)) rpbc<-as.svrepdesign(dpbc) (model<-svycoxph(Surv(time,status>0)~log(bili)+protime+albumin,design=dpbc)) svycoxph(Surv(time,status>0)~log(bili)+protime+albumin,design=rpbc) s<-predict(model,se=TRUE, type="curve", newdata=data.frame(bili=c(3,9), protime=c(10,10), albumin=c(3.5,3.5))) plot(s[[1]],ci=TRUE,col="sienna") lines(s[[2]], ci=TRUE,col="royalblue") quantile(s[[1]], ci=TRUE) confint(s[[2]], parm=365*(1:5)) } \keyword{regression}% at least one, from doc/KEYWORDS \keyword{survival}% at least one, from doc/KEYWORDS \keyword{survey}% at least one, from doc/KEYWORDS survey/man/api.Rd0000755000176200001440000001110514413607127013430 0ustar liggesusers\name{api} \alias{api} \alias{apipop} \alias{apiclus1} \alias{apiclus2} \alias{apistrat} \alias{apisrs} \docType{data} \title{Student performance in California schools} \description{ The Academic Performance Index is computed for all California schools based on standardised testing of students. The data sets contain information for all schools with at least 100 students and for various probability samples of the data. } \usage{ data(api) } \format{ The full population data in \code{apipop} are a data frame with 6194 observations on the following 37 variables. \describe{ \item{cds}{Unique identifier} \item{stype}{Elementary/Middle/High School} \item{name}{School name (15 characters)} \item{sname}{School name (40 characters)} \item{snum}{School number} \item{dname}{District name} \item{dnum}{District number} \item{cname}{County name} \item{cnum}{County number} \item{flag}{reason for missing data} \item{pcttest}{percentage of students tested} \item{api00}{API in 2000} \item{api99}{API in 1999} \item{target}{target for change in API} \item{growth}{Change in API} \item{sch.wide}{Met school-wide growth target?} \item{comp.imp}{Met Comparable Improvement target} \item{both}{Met both targets} \item{awards}{Eligible for awards program} \item{meals}{Percentage of students eligible for subsidized meals} \item{ell}{`English Language Learners' (percent)} \item{yr.rnd}{Year-round school} \item{mobility}{percentage of students for whom this is the first year at the school} \item{acs.k3}{average class size years K-3} \item{acs.46}{average class size years 4-6} \item{acs.core}{Number of core academic courses} \item{pct.resp}{percent where parental education level is known} \item{not.hsg}{percent parents not high-school graduates} \item{hsg}{percent parents who are high-school graduates} \item{some.col}{percent parents with some college} \item{col.grad}{percent parents with college degree} \item{grad.sch}{percent parents with postgraduate education} \item{avg.ed}{average parental education level} \item{full}{percent fully qualified teachers} \item{emer}{percent teachers with emergency qualifications} \item{enroll}{number of students enrolled} \item{api.stu}{number of students tested.} } The other data sets contain additional variables \code{pw} for sampling weights and \code{fpc} to compute finite population corrections to variance. } \details{ \code{apipop} is the entire population, \code{apisrs} is a simple random sample, \code{apiclus1} is a cluster sample of school districts, \code{apistrat} is a sample stratified by \code{stype}, and \code{apiclus2} is a two-stage cluster sample of schools within districts. The sampling weights in \code{apiclus1} are incorrect (the weight should be 757/15) but are as obtained from UCLA. } \source{ Data were obtained from the survey sampling help pages of UCLA Academic Technology Services; these pages are no longer on line. } \references{ The API program has been discontinued at the end of 2018, and the archive page at the California Department of Education is now gone. The Wikipedia article has links to past material at the Internet Archive. \url{https://en.wikipedia.org/wiki/Academic_Performance_Index_(California_public_schools)} } \examples{ library(survey) data(api) mean(apipop$api00) sum(apipop$enroll, na.rm=TRUE) #stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) summary(dstrat) svymean(~api00, dstrat) svytotal(~enroll, dstrat, na.rm=TRUE) # one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) summary(dclus1) svymean(~api00, dclus1) svytotal(~enroll, dclus1, na.rm=TRUE) # two-stage cluster sample dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) summary(dclus2) svymean(~api00, dclus2) svytotal(~enroll, dclus2, na.rm=TRUE) # two-stage `with replacement' dclus2wr<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) summary(dclus2wr) svymean(~api00, dclus2wr) svytotal(~enroll, dclus2wr, na.rm=TRUE) # convert to replicate weights rclus1<-as.svrepdesign(dclus1) summary(rclus1) svymean(~api00, rclus1) svytotal(~enroll, rclus1, na.rm=TRUE) # post-stratify on school type pop.types<-xtabs(~stype, data=apipop) rclus1p<-postStratify(rclus1, ~stype, pop.types) dclus1p<-postStratify(dclus1, ~stype, pop.types) summary(dclus1p) summary(rclus1p) svymean(~api00, dclus1p) svytotal(~enroll, dclus1p, na.rm=TRUE) svymean(~api00, rclus1p) svytotal(~enroll, rclus1p, na.rm=TRUE) } \keyword{datasets} survey/man/svycoplot.Rd0000644000176200001440000000372013517345016014722 0ustar liggesusers\name{svycoplot} \alias{svycoplot} %- Also NEED an '\alias' for EACH other topic documented here. \title{Conditioning plots of survey data } \description{ Draws conditioned scatterplots ('Trellis' plots) of survey data using hexagonal binning or transparency. } \usage{ svycoplot(formula, design, style = c("hexbin", "transparent"), basecol = "black", alpha = c(0, 0.8),hexscale=c("relative","absolute"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{A graph formula suitable for \code{\link{xyplot}}} \item{design}{A survey design object } \item{style}{Hexagonal binning or transparent color?} \item{basecol}{The fully opaque 'base' color for creating transparent colors. This may also be a function; see \code{\link{svyplot}} for details} \item{alpha}{Minimum and maximum opacity } \item{hexscale}{Scale hexagons separate for each panel (relative) or across all panels (absolute)} \item{\dots}{Other arguments passed to \code{grid.hexagons} or \code{\link{xyplot}} } } \value{ An object of class \code{trellis} } \note{ As with all 'Trellis' graphs, this function creates an object but does not draw the graph. When used inside a function or non-interactively you need to \code{print()} the result to create the graph. } \seealso{\code{\link{svyplot}}} \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2, fpc=~fpc1+fpc2) svycoplot(api00~api99|sch.wide*comp.imp, design=dclus2, style="hexbin") svycoplot(api00~api99|sch.wide*comp.imp, design=dclus2, style="hexbin", hexscale="absolute") svycoplot(api00~api99|sch.wide, design=dclus2, style="trans") svycoplot(api00~meals|stype,design=dclus2, style="transparent", basecol=function(d) c("darkred","purple","forestgreen")[as.numeric(d$stype)], alpha=c(0,1)) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{hplot}% __ONLY ONE__ keyword per line survey/man/svycdf.Rd0000644000176200001440000000355513517345016014164 0ustar liggesusers\name{svycdf} \alias{svycdf} \alias{print.svycdf} \alias{plot.svycdf} %- Also NEED an '\alias' for EACH other topic documented here. \title{Cumulative Distribution Function} \description{ Estimates the population cumulative distribution function for specified variables. In contrast to \code{\link{svyquantile}}, this does not do any interpolation: the result is a right-continuous step function. } \usage{ svycdf(formula, design, na.rm = TRUE,...) \method{print}{svycdf}(x,...) \method{plot}{svycdf}(x,xlab=NULL,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{one-sided formula giving variables from the design object } \item{design}{survey design object } \item{na.rm}{remove missing data (case-wise deletion)?} \item{...}{other arguments to \code{\link{plot.stepfun}}} \item{x}{object of class \code{svycdf}} \item{xlab}{a vector of x-axis labels or \code{NULL} for the default labels} } \value{ An object of class \code{svycdf}, which is a list of step functions (of class \code{\link{stepfun}}) } \seealso{ \code{\link{svyquantile}}, \code{\link{svyhist}}, \code{\link{plot.stepfun}}} \examples{ data(api) dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) cdf.est<-svycdf(~enroll+api00+api99, dstrat) cdf.est ## function cdf.est[[1]] ## evaluate the function cdf.est[[1]](800) cdf.est[[2]](800) ## compare to population and sample CDFs. opar<-par(mfrow=c(2,1)) cdf.pop<-ecdf(apipop$enroll) cdf.samp<-ecdf(apistrat$enroll) plot(cdf.pop,main="Population vs sample", xlab="Enrollment") lines(cdf.samp,col.points="red") plot(cdf.pop, main="Population vs estimate", xlab="Enrollment") lines(cdf.est[[1]],col.points="red") par(opar) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{hplot}% __ONLY ONE__ keyword per line survey/man/myco.Rd0000644000176200001440000000361214413717610013626 0ustar liggesusers\name{myco} \alias{myco} \docType{data} \title{ Association between leprosy and BCG vaccination } \description{ These data are in a paper by JNK Rao and colleagues, on score tests for complex survey data. External information (not further specified) suggests the functional form for the \code{Age} variable. } \usage{data("myco")} \format{ A data frame with 516 observations on the following 6 variables. \describe{ \item{\code{Age}}{Age in years at the midpoint of six age strata} \item{\code{Scar}}{Presence of a BCG vaccination scar} \item{\code{n}}{Sampled number of cases (and thus controls) in the age stratum} \item{\code{Ncontrol}}{Number of non-cases in the population} \item{\code{wt}}{Sampling weight} \item{\code{leprosy}}{case status 0/1} } } \details{ The data are a simulated stratified case-control study drawn from a population study conducted in a region of Malawi (Clayton and Hills, 1993, Table 18.1). The goal was to examine whether BCG vaccination against tuberculosis protects against leprosy (the causative agents are both species of _Mycobacterium_). Rao et al have a typographical error: the number of non-cases in the population in the 25-30 age stratum is given as 4981 but 5981 matches both the computational output and the data as given by Clayton and Hills. } \source{ JNK Rao, AJ Scott, and Skinner, C. (1998). QUASI-SCORE TESTS WITH SURVEY DATA. Statistica Sinica, 8(4), 1059-1070. Clayton, D., & Hills, M. (1993). Statistical Models in Epidemiology. OUP } \examples{ data(myco) dmyco<-svydesign(id=~1, strata=~interaction(Age,leprosy),weights=~wt,data=myco) m_full<-svyglm(leprosy~I((Age+7.5)^-2)+Scar, family=quasibinomial, design=dmyco) m_age<-svyglm(leprosy~I((Age+7.5)^-2), family=quasibinomial, design=dmyco) anova(m_full,m_age) ## unweighted model does not match m_full glm(leprosy~I((Age+7.5)^-2)+Scar, family=binomial, data=myco) } \keyword{datasets} survey/man/svykm.Rd0000644000176200001440000000774413517345016014043 0ustar liggesusers\name{svykm} \alias{svykm} \alias{plot.svykm} \alias{plot.svykmlist} \alias{lines.svykm} \alias{quantile.svykm} \alias{confint.svykm} %- Also NEED an '\alias' for EACH other topic documented here. \title{Estimate survival function. } \description{ Estimates the survival function using a weighted Kaplan-Meier estimator. } \usage{ svykm(formula, design,se=FALSE, ...) \method{plot}{svykm}(x,xlab="time",ylab="Proportion surviving", ylim=c(0,1),ci=NULL,lty=1,...) \method{lines}{svykm}(x,xlab="time",type="s",ci=FALSE,lty=1,...) \method{plot}{svykmlist}(x, pars=NULL, ci=FALSE,...) \method{quantile}{svykm}(x, probs=c(0.75,0.5,0.25),ci=FALSE,level=0.95,...) \method{confint}{svykm}(object,parm,level=0.95,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{Two-sided formula. The response variable should be a right-censored \code{Surv} object} \item{design}{survey design object} \item{se}{Compute standard errors? This is slow for moderate to large data sets} \item{\dots}{in \code{plot} and \code{lines} methods, graphical parameters } \item{x}{a \code{svykm} or \code{svykmlist} object} \item{xlab,ylab,ylim,type}{as for \code{plot}} \item{lty}{Line type, see \code{\link{par}}} \item{ci}{Plot (or return, for\code{quantile}) the confidence interval} \item{pars}{A list of vectors of graphical parameters for the separate curves in a \code{svykmlist} object} \item{object}{A \code{svykm} object} \item{parm}{vector of times to report confidence intervals} \item{level}{confidence level} \item{probs}{survival probabilities for computing survival quantiles (note that these are the complement of the usual \code{\link{quantile}} input, so 0.9 means 90\% surviving, not 90\% dead)} } \value{ For \code{svykm}, an object of class \code{svykm} for a single curve or \code{svykmlist} for multiple curves. } \details{ When standard errors are computed, the survival curve is actually the Aalen (hazard-based) estimator rather than the Kaplan-Meier estimator. The standard error computations use memory proportional to the sample size times the square of the number of events. This can be a lot. In the case of equal-probability cluster sampling without replacement the computations are essentially the same as those of Williams (1995), and the same linearization strategy is used for other designs. Confidence intervals are computed on the log(survival) scale, following the default in \code{survival} package, which was based on simulations by Link(1984). Confidence intervals for quantiles use Woodruff's method: the interval is the intersection of the horizontal line at the specified quantile with the pointwise confidence band around the survival curve. } \references{ Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601-610. Williams RL (1995) "Product-Limit Survival Functions with Correlated Survival Times" Lifetime Data Analysis 1: 171--186 Woodruff RS (1952) Confidence intervals for medians and other position measures. JASA 57, 622-627. } \seealso{\code{\link{predict.svycoxph}} for survival curves from a Cox model } \examples{ data(pbc, package="survival") pbc$randomized <- with(pbc, !is.na(trt) & trt>0) biasmodel<-glm(randomized~age*edema,data=pbc) pbc$randprob<-fitted(biasmodel) dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized)) s1<-svykm(Surv(time,status>0)~1, design=dpbc) s2<-svykm(Surv(time,status>0)~I(bili>6), design=dpbc) plot(s1) plot(s2) plot(s2, lwd=2, pars=list(lty=c(1,2),col=c("purple","forestgreen"))) quantile(s1, probs=c(0.9,0.75,0.5,0.25,0.1)) s3<-svykm(Surv(time,status>0)~I(bili>6), design=dpbc,se=TRUE) plot(s3[[2]],col="purple") confint(s3[[2]], parm=365*(1:5)) quantile(s3[[1]], ci=TRUE) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{survival}% __ONLY ONE__ keyword per line survey/man/make.calfun.Rd0000644000176200001440000000375513517345016015054 0ustar liggesusers\name{make.calfun} \alias{make.calfun} \alias{cal.linear} \alias{cal.raking} \alias{cal.logit} \alias{cal.sinh} %- Also NEED an '\alias' for EACH other topic documented here. \title{Calibration metrics} \description{ Create calibration metric for use in \code{\link{calibrate}}. The function \code{F} is the link function described in section 2 of Deville et al. To create a new calibration metric, specify \eqn{F-1}{F-1} and its derivative. The package provides \code{cal.linear}, \code{cal.raking}, \code{cal.logit}, which are standard, and \code{cal.sinh} from the \code{CALMAR2} macro, for which \code{F} is the derivative of the inverse hyperbolic sine. } \usage{ make.calfun(Fm1, dF, name) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{Fm1}{Function \eqn{F-1}{F-1} taking a vector \code{u} and a vector of length 2, \code{bounds}.} \item{dF}{Derivative of \code{Fm1} wrt \code{u}: arguments \code{u} and \code{bounds} } \item{name}{Character string to use as name } } \value{ An object of class \code{"calfun"} } \references{ Deville J-C, Sarndal C-E, Sautory O (1993) Generalized Raking Procedures in Survey Sampling. JASA 88:1013-1020 Deville J-C, Sarndal C-E (1992) Calibration Estimators in Survey Sampling. JASA 87: 376-382 } \seealso{\code{\link{calibrate}} } \examples{ str(cal.linear) cal.linear$Fm1 cal.linear$dF hellinger <- make.calfun(Fm1=function(u, bounds) ((1-u/2)^-2)-1, dF= function(u, bounds) (1-u/2)^-3 , name="hellinger distance") hellinger data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194, 3914069), calfun=hellinger)) svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194, 3914069), calfun=cal.linear)) svymean(~api00,calibrate(dclus1, ~api99, pop=c(6194,3914069), calfun=cal.raking)) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/HR.Rd0000644000176200001440000000313114073741722013167 0ustar liggesusers\name{HR} \Rdversion{1.1} \alias{HR} \alias{ppsmat} \alias{ppscov} %- Also NEED an '\alias' for EACH other topic documented here. \title{Wrappers for specifying PPS designs} \description{ The Horvitz-Thompson estimator and the Hartley-Rao approximation require information in addition to the sampling probabilities for sampled individuals. These functions allow this information to be supplied. } \usage{ HR(psum=NULL, strata = NULL) ppsmat(jointprob, tolerance = 1e-04) ppscov(probcov, weighted=FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{psum}{ The sum of squared sampling probabilities for the population, divided by the sample size, as a single number or as a vector for stratified sampling } \item{strata}{ Stratum labels, of the same length as \code{psum}, if \code{psum} is a vector } \item{jointprob}{Matrix of pairwise sampling probabilities for the sampled individuals} \item{tolerance}{Tolerance for deciding that the covariance of sampling indicators is zero} \item{probcov}{Covariance of the sampling indicators (often written 'Delta'), or weighted covariance if \code{weighted=TRUE}} \item{weighted}{If \code{TRUE}, the \code{probcov} argument is the covariance divided by pairwise sampling probabilities} } \value{ An object of class \code{HR},\code{ppsmat}, \code{ppsdelta}, or \code{ppsdcheck} suitable for supplying as the \code{pps} argument to \code{\link{svydesign}}. } \seealso{ \link{election} for examples of PPS designs } \examples{ HR(0.1) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/svygofchisq.Rd0000644000176200001440000000242313743755164015236 0ustar liggesusers\name{svygofchisq} \alias{svygofchisq} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Test of fit to known probabilities } \description{ A Rao-Scott-type version of the chi-squared test for goodness of fit to prespecified proportions. The test statistic is the chi-squared statistic applied to the estimated population table, and the reference distribution is a Satterthwaite approximation: the test statistic divided by the estimated scale is compared to a chi-squared distribution with the estimated df. } \usage{ svygofchisq(formula, p, design, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{formula}{ Formula specifying a single factor variable } \item{p}{ Vector of probabilities for the categories of the factor, in the correct order (will be rescaled to sum to 1) } \item{design}{ Survey design object } \item{\dots}{ Other arguments to pass to \code{\link{svytotal}}, such as \code{na.rm} } } \value{ An object of class \code{htest} } \seealso{ \code{\link{chisq.test}}, \code{\link{svychisq}}, \code{\link{pchisqsum}} } \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) true_p <- table(apipop$stype) svygofchisq(~stype,dclus2,p=true_p) svygofchisq(~stype,dclus2,p=c(1/3,1/3,1/3)) } survey/man/barplot.svystat.Rd0000644000176200001440000000273413517345016016043 0ustar liggesusers\name{barplot.svystat} \alias{barplot.svystat} \alias{barplot.svrepstat} \alias{barplot.svyby} \alias{dotchart} \alias{dotchart.svystat} \alias{dotchart.svrepstat} \alias{dotchart.svyby} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Barplots and Dotplots } \description{ Draws a barplot or dotplot based on results from a survey analysis. The default barplot method already works for results from \code{\link{svytable}}. } \usage{ \method{barplot}{svystat}(height, ...) \method{barplot}{svrepstat}(height, ...) \method{barplot}{svyby}(height,beside=TRUE, ...) \method{dotchart}{svystat}(x,...,pch=19) \method{dotchart}{svrepstat}(x,...,pch=19) \method{dotchart}{svyby}(x,...,pch=19) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{height,x}{Analysis result } \item{beside}{Grouped, rather than stacked, bars} \item{\dots}{ Arguments to \code{\link{barplot}} or \code{dotchart} } \item{pch}{Overrides the default in \code{dotchart.default}} } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) a<-svymean(~stype, dclus1) barplot(a) barplot(a, names.arg=c("Elementary","High","Middle"), col="purple", main="Proportions of school level") b<-svyby(~enroll+api.stu, ~stype, dclus1, svymean) barplot(b,beside=TRUE,legend=TRUE) dotchart(b) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{hplot}% __ONLY ONE__ keyword per line survey/man/svymle.Rd0000755000176200001440000001632413640534666014216 0ustar liggesusers\name{svymle} \alias{svymle} \alias{print.svymle} \alias{coef.svymle} \alias{summary.svymle} \alias{vcov.svymle} %- Also NEED an `\alias' for EACH other topic documented here. \title{Maximum pseudolikelihood estimation in complex surveys} \description{ Maximises a user-specified likelihood parametrised by multiple linear predictors to data from a complex sample survey and computes the sandwich variance estimator of the coefficients. Note that this function maximises an estimated population likelihood, it is not the sample MLE. } \usage{ svymle(loglike, gradient = NULL, design, formulas, start = NULL, control = list(), na.action="na.fail", method=NULL, lower=NULL,upper=NULL,influence=FALSE,...) \method{summary}{svymle}(object, stderr=c("robust", "model"),...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{loglike}{vectorised loglikelihood function} \item{gradient}{Derivative of \code{loglike}. Required for variance computation and helpful for fitting} \item{design}{ a \code{survey.design} object } \item{formulas}{A list of formulas specifying the variable and linear predictors: see Details below} \item{start}{Starting values for parameters} \item{control}{control options for the optimiser: see the help page for the optimiser you are using.} \item{lower,upper}{Parameter bounds for \code{bobyqa}} \item{influence}{Return the influence functions (primarily for svyby)} \item{na.action}{Handling of \code{NA}s} \item{method}{\code{"nlm"} to use \code{nlm},\code{"uobyqa"} or \code{"bobyqa"} to use those optimisers from the \code{minqa} package; otherwise passed to \code{\link{optim}}} \item{\dots}{Arguments to \code{loglike} and \code{gradient} that are not to be optimised over.} \item{object}{\code{svymle} object} \item{stderr}{Choice of standard error estimator. The default is a standard sandwich estimator. See Details below.} } \details{ Optimization is done by \code{\link{nlm}} by default or if \code{method=="nlm"}. Otherwise \code{\link{optim}} is used and \code{method} specifies the method and \code{control} specifies control parameters. The \code{design} object contains all the data and design information from the survey, so all the formulas refer to variables in this object. The \code{formulas} argument needs to specify the response variable and a linear predictor for each freely varying argument of \code{loglike}. Consider for example the \code{\link{dnorm}} function, with arguments \code{x}, \code{mean}, \code{sd} and \code{log}, and suppose we want to estimate the mean of \code{y} as a linear function of a variable \code{z}, and to estimate a constant standard deviation. The \code{log} argument must be fixed at \code{FALSE} to get the loglikelihood. A \code{formulas} argument would be \code{list(~y, mean=~z, sd=~1)}. Note that the data variable \code{y} must be the first argument to \code{dnorm} and the first formula and that all the other formulas are labelled. It is also permitted to have the data variable as the left-hand side of one of the formulas: eg \code{list( mean=y~z, sd=~1)}. The two optimisers from the \code{minqa} package do not use any derivatives to be specified for optimisation, but they do assume that the function is smooth enough for a quadratic approximation, ie, that two derivatives exist. The usual variance estimator for MLEs in a survey sample is a `sandwich' variance that requires the score vector and the information matrix. It requires only sampling assumptions to be valid (though some model assumptions are required for it to be useful). This is the \code{stderr="robust"} option, which is available only when the \code{gradient} argument was specified. If the model is correctly specified and the sampling is at random conditional on variables in the model then standard errors based on just the information matrix will be approximately valid. In particular, for independent sampling where weights and strata depend on variables in the model the \code{stderr="model"} should work fairly well. } \value{ An object of class \code{svymle} } \author{Thomas Lumley} \seealso{\code{\link{svydesign}}, \code{\link{svyglm}}} \examples{ data(api) dstrat<-svydesign(id=~1, strata=~stype, weight=~pw, fpc=~fpc, data=apistrat) ## fit with glm m0 <- svyglm(api00~api99+ell,family="gaussian",design=dstrat) ## fit as mle (without gradient) m1 <- svymle(loglike=dnorm,gradient=NULL, design=dstrat, formulas=list(mean=api00~api99+ell, sd=~1), start=list(c(80,1,0),c(20)), log=TRUE) ## with gradient gr<- function(x,mean,sd,log){ dm<-2*(x - mean)/(2*sd^2) ds<-(x-mean)^2*(2*(2 * sd))/(2*sd^2)^2 - sqrt(2*pi)/(sd*sqrt(2*pi)) cbind(dm,ds) } m2 <- svymle(loglike=dnorm,gradient=gr, design=dstrat, formulas=list(mean=api00~api99+ell, sd=~1), start=list(c(80,1,0),c(20)), log=TRUE, method="BFGS") summary(m0) summary(m1,stderr="model") summary(m2) ## Using offsets m3 <- svymle(loglike=dnorm,gradient=gr, design=dstrat, formulas=list(mean=api00~api99+offset(ell)+ell, sd=~1), start=list(c(80,1,0),c(20)), log=TRUE, method="BFGS") ## demonstrating multiple linear predictors m3 <- svymle(loglike=dnorm,gradient=gr, design=dstrat, formulas=list(mean=api00~api99+offset(ell)+ell, sd=~stype), start=list(c(80,1,0),c(20,0,0)), log=TRUE, method="BFGS") ## More complicated censored lognormal data example ## showing that the response variable can be multivariate data(pbc, package="survival") pbc$randomized <- with(pbc, !is.na(trt) & trt>0) biasmodel<-glm(randomized~age*edema,data=pbc) pbc$randprob<-fitted(biasmodel) dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized)) ## censored logNormal likelihood lcens<-function(x,mean,sd){ ifelse(x[,2]==1, dnorm(log(x[,1]),mean,sd,log=TRUE), pnorm(log(x[,1]),mean,sd,log=TRUE,lower.tail=FALSE) ) } gcens<- function(x,mean,sd){ dz<- -dnorm(log(x[,1]),mean,sd)/pnorm(log(x[,1]),mean,sd,lower.tail=FALSE) dm<-ifelse(x[,2]==1, 2*(log(x[,1]) - mean)/(2*sd^2), dz*-1/sd) ds<-ifelse(x[,2]==1, (log(x[,1])-mean)^2*(2*(2 * sd))/(2*sd^2)^2 - sqrt(2*pi)/(sd*sqrt(2*pi)), ds<- dz*-(log(x[,1])-mean)/(sd*sd)) cbind(dm,ds) } m<-svymle(loglike=lcens, gradient=gcens, design=dpbc, method="newuoa", formulas=list(mean=I(cbind(time,status>0))~bili+protime+albumin, sd=~1), start=list(c(10,0,0,0),c(1))) summary(m) ## the same model, but now specifying the lower bound of zero on the ## log standard deviation mbox<-svymle(loglike=lcens, gradient=gcens, design=dpbc, method="bobyqa", formulas=list(mean=I(cbind(time,status>0))~bili+protime+albumin, sd=~1), lower=list(c(-Inf,-Inf,-Inf,-Inf),0), upper=Inf, start=list(c(10,0,0,0),c(1))) ## The censored lognormal model is now available in svysurvreg() summary(svysurvreg(Surv(time,status>0)~bili+protime+albumin, design=dpbc,dist="lognormal")) ## compare svymle scale value after log transformation svycontrast(m, quote(log(`sd.(Intercept)`))) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{models}% __ONLY ONE__ keyword per line \keyword{optimize}% __ONLY ONE__ keyword per line survey/man/marginpred.Rd0000644000176200001440000000562113517345016015012 0ustar liggesusers\name{marginpred} \Rdversion{1.1} \alias{marginpred} \alias{marginpred.svycoxph} \alias{marginpred.svykmlist} \alias{marginpred.svyglm} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Standardised predictions (predictive margins) for regression models. } \description{ Reweights the design (using \code{\link{calibrate}}) so that the adjustment variables are uncorrelated with the variables in the model, and then performs predictions by calling \code{predict}. When the adjustment model is saturated this is equivalent to direct standardization on the adjustment variables. The \code{svycoxph} and \code{svykmlist} methods return survival curves. } \usage{ marginpred(model, adjustfor, predictat, ...) \method{marginpred}{svycoxph}(model, adjustfor, predictat, se=FALSE, ...) \method{marginpred}{svykmlist}(model, adjustfor, predictat, se=FALSE, ...) \method{marginpred}{svyglm}(model, adjustfor, predictat, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{model}{ A regression model object of a class that has a \code{marginpred} method } \item{adjustfor}{ Model formula specifying adjustment variables, which must be in the design object of the model } \item{predictat}{ A data frame giving values of the variables in \code{model} to predict at} \item{se}{Estimate standard errors for the survival curve (uses a lot of memory if the sample size is large)} \item{\dots}{Extra arguments, passed to the \code{predict} method for \code{model}} } \seealso{ \code{\link{svypredmeans}} for the method of Graubard and Korn implemented in SUDAAN. \code{\link{calibrate}} \code{\link{predict.svycoxph}} } \examples{ ## generate data with apparent group effect from confounding set.seed(42) df<-data.frame(x=rnorm(100)) df$time<-rexp(100)*exp(df$x-1) df$status<-1 df$group<-(df$x+rnorm(100))>0 des<-svydesign(id=~1,data=df) newdf<-data.frame(group=c(FALSE,TRUE), x=c(0,0)) ## Cox model m0<-svycoxph(Surv(time,status)~group,design=des) m1<-svycoxph(Surv(time,status)~group+x,design=des) ## conditional predictions, unadjusted and adjusted cpred0<-predict(m0, type="curve", newdata=newdf, se=TRUE) cpred1<-predict(m1, type="curve", newdata=newdf, se=TRUE) ## adjusted marginal prediction mpred<-marginpred(m0, adjustfor=~x, predictat=newdf, se=TRUE) plot(cpred0) lines(cpred1[[1]],col="red") lines(cpred1[[2]],col="red") lines(mpred[[1]],col="blue") lines(mpred[[2]],col="blue") ## Kaplan--Meier s2<-svykm(Surv(time,status>0)~group, design=des) p2<-marginpred(s2, adjustfor=~x, predictat=newdf,se=TRUE) plot(s2) lines(p2[[1]],col="green") lines(p2[[2]],col="green") ## logistic regression logisticm <- svyglm(group~time, family=quasibinomial, design=des) newdf$time<-c(0.1,0.8) logisticpred <- marginpred(logisticm, adjustfor=~x, predictat=newdf) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/psrsq.Rd0000644000176200001440000000256413517345016014035 0ustar liggesusers\name{psrsq} \alias{psrsq} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Pseudo-Rsquareds } \description{ Compute the Nagelkerke and Cox--Snell pseudo-rsquared statistics, primarily for logistic regression. A generic function with methods for \code{glm} and \code{\link{svyglm}}. The method for \code{svyglm} objects uses the design-based estimators described by Lumley (2017) } \usage{ psrsq(object, method = c("Cox-Snell", "Nagelkerke"), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ A regression model (\code{glm} or \code{svyglm}) } \item{method}{ Which statistic to compute } \item{\dots}{ For future expansion } } \value{ Numeric value } \references{ Lumley T (2017) "Pseudo-R2 statistics under complex sampling" Australian and New Zealand Journal of Statistics DOI: 10.1111/anzs.12187 (preprint: \url{https://arxiv.org/abs/1701.07745}) } \seealso{ \code{\link{AIC.svyglm}} } \examples{ data(api) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) model1<-svyglm(I(sch.wide=="Yes")~ell+meals+mobility+as.numeric(stype), design=dclus2, family=quasibinomial()) psrsq(model1, type="Nagelkerke") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey }% use one of RShowDoc("KEYWORDS") \keyword{regression }% __ONLY ONE__ keyword per line survey/man/hospital.Rd0000755000176200001440000000165114073741722014511 0ustar liggesusers\name{hospital} \alias{hospital} \non_function{} \title{Sample of obstetric hospitals } \usage{data(hospital)} \description{ The \code{hospital} data frame has 15 rows and 5 columns. } \format{ This data frame contains the following columns: \describe{ \item{hospno}{Hospital id} \item{oblevel}{level of obstetric care} \item{weighta}{Weights, as given by the original reference} \item{tothosp}{total hospitalisations} \item{births}{births} \item{weightats}{Weights, as given in the source} } } \source{ Previously at \verb{http://www.ats.ucla.edu/stat/books/sop/hospsamp.dta} } \references{ Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley. } \examples{ data(hospital) hospdes<-svydesign(strata=~oblevel, id=~hospno, weights=~weighta, fpc=~tothosp, data=hospital) hosprep<-as.svrepdesign(hospdes) svytotal(~births, design=hospdes) svytotal(~births, design=hosprep) } \keyword{datasets} survey/man/svrepdesign.Rd0000755000176200001440000002164014556563037015226 0ustar liggesusers\name{svrepdesign} \alias{svrepdesign} \alias{svrepdesign.default} \alias{svrepdesign.imputationList} \alias{svrepdesign.character} \alias{[.svyrep.design} \alias{image.svyrep.design} \alias{print.svyrep.design} \alias{model.frame.svyrep.design} \alias{summary.svyrep.design} \alias{print.summary.svyrep.design} %- Also NEED an `\alias' for EACH other topic documented here. \title{Specify survey design with replicate weights} \description{ Some recent large-scale surveys specify replication weights rather than the sampling design (partly for privacy reasons). This function specifies the data structure for such a survey. } \usage{ svrepdesign(variables , repweights , weights, data, degf=NULL,...) \method{svrepdesign}{default}(variables = NULL, repweights = NULL, weights = NULL, data = NULL, degf=NULL, type = c("BRR", "Fay", "JK1","JKn","bootstrap", "ACS","successive-difference","JK2","other"), combined.weights=TRUE, rho = NULL, bootstrap.average=NULL, scale=NULL, rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"), mse=getOption("survey.replicates.mse"),...) \method{svrepdesign}{imputationList}(variables=NULL, repweights,weights,data, degf=NULL, mse=getOption("survey.replicates.mse"),...) \method{svrepdesign}{character}(variables=NULL,repweights=NULL, weights=NULL,data=NULL, degf=NULL, type=c("BRR","Fay","JK1", "JKn","bootstrap","ACS","successive-difference","JK2","other"), combined.weights=TRUE, rho=NULL, bootstrap.average=NULL, scale=NULL,rscales=NULL, fpc=NULL,fpctype=c("fraction","correction"),mse=getOption("survey.replicates.mse"), dbtype="SQLite", dbname,...) \method{image}{svyrep.design}(x, ..., col=grey(seq(.5,1,length=30)), type.=c("rep","total")) } %- maybe also `usage' for other objects documented here. \arguments{ \item{variables}{formula or data frame specifying variables to include in the design (default is all) } \item{repweights}{formula or data frame specifying replication weights, or character string specifying a regular expression that matches the names of the replication weight variables } \item{weights}{sampling weights } \item{data}{data frame to look up variables in formulas, or character string giving name of database table} \item{degf}{Design degrees of freedom; use \code{NULL} to have the function work this out for you} \item{type}{Type of replication weights} \item{combined.weights}{\code{TRUE} if the \code{repweights} already include the sampling weights. This is usually the case.} \item{rho}{Shrinkage factor for weights in Fay's method} \item{bootstrap.average}{For \code{type="bootstrap"}, if the bootstrap weights have been averaged, gives the number of iterations averaged over} \item{scale, rscales}{Scaling constant for variance, see Details below} \item{fpc,fpctype}{Finite population correction information} \item{mse}{If \code{TRUE}, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates} \item{dbname}{name of database, passed to \code{DBI::dbConnect()}} \item{dbtype}{Database driver: see Details} \item{x}{survey design with replicate weights} \item{...}{Other arguments to \code{\link{image}}} \item{col}{Colors} \item{type.}{\code{"rep"} for only the replicate weights, \code{"total"} for the replicate and sampling weights combined.} } \details{ In the BRR method, the dataset is split into halves, and the difference between halves is used to estimate the variance. In Fay's method, rather than removing observations from half the sample they are given weight \code{rho} in one half-sample and \code{2-rho} in the other. The ideal BRR analysis is restricted to a design where each stratum has two PSUs, however, it has been used in a much wider class of surveys. The \code{scale} and \code{rscales} arguments will be ignored (with a warning) if they are specified. The JK1 and JKn types are both jackknife estimators deleting one cluster at a time. JKn is designed for stratified and JK1 for unstratified designs. The successive-difference weights in the American Community Survey automatically use \code{scale = 4/ncol(repweights)} and \code{rscales=rep(1, ncol(repweights))}. This can be specified as \code{type="ACS"} or \code{type="successive-difference"}. The \code{scale} and \code{rscales} arguments will be ignored (with a warning) if they are specified. JK2 weights (\code{type="JK2"}), as in the California Health Interview Survey, automatically use \code{scale=1}, \code{rscales=rep(1, ncol(repweights))}. The \code{scale} and \code{rscales} arguments will be ignored (with a warning) if they are specified. Averaged bootstrap weights ("mean bootstrap") are used for some surveys from Statistics Canada. Yee et al (1999) describe their construction and use for one such survey. The variance is computed as the sum of squared deviations of the replicates from their mean. This may be rescaled: \code{scale} is an overall multiplier and \code{rscales} is a vector of replicate-specific multipliers for the squared deviations. That is, \code{rscales} should have one entry for each column of \code{repweights} If thereplication weights incorporate the sampling weights (\code{combined.weights=TRUE}) or for \code{type="other"} these must be specified, otherwise they can be guessed from the weights. A finite population correction may be specified for \code{type="other"}, \code{type="JK1"} and \code{type="JKn"}. \code{fpc} must be a vector with one entry for each replicate. To specify sampling fractions use \code{fpctype="fraction"} and to specify the correction directly use \code{fpctype="correction"} The design degrees of freedom are returned by \code{\link{degf}}. By default they are computed from the numerical rank of the repweights. This is slow for very large data sets and you can specify a value instead. \code{repweights} may be a character string giving a regular expression for the replicate weight variables. For example, in the California Health Interview Survey public-use data, the sampling weights are \code{"rakedw0"} and the replicate weights are \code{"rakedw1"} to \code{"rakedw80"}. The regular expression \code{"rakedw[1-9]"} matches the replicate weight variables (and not the sampling weight variable). \code{data} may be a character string giving the name of a table or view in a relational database that can be accessed through the \code{DBI} interface. For DBI interfaces \code{dbtype} should be the name of the database driver and \code{dbname} should be the name by which the driver identifies the specific database (eg file name for SQLite). The appropriate database interface package must already be loaded (eg \code{RSQLite} for SQLite). The survey design object will contain the replicate weights, but actual variables will be loaded from the database only as needed. Use \code{\link[=close.DBIsvydesign]{close}} to close the database connection and \code{\link[=open.DBIsvydesign]{open}} to reopen the connection, eg, after loading a saved object. The database interface does not attempt to modify the underlying database and so can be used with read-only permissions on the database. To generate your own replicate weights either use \code{\link{as.svrepdesign}} on a \code{survey.design} object, or see \code{\link{brrweights}}, \code{\link{bootweights}}, \code{\link{jk1weights}} and \code{\link{jknweights}} The \code{model.frame} method extracts the observed data. } \value{ Object of class \code{svyrep.design}, with methods for \code{print}, \code{summary}, \code{weights}, \code{image}. } \references{Levy and Lemeshow. "Sampling of Populations". Wiley. Shao and Tu. "The Jackknife and Bootstrap." Springer. Yee et al (1999). Bootstrat Variance Estimation for the National Population Health Survey. Proceedings of the ASA Survey Research Methodology Section. \url{https://web.archive.org/web/20151110170959/http://www.amstat.org/sections/SRMS/Proceedings/papers/1999_136.pdf} } \note{To use replication-weight analyses on a survey specified by sampling design, use \code{as.svrepdesign} to convert it. } \seealso{\code{\link{as.svrepdesign}}, \code{\link{svydesign}}, \code{\link{brrweights}}, \code{bootweights} } \examples{ data(scd) # use BRR replicate weights from Levy and Lemeshow repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights, combined.weights=FALSE) svyratio(~alive, ~arrests, scdrep) \dontrun{ ## Needs RSQLite library(RSQLite) db_rclus1<-svrepdesign(weights=~pw, repweights="wt[1-9]+", type="JK1", scale=(1-15/757)*14/15, data="apiclus1rep",dbtype="SQLite", dbname=system.file("api.db",package="survey"), combined=FALSE) svymean(~api00+api99,db_rclus1) summary(db_rclus1) ## closing and re-opening a connection close(db_rclus1) db_rclus1 try(svymean(~api00+api99,db_rclus1)) db_rclus1<-open(db_rclus1) svymean(~api00+api99,db_rclus1) } } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/subset.survey.design.Rd0000755000176200001440000000272113517345016016774 0ustar liggesusers\name{subset.survey.design} \alias{subset.survey.design} \alias{subset.svyrep.design} \alias{[.survey.design} %- Also NEED an `\alias' for EACH other topic documented here. \title{Subset of survey} \description{ Restrict a survey design to a subpopulation, keeping the original design information about number of clusters, strata. If the design has no post-stratification or calibration data the subset will use proportionately less memory. } \usage{ \method{subset}{survey.design}(x, subset, ...) \method{subset}{svyrep.design}(x, subset, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{A survey design object} \item{subset}{An expression specifying the subpopulation} \item{\dots}{Arguments not used by this method} } \value{ A new survey design object } \seealso{\code{\link{svydesign}}} \examples{ data(fpc) dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) dsub<-subset(dfpc,x>4) summary(dsub) svymean(~x,design=dsub) ## These should give the same domain estimates and standard errors svyby(~x,~I(x>4),design=dfpc, svymean) summary(svyglm(x~I(x>4)+0,design=dfpc)) data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) svymean(~enroll, subset(dclus1, sch.wide=="Yes" & comp.imp=="Yes")) svymean(~enroll, subset(rclus1, sch.wide=="Yes" & comp.imp=="Yes")) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/man/trimWeights.Rd0000644000176200001440000000440614113110202015144 0ustar liggesusers\name{trimWeights} \Rdversion{1.1} \alias{trimWeights} \alias{trimWeights.svyrep.design} \alias{trimWeights.survey.design2} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Trim sampling weights } \description{ Trims very high or very low sampling weights to reduce the influence of outlying observations. In a replicate-weight design object, the replicate weights are also trimmed. The total amount trimmed is divided among the observations that were not trimmed, so that the total weight remains the same. } \usage{ trimWeights(design, upper = Inf, lower = -Inf, ...) \method{trimWeights}{survey.design2}(design, upper = Inf, lower = -Inf, strict=FALSE,...) \method{trimWeights}{svyrep.design}(design, upper = Inf, lower = -Inf, strict=FALSE, compress=FALSE,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{design}{ A survey design object } \item{upper}{ Upper bound for weights } \item{lower}{ Lower bound for weights } \item{strict}{ The reapportionment of the `trimmings' from the weights can push other weights over the limits. If \code{trim=TRUE} the function repeats the trimming iteratively to prevent this. For replicate-weight designs \code{strict} applies only to the trimming of the sampling weights. } \item{compress}{ Compress the replicate weights after trimming. } \item{\dots}{ Other arguments for future expansion } } \value{ A new survey design object with trimmed weights. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ \code{\link{calibrate}} has a \code{trim} option for trimming the calibration adjustments. } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018, api99=3914069) dclus1g<-calibrate(dclus1, ~stype+api99, pop.totals) summary(weights(dclus1g)) dclus1t<-trimWeights(dclus1g,lower=20, upper=45) summary(weights(dclus1t)) dclus1tt<-trimWeights(dclus1g, lower=20, upper=45,strict=TRUE) summary(weights(dclus1tt)) svymean(~api99+api00+stype, dclus1g) svymean(~api99+api00+stype, dclus1t) svymean(~api99+api00+stype, dclus1tt) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/crowd.Rd0000755000176200001440000000205313517345016013777 0ustar liggesusers\name{crowd} \alias{crowd} \docType{data} \title{Household crowding} \description{ A tiny dataset from the VPLX manual. } \usage{data(crowd)} \format{ A data frame with 6 observations on the following 5 variables. \describe{ \item{rooms}{Number of rooms in the house} \item{person}{Number of people in the household} \item{weight}{Sampling weight} \item{cluster}{Cluster number} \item{stratum}{Stratum number} } } \source{ Manual for VPLX, Census Bureau. } \examples{ data(crowd) ## Example 1-1 i1.1<-as.svrepdesign(svydesign(id=~cluster, weight=~weight,data=crowd)) i1.1<-update(i1.1, room.ratio=rooms/person, overcrowded=factor(person>rooms)) svymean(~rooms+person+room.ratio,i1.1) svytotal(~rooms+person+room.ratio,i1.1) svymean(~rooms+person+room.ratio,subset(i1.1,overcrowded==TRUE)) svytotal(~rooms+person+room.ratio,subset(i1.1,overcrowded==TRUE)) ## Example 1-2 i1.2<-as.svrepdesign(svydesign(id=~cluster,weight=~weight,strata=~stratum, data=crowd)) svymean(~rooms+person,i1.2) svytotal(~rooms+person,i1.2) } \keyword{datasets} survey/man/election.Rd0000644000176200001440000000547313517345016014471 0ustar liggesusers\name{election} \alias{election} \alias{election_pps} \alias{election_jointprob} \alias{election_jointHR} \alias{election_insample} \docType{data} \title{US 2004 presidential election data at state or county level} \description{ A sample of voting data from US states or counties (depending on data availability), sampled with probability proportional to number of votes. The sample was drawn using Tille's splitting method, implemented in the "sampling" package. } \usage{data(election)} \format{ \code{election} is a data frame with 4600 observations on the following 8 variables. \describe{ \item{\code{County}}{A factor specifying the state or country} \item{\code{TotPrecincts}}{Number of precincts in the state or county} \item{\code{PrecinctsReporting}}{Number of precincts supplying data} \item{\code{Bush}}{Votes for George W. Bush} \item{\code{Kerry}}{Votes for John Kerry} \item{\code{Nader}}{Votes for Ralph Nader} \item{\code{votes}}{Total votes for those three candidates} \item{\code{p}}{Sampling probability, proportional to \code{votes}} } \code{election_pps} is a sample of 40 counties or states taken with probability proportional to the number of votes. It includes the additional column \code{wt} with the sampling weights. \code{election_insample} indicates which rows of \code{election} were sampled. \code{election_jointprob} are the pairwise sampling probabilities and \code{election_jointHR} are approximate pairwise sampling probabilities using the Hartley-Rao approximation. } \source{ . } \examples{ data(election) ## high positive correlation between totals plot(Bush~Kerry,data=election,log="xy") ## high negative correlation between proportions plot(I(Bush/votes)~I(Kerry/votes), data=election) ## Variances without replacement ## Horvitz-Thompson type dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) ## Yates-Grundy type dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") ## The with-replacement approximation dppswr <-svydesign(id=~1, probs=~p, data=election_pps) svytotal(~Bush+Kerry+Nader, dpps_ht) svytotal(~Bush+Kerry+Nader, dpps_yg) svytotal(~Bush+Kerry+Nader, dpps_hr) svytotal(~Bush+Kerry+Nader, dpps_hryg) svytotal(~Bush+Kerry+Nader, dpps_hr1) svytotal(~Bush+Kerry+Nader, dpps_br) svytotal(~Bush+Kerry+Nader, dpps_ov) svytotal(~Bush+Kerry+Nader, dppswr) } \keyword{datasets} \keyword{survey} survey/man/yrbs.Rd0000644000176200001440000000217514567004143013641 0ustar liggesusers\name{yrbs} \alias{yrbs} \docType{data} \title{ One variable from the Youth Risk Behaviors Survey, 2015. } \description{ Design information from the Youth Risk Behaviors Survey (YRBS), together with the single variable `Never/Rarely wore bike helmet'. Used as an analysis example by CDC. } \usage{data("yrbs")} \format{ A data frame with 15624 observations on the following 4 variables. \describe{ \item{\code{weight}}{sampling weights} \item{\code{stratum}}{sampling strata} \item{\code{psu}}{primary sampling units} \item{\code{qn8}}{1=Yes, 2=No} } } \source{ \url{https://ftp.cdc.gov/pub/Data/YRBS/2015smy/} for files } \references{ Centers for Disease Control and Prevention (2016) Software for Analysis of YRBS Data. [CRAN doesn't believe the URL is valid] } \examples{ data(yrbs) yrbs_design <- svydesign(id=~psu, weight=~weight, strata=~stratum, data=yrbs) yrbs_design <- update(yrbs_design, qn8yes=2-qn8) ci <- svyciprop(~qn8yes, yrbs_design, na.rm=TRUE, method="xlogit") ci ## to print more digits: matches SUDAAN and SPSS exactly, per table 3 of reference coef(ci) SE(ci) attr(ci,"ci") } \keyword{datasets} survey/man/stratsample.Rd0000644000176200001440000000165213517345016015221 0ustar liggesusers\name{stratsample} \Rdversion{1.1} \alias{stratsample} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Take a stratified sample } \description{ This function takes a stratified sample without replacement from a data set. } \usage{ stratsample(strata, counts) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{strata}{ Vector of stratum identifiers; will be coerced to character } \item{counts}{ named vector of stratum sample sizes, with names corresponding to the values of \code{as.character(strata)} } } \value{ vector of indices into \code{strata} giving the sample } \seealso{ \code{\link{sample}} The "sampling" package has many more sampling algorithms. } \examples{ data(api) s<-stratsample(apipop$stype, c("E"=5,"H"=4,"M"=2)) table(apipop$stype[s]) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} survey/man/twophase.Rd0000644000176200001440000002124414565003231014505 0ustar liggesusers\name{twophase} \alias{twophase} \alias{twophasevar} \alias{twophase2var} \alias{[.twophase} \alias{subset.twophase} \alias{print.twophase} \alias{summary.twophase} \alias{print.summary.twophase} \alias{model.frame.twophase} \alias{na.fail.twophase} \alias{na.omit.twophase} \alias{na.exclude.twophase} \alias{svyrecvar.phase1} \alias{multistage.phase1} \alias{onestage.phase1} \alias{onestrat.phase1} %- Also NEED an '\alias' for EACH other topic documented here. \title{Two-phase designs} \description{ In a two-phase design a sample is taken from a population and a subsample taken from the sample, typically stratified by variables not known for the whole population. The second phase can use any design supported for single-phase sampling. The first phase must currently be one-stage element or cluster sampling } \usage{ twophase(id, strata = NULL, probs = NULL, weights = NULL, fpc = NULL, subset, data, method=c("full","approx","simple"), pps=NULL) twophasevar(x,design) twophase2var(x,design) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{id}{list of two formulas for sampling unit identifiers} \item{strata}{list of two formulas (or \code{NULL}s) for stratum identifies} \item{probs}{ list of two formulas (or \code{NULL}s) for sampling probabilities} \item{weights}{Only for \code{method="approx"}, list of two formulas (or \code{NULL}s) for sampling weights} \item{fpc}{list of two formulas (or \code{NULL}s) for finite population corrections} \item{subset}{formula specifying which observations are selected in phase 2} \item{data}{Data frame will all data for phase 1 and 2} \item{method}{\code{"full"} requires (much) more memory, but gives unbiased variance estimates for general multistage designs at both phases. \code{"simple"} or \code{"approx"} uses the standard error calculation from version 3.14 and earlier, which uses much less memory and is correct for designs with simple random sampling at phase one and stratified random sampling at phase two. } \item{pps}{With \code{method="full"}, an optional list of two PPS specifications for \code{\link{svydesign}}. At the moment, the phase-one element must be \code{NULL}} \item{x}{probability-weighted estimating functions} \item{design}{two-phase design} } \details{ The population for the second phase is the first-phase sample. If the second phase sample uses stratified (multistage cluster) sampling without replacement and all the stratum and sampling unit identifier variables are available for the whole first-phase sample it is possible to estimate the sampling probabilities/weights and the finite population correction. These would then be specified as \code{NULL}. Two-phase case-control and case-cohort studies in biostatistics will typically have simple random sampling with replacement as the first stage. Variances given here may differ slightly from those in the biostatistics literature where a model-based estimator of the first-stage variance would typically be used. Variance computations are based on the conditioning argument in Section 9.3 of Sarndal et al. Method \code{"full"} corresponds exactly to the formulas in that reference. Method \code{"simple"} or \code{"approx"} (the two are the same) uses less time and memory but is exact only for some special cases. The most important special case is the two-phase epidemiologic designs where phase 1 is simple random sampling from an infinite population and phase 2 is stratified random sampling. See the \code{tests} directory for a worked example. The only disadvantage of method="simple" in these cases is that standardization of margins (\code{\link{marginpred}}) is not available. For \code{method="full"}, sampling probabilities must be available for each stage of sampling, within each phase. For multistage sampling this requires specifying either \code{fpc} or \code{probs} as a formula with a term for each stage of sampling. If no \code{fpc} or \code{probs} are specified at phase 1 it is treated as simple random sampling from an infinite population, and population totals will not be correctly estimated, but means, quantiles, and regression models will be correct. The \code{pps} argument allows for PPS sampling at phase two (or eventually at phase one), and also for Poisson sampling at phase two as a model for non-response. } \value{ \code{twophase} returns an object of class \code{twophase2} (for \code{method="full"}) or \code{twophase}. The structure of \code{twophase2} objects may change as unnecessary components are removed. \code{twophase2var} and \code{twophasevar} return a variance matrix with an attribute containing the separate phase 1 and phase 2 contributions to the variance. } \references{ Sarndal CE, Swensson B, Wretman J (1992) "Model Assisted Survey Sampling" Springer. Breslow NE and Chatterjee N, Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. "Applied Statistics" 48:457-68, 1999 Breslow N, Lumley T, Ballantyne CM, Chambless LE, Kulick M. (2009) Improved Horvitz-Thompson estimation of model parameters from two-phase stratified samples: applications in epidemiology. Statistics in Biosciences. doi 10.1007/s12561-009-9001-6 Lin, DY and Ying, Z (1993). Cox regression with incomplete covariate measurements. "Journal of the American Statistical Association" 88: 1341-1349. } \seealso{\code{\link{svydesign}}, \code{\link{svyrecvar}} for multi*stage* sampling \code{\link{calibrate}} for calibration (GREG) estimators. \code{\link{estWeights}} for two-phase designs for missing data. The "epi" and "phase1" vignettes for examples and technical details. } \examples{ ## two-phase simple random sampling. data(pbc, package="survival") pbc$randomized<-with(pbc, !is.na(trt) & trt>0) pbc$id<-1:nrow(pbc) d2pbc<-twophase(id=list(~id,~id), data=pbc, subset=~randomized) svymean(~bili, d2pbc) ## two-stage sampling as two-phase data(mu284) ii<-with(mu284, c(1:15, rep(1:5,n2[1:5]-3))) mu284.1<-mu284[ii,] mu284.1$id<-1:nrow(mu284.1) mu284.1$sub<-rep(c(TRUE,FALSE),c(15,34-15)) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) ## first phase cluster sample, second phase stratified within cluster d2mu284<-twophase(id=list(~id1,~id),strata=list(NULL,~id1), fpc=list(~n1,NULL),data=mu284.1,subset=~sub) svytotal(~y1, dmu284) svytotal(~y1, d2mu284) svymean(~y1, dmu284) svymean(~y1, d2mu284) ## case-cohort design: this example requires R 2.2.0 or later library("survival") data(nwtco) ## stratified on case status dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcchs) ## Using survival::cch subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing") ## two-phase case-control ## Similar to Breslow & Chatterjee, Applied Statistics (1999) but with ## a slightly different version of the data set nwtco$incc2<-as.logical(with(nwtco, ifelse(rel | instit==2,1,rbinom(nrow(nwtco),1,.1)))) dccs2<-twophase(id=list(~seqno,~seqno),strata=list(NULL,~interaction(rel,instit)), data=nwtco, subset=~incc2) dccs8<-twophase(id=list(~seqno,~seqno),strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2) summary(glm(rel~factor(stage)*factor(histol),data=nwtco,family=binomial())) summary(svyglm(rel~factor(stage)*factor(histol),design=dccs2,family=quasibinomial())) summary(svyglm(rel~factor(stage)*factor(histol),design=dccs8,family=quasibinomial())) ## Stratification on stage is really post-stratification, so we should use calibrate() gccs8<-calibrate(dccs2, phase=2, formula=~interaction(rel,stage,instit)) summary(svyglm(rel~factor(stage)*factor(histol),design=gccs8,family=quasibinomial())) ## For this saturated model calibration is equivalent to estimating weights. pccs8<-calibrate(dccs2, phase=2,formula=~interaction(rel,stage,instit), calfun="rrz") summary(svyglm(rel~factor(stage)*factor(histol),design=pccs8,family=quasibinomial())) ## Since sampling is SRS at phase 1 and stratified RS at phase 2, we ## can use method="simple" to save memory. dccs8_simple<-twophase(id=list(~seqno,~seqno),strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2,method="simple") summary(svyglm(rel~factor(stage)*factor(histol),design=dccs8_simple,family=quasibinomial())) } \keyword{survey}% at least one, from doc/KEYWORDS survey/man/dimnames.DBIsvydesign.Rd0000644000176200001440000000303413517621352017004 0ustar liggesusers\name{dimnames.DBIsvydesign} \alias{dimnames.DBIsvydesign} \alias{dimnames.survey.design} \alias{dimnames.svyrep.design} \alias{dimnames.twophase} \alias{dimnames.svyimputationList} \alias{dim.DBIsvydesign} \alias{dim.survey.design} \alias{dim.twophase} \alias{dim.svyimputationList} \alias{dim.svyrep.design} %- Also NEED an '\alias' for EACH other topic documented here. \title{Dimensions of survey designs} \description{ \code{dimnames} returns variable names and row names for the data variables in a design object and \code{dim} returns dimensions. For multiple imputation designs there is a third dimension giving the number of imputations. For database-backed designs the second dimension includes variables defined by \code{update}. The first dimension excludes observations with zero weight. } \usage{ \method{dim}{survey.design}(x) \method{dim}{svyimputationList}(x) \method{dimnames}{survey.design}(x) \method{dimnames}{DBIsvydesign}(x) \method{dimnames}{svyimputationList}(x) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{Design object} } \value{ A vector of numbers for \code{dim}, a list of vectors of strings for \code{dimnames}. } \seealso{ \code{\link{update.DBIsvydesign}}, \code{\link{with.svyimputationList}}} \examples{ data(api) dclus1 <- svydesign(ids=~dnum,weights=~pw,data=apiclus1,fpc=~fpc) dim(dclus1) dimnames(dclus1) colnames(dclus1) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{survey} \keyword{manip}% __ONLY ONE__ keyword per line survey/man/svyglm.Rd0000755000176200001440000002021713641450705014204 0ustar liggesusers\name{svyglm} \alias{svyglm} \alias{svyglm.survey.design} \alias{svyglm.svyrep.design} \alias{summary.svyglm} \alias{summary.svrepglm} \alias{vcov.svyglm} \alias{residuals.svyglm} \alias{residuals.svrepglm} \alias{predict.svyglm} \alias{predict.svrepglm} \alias{coef.svyglm} %- Also NEED an `\alias' for EACH other topic documented here. \title{Survey-weighted generalised linear models.} \description{ Fit a generalised linear model to data from a complex survey design, with inverse-probability weighting and design-based standard errors. } \usage{ \method{svyglm}{survey.design}(formula, design, subset=NULL, family=stats::gaussian(),start=NULL, rescale=TRUE, ..., deff=FALSE, influence=FALSE) \method{svyglm}{svyrep.design}(formula, design, subset=NULL, family=stats::gaussian(),start=NULL, rescale=NULL, ..., rho=NULL, return.replicates=FALSE, na.action,multicore=getOption("survey.multicore")) \method{summary}{svyglm}(object, correlation = FALSE, df.resid=NULL, ...) \method{predict}{svyglm}(object,newdata=NULL,total=NULL, type=c("link","response","terms"), se.fit=(type != "terms"),vcov=FALSE,...) \method{predict}{svrepglm}(object,newdata=NULL,total=NULL, type=c("link","response","terms"), se.fit=(type != "terms"),vcov=FALSE, return.replicates=!is.null(object$replicates),...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{formula}{Model formula} \item{design}{Survey design from \code{\link{svydesign}} or \code{\link{svrepdesign}}. Must contain all variables in the formula} \item{subset}{Expression to select a subpopulation} \item{family}{\code{family} object for \code{glm}} \item{start}{Starting values for the coefficients (needed for some uncommon link/family combinations)} \item{rescale}{Rescaling of weights, to improve numerical stability. The default rescales weights to sum to the sample size. Use \code{FALSE} to not rescale weights. For replicate-weight designs, use \code{TRUE} to rescale weights to sum to 1, as was the case before version 3.34.} \item{\dots}{Other arguments passed to \code{glm} or \code{summary.glm} } \item{rho}{For replicate BRR designs, to specify the parameter for Fay's variance method, giving weights of \code{rho} and \code{2-rho}} \item{return.replicates}{Return the replicates as the \code{replicates} component of the result? (for \code{predict}, only possible if they were computed in the \code{svyglm} fit)} \item{deff}{Estimate the design effects} \item{influence}{Return influence functions} \item{object}{A \code{svyglm} object} \item{correlation}{Include the correlation matrix of parameters?} \item{na.action}{Handling of NAs} \item{multicore}{Use the \code{multicore} package to distribute replicates across processors?} \item{df.resid}{Optional denominator degrees of freedom for Wald tests} \item{newdata}{new data frame for prediction} \item{total}{population size when predicting population total} \item{type}{linear predictor (\code{link}) or response} \item{se.fit}{if \code{TRUE}, return variances of predictions} \item{vcov}{if \code{TRUE} and \code{se=TRUE} return full variance-covariance matrix of predictions} } \details{ For binomial and Poisson families use \code{family=quasibinomial()} and \code{family=quasipoisson()} to avoid a warning about non-integer numbers of successes. The `quasi' versions of the family objects give the same point estimates and standard errors and do not give the warning. If \code{df.resid} is not specified the df for the null model is computed by \code{\link{degf}} and the residual df computed by subtraction. This is recommended by Korn and Graubard and is correct for PSU-level covariates but is potentially very conservative for individual-level covariates. To get tests based on a Normal distribution use \code{df.resid=Inf}, and to use number of PSUs-number of strata, specify \code{df.resid=degf(design)}. Parallel processing with \code{multicore=TRUE} is helpful only for fairly large data sets and on computers with sufficient memory. It may be incompatible with GUIs, although the Mac Aqua GUI appears to be safe. \code{predict} gives fitted values and sampling variability for specific new values of covariates. When \code{newdata} are the population mean it gives the regression estimator of the mean, and when \code{newdata} are the population totals and \code{total} is specified it gives the regression estimator of the population total. Regression estimators of mean and total can also be obtained with \code{\link{calibrate}}. } \note{ \code{svyglm} always returns 'model-robust' standard errors; the Horvitz-Thompson-type standard errors used everywhere in the survey package are a generalisation of the model-robust 'sandwich' estimators. In particular, a quasi-Poisson \code{svyglm} will return correct standard errors for relative risk regression models. } \note{ This function does not return the same standard error estimates for the regression estimator of population mean and total as some textbooks, or SAS. However, it does give the same standard error estimator as estimating the mean or total with calibrated weights. In particular, under simple random sampling with or without replacement there is a simple rescaling of the mean squared residual to estimate the mean squared error of the regression estimator. The standard error estimate produced by \code{predict.svyglm} has very similar (asymptotically identical) expected value to the textbook estimate, and has the advantage of being applicable when the supplied \code{newdata} are not the population mean of the predictors. The difference is small when the sample size is large, but can be appreciable for small samples. You can obtain the other standard error estimator by calling \code{predict.svyglm} with the covariates set to their estimated (rather than true) population mean values. } \value{ \code{svyglm} returns an object of class \code{svyglm}. The \code{predict} method returns an object of class \code{svystat}} \author{Thomas Lumley} \seealso{ \code{\link{glm}}, which is used to do most of the work. \code{\link{regTermTest}}, for multiparameter tests \code{\link{calibrate}}, for an alternative way to specify regression estimators of population totals or means \code{\link{svyttest}} for one-sample and two-sample t-tests. } \references{ Lumley T, Scott A (2017) "Fitting Regression Models to Survey Data" Statistical Science 32: 265-278 } \examples{ data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) rstrat<-as.svrepdesign(dstrat) rclus2<-as.svrepdesign(dclus2) summary(svyglm(api00~ell+meals+mobility, design=dstrat)) summary(svyglm(api00~ell+meals+mobility, design=dclus2)) summary(svyglm(api00~ell+meals+mobility, design=rstrat)) summary(svyglm(api00~ell+meals+mobility, design=rclus2)) ## use quasibinomial, quasipoisson to avoid warning messages summary(svyglm(sch.wide~ell+meals+mobility, design=dstrat, family=quasibinomial())) ## Compare regression and ratio estimation of totals api.ratio <- svyratio(~api.stu,~enroll, design=dstrat) pop<-data.frame(enroll=sum(apipop$enroll, na.rm=TRUE)) npop <- nrow(apipop) predict(api.ratio, pop$enroll) ## regression estimator is less efficient api.reg <- svyglm(api.stu~enroll, design=dstrat) predict(api.reg, newdata=pop, total=npop) ## same as calibration estimator svytotal(~api.stu, calibrate(dstrat, ~enroll, pop=c(npop, pop$enroll))) ## svyglm can also reproduce the ratio estimator api.reg2 <- svyglm(api.stu~enroll-1, design=dstrat, family=quasi(link="identity",var="mu")) predict(api.reg2, newdata=pop, total=npop) ## higher efficiency by modelling variance better api.reg3 <- svyglm(api.stu~enroll-1, design=dstrat, family=quasi(link="identity",var="mu^3")) predict(api.reg3, newdata=pop, total=npop) ## true value sum(apipop$api.stu) } \keyword{regression}% at least one, from doc/KEYWORDS \keyword{survey}% at least one, from doc/KEYWORDS survey/man/calibrate.Rd0000644000176200001440000003566213706523370014621 0ustar liggesusers\name{calibrate} \alias{calibrate.survey.design2} \alias{calibrate.svyrep.design} %\alias{is.calibrated} \alias{calibrate} %\alias{regcalibrate.survey.design2} %\alias{regcalibrate.svyrep.design} %\alias{regcalibrate} \alias{calibrate.twophase} \alias{grake} \alias{cal_names} %- Also NEED an '\alias' for EACH other topic documented here. \title{Calibration (GREG) estimators} \description{ Calibration, generalized raking, or GREG estimators generalise post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. This function reweights the survey design and adds additional information that is used by \code{svyrecvar} to reduce the estimated standard errors. } \usage{ calibrate(design,...) \method{calibrate}{survey.design2}(design, formula, population, aggregate.stage=NULL, stage=0, variance=NULL, bounds=c(-Inf,Inf), calfun=c("linear","raking","logit"), maxit=50,epsilon=1e-7,verbose=FALSE,force=FALSE,trim=NULL, bounds.const=FALSE, sparse=FALSE,...) \method{calibrate}{svyrep.design}(design, formula, population,compress=NA, aggregate.index=NULL, variance=NULL, bounds=c(-Inf,Inf), calfun=c("linear","raking","logit"), maxit=50, epsilon=1e-7, verbose=FALSE,force=FALSE,trim=NULL, bounds.const=FALSE, sparse=FALSE,...) \method{calibrate}{twophase}(design, phase=2,formula, population, calfun=c("linear","raking","logit","rrz"),...) grake(mm,ww,calfun,eta=rep(0,NCOL(mm)),bounds,population,epsilon, verbose,maxit,variance=NULL) cal_names(formula,design,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{design}{Survey design object} \item{formula}{Model formula for calibration model, or list of formulas for each margin} \item{population}{Vectors of population column totals for the model matrix in the calibration model, or list of such vectors for each cluster, or list of tables for each margin. Required except for two-phase designs} \item{compress}{compress the resulting replicate weights if \code{TRUE} or if \code{NA} and weights were previously compressed} \item{stage}{See Details below} \item{variance}{Coefficients for variance in calibration model (heteroskedasticity parameters) (see Details below)} \item{aggregate.stage}{An integer. If not \code{NULL}, make calibration weights constant within sampling units at this stage.} \item{aggregate.index}{A vector or one-sided formula. If not \code{NULL}, make calibration weights constant within levels of this variable} \item{bounds}{Bounds for the calibration weights, optional except for \code{calfun="logit"}} \item{bounds.const}{Should be \code{TRUE} if \code{bounds} have been spcified as constant values rather than multiplicative values} \item{trim}{Weights outside this range will be trimmed to these bounds.} \item{...}{Options for other methods} \item{calfun}{Calibration function: see below} \item{maxit}{Number of iterations} \item{epsilon}{Tolerance in matching population total. Either a single number or a vector of the same length as \code{population}} \item{verbose}{Print lots of uninteresting information} \item{force}{Return an answer even if the specified accuracy was not achieved} \item{phase}{Phase of a two-phase design to calibrate (only \code{phase=2} currently implemented.)} \item{mm}{Model matrix} \item{ww}{Vector of weights} \item{eta}{Starting values for iteration} \item{sparse}{Use sparse matrices for faster computation} } \details{ The \code{formula} argument specifies a model matrix, and the \code{population} argument is the population column sums of this matrix. The function \code{cal_names} shows what the column names of this model matrix will be. For the important special case where the calibration totals are (possibly overlapping) marginal tables of factor variables, as in classical raking, the \code{formula} and \code{population} arguments may be lists in the same format as the input to \code{\link{rake}}. If the \code{population} argument has a names attribute it will be checked against the names produced by \code{model.matrix(formula)} and reordered if necessary. This protects against situations where the (locale-dependent) ordering of factor levels is not what you expected. Numerical instabilities may result if the sampling weights in the \code{design} object are wrong by multiple orders of magnitude. The code now attempts to rescale the weights first, but it is better for the user to ensure that the scale is reasonable. The \code{calibrate} function implements linear, bounded linear, raking, bounded raking, and logit calibration functions. All except unbounded linear calibration use the Newton-Raphson algorithm described by Deville et al (1993). This algorithm is exposed for other uses in the \code{grake} function. Unbounded linear calibration uses an algorithm that is less sensitive to collinearity. The calibration function may be specified as a string naming one of the three built-in functions or as an object of class \code{calfun}, allowing user-defined functions. See \code{\link{make.calfun}} for details. The \code{bounds} argument can be specified as global upper and lower bounds e.g \code{bounds=c(0.5, 2)} or as a list with lower and upper vectors e.g. \code{bounds=list(lower=lower, upper=upper)}. This allows for individual boundary constraints for each unit. The lower and upper vectors must be the same length as the input data. The bounds can be specified as multiplicative values or constant values. If constant, \code{bounds.const} must be set to \code{TRUE}. Calibration with bounds, or on highly collinear data, may fail. If \code{force=TRUE} the approximately calibrated design object will still be returned (useful for examining why it failed). A failure in calibrating a set of replicate weights when the sampling weights were successfully calibrated will give only a warning, not an error. When calibration to the desired set of bounds is not possible, another option is to trim weights. To do this set \code{bounds} to a looser set of bounds for which calibration is achievable and set \code{trim} to the tighter bounds. Weights outside the bounds will be trimmed to the bounds, and the excess weight distributed over other observations in proportion to their sampling weight (and so this may put some other observations slightly over the trimming bounds). The projection matrix used in computing standard errors is based on the feasible bounds specified by the \code{bounds} argument. See also \code{\link{trimWeights}}, which trims the final weights in a design object rather than the calibration adjustments. For two-phase designs \code{calfun="rrz"} estimates the sampling probabilities using logistic regression as described by Robins et al (1994). \code{\link{estWeights}} will do the same thing. Calibration may result in observations within the last-stage sampling units having unequal weight even though they necessarily are sampled together. Specifying \code{aggegrate.stage} ensures that the calibration weight adjustments are constant within sampling units at the specified stage; if the original sampling weights were equal the final weights will also be equal. The algorithm is as described by Vanderhoeft (2001, section III.D). Specifying \code{aggregate.index} does the same thing for replicate weight designs; a warning will be given if the original weights are not constant within levels of \code{aggregate.index}. In a model with two-stage sampling, population totals may be available for the PSUs actually sampled, but not for the whole population. In this situation, calibrating within each PSU reduces with second-stage contribution to variance. This generalizes to multistage sampling. The \code{stage} argument specifies which stage of sampling the totals refer to. Stage 0 is full population totals, stage 1 is totals for PSUs, and so on. The default, \code{stage=NULL} is interpreted as stage 0 when a single population vector is supplied and stage 1 when a list is supplied. Calibrating to PSU totals will fail (with a message about an exactly singular matrix) for PSUs that have fewer observations than the number of calibration variables. The variance in the calibration model may depend on covariates. If \code{variance=NULL} the calibration model has constant variance. If \code{variance} is not \code{NULL} it specifies a linear combination of the columns of the model matrix and the calibration variance is proportional to that linear combination. Alternatively \code{variance} can be specified as a vector of values the same length as the input data specifying a heteroskedasticity parameter for each unit. The design matrix specified by formula (after any aggregation) must be of full rank, with one exception. If the population total for a column is zero and all the observations are zero the column will be ignored. This allows the use of factors where the population happens to have no observations at some level. In a two-phase design, \code{population} may be omitted when \code{phase=2}, to specify calibration to the phase-one sample. If the two-phase design object was constructed using the more memory-efficient \code{method="approx"} argument to \code{\link{twophase}}, calibration of the first phase of sampling to the population is not supported. } \value{ A survey design object. } \references{ Breslow NE, Lumley T, Ballantyne CM, Chambless LE, Kulich M. Using the whole cohort in the analysis of case-cohort data. Am J Epidemiol. 2009;169(11):1398-1405. doi:10.1093/aje/kwp055 Deville J-C, Sarndal C-E, Sautory O (1993) Generalized Raking Procedures in Survey Sampling. JASA 88:1013-1020 Kalton G, Flores-Cervantes I (2003) "Weighting methods" J Official Stat 19(2) 81-97 Lumley T, Shaw PA, Dai JY (2011) "Connections between survey calibration estimators and semiparametric models for incomplete data" International Statistical Review. 79:200-220. (with discussion 79:221-232) Sarndal C-E, Swensson B, Wretman J. "Model Assisted Survey Sampling". Springer. 1991. Rao JNK, Yung W, Hidiroglou MA (2002) Estimating equations for the analysis of survey data using poststratification information. Sankhya 64 Series A Part 2, 364-378. Robins JM, Rotnitzky A, Zhao LP. (1994) Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89, 846-866. Vanderhoeft C (2001) Generalized Calibration at Statistics Belgium. Statistics Belgium Working Paper No 3. } \seealso{ \code{\link{postStratify}}, \code{\link{rake}} for other ways to use auxiliary information \code{\link{twophase}} and \code{vignette("epi")} for an example of calibration in two-phase designs \code{survey/tests/kalton.R} for examples replicating those in Kalton & Flores-Cervantes (2003) \code{\link{make.calfun}} for user-defined calibration distances. \code{\link{trimWeights}} to trim final weights rather than calibration adjustments. } \examples{ data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) cal_names(~stype, dclus1) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) ## For a single factor variable this is equivalent to ## postStratify (dclus1g<-calibrate(dclus1, ~stype, pop.totals)) svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) svytotal(~stype, dclus1g) ## Make weights constant within school district (dclus1agg<-calibrate(dclus1, ~stype, pop.totals, aggregate=1)) svymean(~api00, dclus1agg) svytotal(~enroll, dclus1agg) svytotal(~stype, dclus1agg) ## Now add sch.wide cal_names(~stype+sch.wide, dclus1) (dclus1g2 <- calibrate(dclus1, ~stype+sch.wide, c(pop.totals, sch.wideYes=5122))) svymean(~api00, dclus1g2) svytotal(~enroll, dclus1g2) svytotal(~stype, dclus1g2) ## Finally, calibrate on 1999 API and school type cal_names(~stype+api99, dclus1) (dclus1g3 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069))) svymean(~api00, dclus1g3) svytotal(~enroll, dclus1g3) svytotal(~stype, dclus1g3) ## Same syntax with replicate weights rclus1<-as.svrepdesign(dclus1) (rclus1g3 <- calibrate(rclus1, ~stype+api99, c(pop.totals, api99=3914069))) svymean(~api00, rclus1g3) svytotal(~enroll, rclus1g3) svytotal(~stype, rclus1g3) (rclus1agg3 <- calibrate(rclus1, ~stype+api99, c(pop.totals,api99=3914069), aggregate.index=~dnum)) svymean(~api00, rclus1agg3) svytotal(~enroll, rclus1agg3) svytotal(~stype, rclus1agg3) ### ## Bounded weights range(weights(dclus1g3)/weights(dclus1)) dclus1g3b <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069),bounds=c(0.6,1.6)) range(weights(dclus1g3b)/weights(dclus1)) svymean(~api00, dclus1g3b) svytotal(~enroll, dclus1g3b) svytotal(~stype, dclus1g3b) ## Individual boundary constraints as constant values # the first weight will be bounded at 40, the rest free to move bnds <- list( lower = rep(-Inf, nrow(apiclus1)), upper = c(40, rep(Inf, nrow(apiclus1)-1))) head(weights(dclus1g3)) dclus1g3b1 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069), bounds=bnds, bounds.const=TRUE) head(weights(dclus1g3b1)) svytotal(~api.stu, dclus1g3b1) ## trimming dclus1tr <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069), bounds=c(0.5,2), trim=c(2/3,3/2)) svymean(~api00+api99+enroll, dclus1tr) svytotal(~stype,dclus1tr) range(weights(dclus1tr)/weights(dclus1)) rclus1tr <- calibrate(rclus1, ~stype+api99, c(pop.totals, api99=3914069), bounds=c(0.5,2), trim=c(2/3,3/2)) svymean(~api00+api99+enroll, rclus1tr) svytotal(~stype,rclus1tr) ## Input in the same format as rake() for classical raking pop.table <- xtabs(~stype+sch.wide,apipop) pop.table2 <- xtabs(~stype+comp.imp,apipop) dclus1r<-rake(dclus1, list(~stype+sch.wide, ~stype+comp.imp), list(pop.table, pop.table2)) gclus1r<-calibrate(dclus1, formula=list(~stype+sch.wide, ~stype+comp.imp), population=list(pop.table, pop.table2),calfun="raking") svymean(~api00+stype, dclus1r) svymean(~api00+stype, gclus1r) ## generalised raking dclus1g3c <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069), calfun="raking") range(weights(dclus1g3c)/weights(dclus1)) (dclus1g3d <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069), calfun=cal.logit, bounds=c(0.5,2.5))) range(weights(dclus1g3d)/weights(dclus1)) ## Ratio estimators are calibration estimators dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) svytotal(~api.stu,dstrat) common<-svyratio(~api.stu, ~enroll, dstrat, separate=FALSE) predict(common, total=3811472) pop<-3811472 ## equivalent to (common) ratio estimator dstratg1<-calibrate(dstrat,~enroll-1, pop, variance=1) svytotal(~api.stu, dstratg1) # Alternatively specifying the heteroskedasticity parameters directly dstratgh <- calibrate(dstrat,~enroll-1, pop, variance=apistrat$enroll) svytotal(~api.stu, dstratgh) } \keyword{survey}% at least one, from doc/KEYWORDS \keyword{manip}% __ONLY ONE__ keyword per line survey/TODO0000644000176200001440000000407013517345033012304 0ustar liggesusersPossible additional developments in no particular order: --------------------------------------------------------- [done?] options to handle quantiles in rounded continuous data like SUDAAN does (if I can work out what it does). - Score-based confidence intervals for glms [mostly done] Rao-Scott-type tests for glms. [done] AIC/BIC - better choice of denominator df for tests [done] More general HT estimator for sampling without replacement - using joint inclusion probabilities - using population first-order inclusion probabilities and Hartley-Rao approximation - using Overton's approximation and sample inclusion probabilities - using Berger's approximation and sample inclusion probabilities. - GEE (you can do the independence working model just as another level of clustering). - an interface to the Auckland semiparametric approach to two-phase designs. - Parametric survival models (you can do these with svymle) - linear mixed models? - simpler case: multilevel model following sampling design - nested model exactly matching design - design is simpler than model - interesting case: separate model and sampling designs. - cluster sampling, so that higher-order sampling probabilities are available - more general sampling. [done] standard errors for survival curves, particularly in two-phase studies - an interface to twophase and calibrate for IPTW estimation. - Replicate weights for two-phase designs [done] and for multistage designs with large sampling fraction (Fukuoka's BBE?) [experimental] parallel processing for replicate weights, svyby(), svyrecvar()? - Gini index and other summaries of concentration and inequality. - Krista Giles' respondent-driven sampling estimators? [done] database-backed designs for replicate weights. - Multivariate statistics [done] principal components [experimental] factor analysis [done in lavaan.survey package] SEMs? ########## things to fix ################ Use naresid() to get better missing-value handling in svyglm, panel.svysmooth, etc survey/DESCRIPTION0000755000176200001440000000225314576600372013334 0ustar liggesusersPackage: survey Title: Analysis of Complex Survey Samples Description: Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Version: 4.4-2 Author: Thomas Lumley, Peter Gao, Ben Schneider Maintainer: "Thomas Lumley" License: GPL-2 | GPL-3 Depends: R (>= 4.1.0), grid, methods, Matrix, survival Imports: stats, graphics, splines, lattice, minqa, numDeriv, mitools (>= 2.4), Rcpp (>= 0.12.8) LinkingTo: Rcpp, RcppArmadillo VignetteBuilder: R.rsp Suggests: foreign, MASS, KernSmooth, hexbin, RSQLite, quantreg, parallel, CompQuadForm, DBI, AER, SUMMER (>= 1.4.0), R.rsp URL: http://r-survey.r-forge.r-project.org/survey/ NeedsCompilation: yes Packaged: 2024-03-20 00:51:41 UTC; tlum005 Repository: CRAN Date/Publication: 2024-03-20 15:30:02 UTC survey/build/0000755000176200001440000000000014576431435012722 5ustar liggesuserssurvey/build/vignette.rds0000644000176200001440000000061714576431435015265 0ustar liggesusersRMS0M[kƯAq@3  ] 3iͿE mG=tw[V0h`]WvuHysۡ7&1h]d)015x-.I-{W9ѯi?8(jR0,.T1)T ɜy 45[2ն`2Hj&t:d*GK0t?gGBeMRɱ1eC2YծGPpj 9mvكe|懼>( Uov RTl;593Mw 1Jw"Sꩬ}s/vfGKw0}o/Hsurvey/tests/0000755000176200001440000000000014571451657012770 5ustar liggesuserssurvey/tests/toy_example_for_postStratify.R0000644000176200001440000000307313743475476021112 0ustar liggesuserslibrary(survey) # Dummy data for testing nonresponse adjustments toy <- data.frame(id = 1:10, dummy = 1, class = c(rep("A", 5), rep("B", 5)), responded = c(rep(TRUE, 8), FALSE, FALSE), weight = rep(100, 10)) # With jackknife replicate weights toy_repweights <- matrix(rep(1000/9, 100), nrow = 10) diag(toy_repweights) <- rep(0, 10) # Scramble up which person is in which jackknife group toy_repweights <- toy_repweights[sample(1:10, size = 10), ] toy_design <- svrepdesign(variables = toy[, 1:4], weights = toy$weight, repweights = toy_repweights, type = "JK1", scale = 0.9) # Get the sum of the weights for the full sample poptotals <- as.data.frame(svyby(formula = ~dummy, by = ~class, design = toy_design, FUN = survey::svytotal)) poptotals$Freq <- poptotals$dummy poptotals$dummy <- NULL poptotals$se <- NULL # Adjust the weights of the responding sample to match to the full sample adjusted <- postStratify(design = subset(toy_design, responded), strata = ~class, population = poptotals) # This works for the weights... svyby(formula = ~dummy, by = ~class, design = adjusted, FUN = survey::svytotal) # ...and some, but not all, the replicate weights stopifnot(all.equal(colSums(toy_design$repweights), colSums(adjusted$repweights))) survey/tests/datos_ejemplo.rds0000755000176200001440000001307713676544610016337 0ustar liggesusers] XTUQds DLMILAD]s J2s%si}-ܲ@f()/Xay3\fyY߻P(vu ~Tԍ!t7 ] Q,NkkDzy('&YX K>a>EQ+o m/YZS3X|GD}S|G#A}Vj3q{;K}F;e1+BXA.A_B&Ţ15#k ׌kWZPG}L;?ʍy(H@g_q扨J&[0?Y< eut*}IH <4B!HT0)h73vy*XLJN_n}_Z,餕I (_5=A~GhbܾJ&yx<ڂKkkh'c¼ZbIhn=3^:n<^_!Y! 5Brp;R+ O#'$6oX/ԙ !Xd ˉz!_= cIgܟ"5IJA\?(o6 {E}IW%Ts1v*4՝a}S2i#FFPaIV)Ǫ22T9ɡʜ#23Rr( GffUCr*G~W l~R^3;ϧi|i{͵U[j-]r v}:קœ+[n-*XTzX|cK=^9枫xS_[[oSRxO?7͍־'kVnp?gϳKk/ZkߟV\|Vu|Q?hިlŸ[St1Az^3^b!y fę6G*N,TWa~*gqcgE:ayzEF`n_Y LۿIy/[ rS}z> ٥ܶ0QӲ[Χ-okـ`7yq{|8he/$Ī_MP=zDo?'a#zy9І@> gz9yj$WNf|VUD  ^*_^ؠ xv>2Omd; #Cq*3ث(4čړqy#[f Mׇ?<#DE+,Lq<Н=|ˣvN^^``BZ#bл'ԟA0yKl7}`#OQ5QzyW5>oϱOf&ۦ1iuk0qWeW$!},;3Ou+n,SP4![E{'|[(yDzaqm!@`%Y=xUnFgh87`T~8o5? 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Repeat for calibrated and raked designs fpc.R: Many ways to specify fpc kalton.R: Calibration examples from Kalton & Flore-Cervantes, J Off Stat, 19(2) 81-97 lonely.psu.R: All the lonely PSU options multistage.R: Check that a two-stage cluster sample analysis agrees with the hand-calcuated result in Sarndal et al. nwts.R: Compare results from twophase() to published two-phase case-control example nwts-cch.R: Compare results from twophase() to case-cohort analyses in survival package. pps.R: Brewer's approximation for pps without replacement quantile.R: quantile estimation on a lognormal sample rakecheck.R: check that raking by iterative post-stratification agrees with raking using calibrate() regpredict.R: ratio and regression estimation of a total. rss_scores.R: Score test example from Rao, Scott, and Skinner (1998) scoping.R: check that svyglm and svycoxph work inside functions. survcurve.R: check that svykm and predict.coxph give the same result when a data set is doubled and the two replicates of each observation are treated as a cluster. twophase.R: separately verifiable examples of twophase studies quantile-chile.R: example of interpolating for quantiles, from Chilean stats agency na_action.R: check that svyglm works with na.action=na.exclude svyivreg-var.R: check that summary.svyivreg is finding the right variances svyolr-rake-subset.R: make sure subsets of calibrated designs get handled correctly anova-svycoxph.R: check that symbolically nested and linear-algebra versions of anova.svycoxph agree survey/tests/domain.Rout.save0000644000176200001440000001553313517345033016043 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## > ## Domain means can be written as ratio estimators or as regression coefficients > ## > ## This code checks that subsetting the design object gives the same results as > ## these approaches. > ## > > > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(fpc) > dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) > dsub<-subset(dfpc,x>4) > (m1<-svymean(~x,design=dsub)) mean SE x 6.195 0.7555 > > ## These should give the same domain estimates and standard errors > (m2<-svyby(~x,~I(x>4),design=dfpc, svymean,keep.var=TRUE)) I(x > 4) x se FALSE FALSE 3.314286 0.3117042 TRUE TRUE 6.195000 0.7555129 > m3<-svyglm(x~I(x>4)+0,design=dfpc) > summary(m3) Call: svyglm(formula = x ~ I(x > 4) + 0, design = dfpc) Survey design: svydesign(id = ~psuid, strat = ~stratid, weight = ~weight, data = fpc, nest = TRUE) Coefficients: Estimate Std. Error t value Pr(>|t|) I(x > 4)FALSE 3.3143 0.3117 10.63 0.000127 *** I(x > 4)TRUE 6.1950 0.7555 8.20 0.000439 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 2.557379) Number of Fisher Scoring iterations: 2 > (m4<-svyratio(~I(x*(x>4)),~as.numeric(x>4), dfpc)) Ratio estimator: svyratio.survey.design2(~I(x * (x > 4)), ~as.numeric(x > 4), dfpc) Ratios= as.numeric(x > 4) I(x * (x > 4)) 6.195 SEs= as.numeric(x > 4) I(x * (x > 4)) 0.7555129 > stopifnot(isTRUE(all.equal(SE(m2), as.vector(SE(m3))))) > stopifnot(isTRUE(all.equal(SE(m2)[2], as.vector(SE(m4))))) > > ## with strata > data(api) > dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) > m1<-svymean(~enroll, subset(dstrat, comp.imp=="Yes")) > m2<-svyglm(enroll~comp.imp-1, dstrat) > m3<- svyratio(~I(enroll*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dstrat) > stopifnot(isTRUE(all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) > stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) > > ## with calibration > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) > (dclus1g3 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069))) 1 - level Cluster Sampling design With (15) clusters. calibrate(dclus1, ~stype + api99, c(pop.totals, api99 = 3914069)) > > m1<-svymean(~api00, subset(dclus1g3, comp.imp=="Yes")) > m3<-svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dclus1g3) > m2<-svyglm(api00~comp.imp-1, dclus1g3) > stopifnot(isTRUE( all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) > stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) > > ## with raking > pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) > pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) > dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) > m1<-svymean(~api00, subset(dclus1r, comp.imp=="Yes")) > m2<-svyglm(api00~comp.imp-1, dclus1r) > m3<-svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dclus1r) > stopifnot(isTRUE( all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) > stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) > > > > ## > ## based on bug report from Takahiro Tsuchiya for version 3.4 > ## > rei<-read.table(tmp<-textConnection( + " id N n.a h n.ah n.h sub y + 1 1 300 20 1 12 5 TRUE 1 + 2 2 300 20 1 12 5 TRUE 2 + 3 3 300 20 1 12 5 TRUE 3 + 4 4 300 20 1 12 5 TRUE 4 + 5 5 300 20 1 12 5 TRUE 5 + 6 6 300 20 1 12 5 FALSE NA + 7 7 300 20 1 12 5 FALSE NA + 8 8 300 20 1 12 5 FALSE NA + 9 9 300 20 1 12 5 FALSE NA + 10 10 300 20 1 12 5 FALSE NA + 11 11 300 20 1 12 5 FALSE NA + 12 12 300 20 1 12 5 FALSE NA + 13 13 300 20 2 8 3 TRUE 6 + 14 14 300 20 2 8 3 TRUE 7 + 15 15 300 20 2 8 3 TRUE 8 + 16 16 300 20 2 8 3 FALSE NA + 17 17 300 20 2 8 3 FALSE NA + 18 18 300 20 2 8 3 FALSE NA + 19 19 300 20 2 8 3 FALSE NA + 20 20 300 20 2 8 3 FALSE NA + "), header=TRUE) > close(tmp) > > > des.rei2 <- twophase(id=list(~id,~id), strata=list(NULL,~h), + fpc=list(~N,NULL), subset=~sub, data=rei, method="full") > tot2<- svytotal(~y, subset(des.rei2, y>3)) > > rei$y<-rei$y*(rei$y>3) > ## based on Sarndal et al (9.4.14) > rei$w.ah <- rei$n.ah / rei$n.a > a.rei <- aggregate(rei, by=list(rei$h), mean, na.rm=TRUE) > a.rei$S.ysh <- tapply(rei$y, rei$h, var, na.rm=TRUE) > a.rei$y.u <- sum(a.rei$w.ah * a.rei$y) > V <- with(a.rei, sum(N * (N-1) * ((n.ah-1)/(n.a-1) - (n.h-1)/(N-1)) * w.ah * S.ysh / n.h)) > V <- V + with(a.rei, sum(N * (N-n.a) * w.ah * (y - y.u)^2 / (n.a-1))) > > a.rei$f.h<-with(a.rei, n.h/n.ah) > Vphase2<-with(a.rei, sum(N*N*w.ah^2* ((1-f.h)/n.h) *S.ysh)) > > a.rei$f<-with(a.rei, n.a/N) > a.rei$delta.h<-with(a.rei, (1/n.h)*(n.a-n.ah)/(n.a-1)) > Vphase1<-with(a.rei, sum(N*N*((1-f)/n.a)*( w.ah*(1-delta.h)*S.ysh+ ((n.a)/(n.a-1))*w.ah*(y-y.u)^2))) > > V [1] 70761.47 > Vphase1 [1] 44325.47 > Vphase2 [1] 26436 > vcov(tot2) [,1] [1,] 70761.47 attr(,"phases") attr(,"phases")$phase1 [,1] [1,] 44325.47 attr(,"phases")$phase2 [,1] [1,] 26436 > > ## comparing to regression > reg<-svyglm(y~I(y<4), design=des.rei2) > mn<-svymean(~y, subset(des.rei2,y>3)) > all.equal(as.vector(coef(reg))[1],as.vector(coef(mn))) [1] TRUE > all.equal(as.vector(SE(reg))[1],as.vector(SE(mn))) [1] TRUE > vcov(mn) [,1] [1,] 0.3292258 attr(,"phases") attr(,"phases")$phase1 [,1] [1,] 0.1599264 attr(,"phases")$phase2 [,1] [1,] 0.1692994 > vcov(reg) (Intercept) I(y < 4)TRUE (Intercept) 0.3292258 -0.3292258 I(y < 4)TRUE -0.3292258 0.5901907 attr(,"phases") attr(,"phases")$phase1 (Intercept) I(y < 4)TRUE (Intercept) 0.1599264 -0.1599264 I(y < 4)TRUE -0.1599264 0.2588542 attr(,"phases")$phase2 (Intercept) I(y < 4)TRUE (Intercept) 0.1692994 -0.1692994 I(y < 4)TRUE -0.1692994 0.3313365 > > > proc.time() user system elapsed 1.707 0.055 1.778 survey/tests/twophase.R0000644000176200001440000001176213517345033014741 0ustar liggesuserslibrary(survey) ## two-phase simple random sampling. data(pbc, package="survival") pbc$id<-1:nrow(pbc) pbc$randomized<-with(pbc, !is.na(trt) & trt>-9) (d2pbc<-twophase(id=list(~id,~id), data=pbc, subset=~I(!randomized))) m<-svymean(~bili, d2pbc) all.equal(as.vector(coef(m)),with(pbc, mean(bili[!randomized]))) all.equal(as.vector(SE(m)), with(pbc, sd(bili[!randomized])/sqrt(sum(!randomized))), tolerance=0.002) ## two-stage sampling as two-phase data(mu284) ii<-with(mu284, c(1:15, rep(1:5,n2[1:5]-3))) mu284.1<-mu284[ii,] mu284.1$id<-1:nrow(mu284.1) mu284.1$sub<-rep(c(TRUE,FALSE),c(15,34-15)) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) ## first phase cluster sample, second phase stratified within cluster (d2mu284<-twophase(id=list(~id1,~id),strata=list(NULL,~id1), fpc=list(~n1,NULL),data=mu284.1,subset=~sub,method="approx")) (d22mu284<-twophase(id=list(~id1,~id),strata=list(NULL,~id1), fpc=list(~n1,NULL),data=mu284.1,subset=~sub,method="full")) summary(d2mu284) t1<-svytotal(~y1, dmu284) t2<-svytotal(~y1, d2mu284) t22<-svytotal(~y1,d22mu284) m1<-svymean(~y1, dmu284) m2<-svymean(~y1, d2mu284) m22<-svymean(~y1, d22mu284) all.equal(coef(t1),coef(t2)) all.equal(coef(t1),coef(t22)) all.equal(coef(m1),coef(m2)) all.equal(coef(m1),coef(m22)) all.equal(as.vector(SE(m1)),as.vector(SE(m2))) all.equal(as.vector(SE(m1)),as.vector(SE(m22))) all.equal(as.vector(SE(t1)),as.vector(SE(t2))) all.equal(as.vector(SE(t1)),as.vector(SE(t22))) ## case-cohort design ##this example requires R 2.3.1 or later for cch and data. library("survival") data(nwtco, package="survival") ## unstratified, equivalent to Lin & Ying (1993) print(dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco)) cch1<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcchs) dcchs2<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco,method="approx") cch1.2<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcchs) all.equal(coef(cch1),coef(cch1.2)) all.equal(SE(cch1),SE(cch1.2)) ## Using survival::cch subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] cch2<-cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing", robust=TRUE) print(all.equal(as.vector(coef(cch1)),as.vector(coef(cch2)))) ## cch has smaller variances by a factor of 1.0005 because ## there is a (n/(n-1)) in the survey phase1 varianace print(all.equal(as.vector(SE(cch1)), as.vector(SE(cch2)),tolerance=0.0006)) ## bug report from Takahiro Tsuchiya for version 3.4 ## We used to not match Sarndal exactly, because our old phase-one ## estimator had a small bias for finite populations rei<-read.table(tmp<-textConnection( " id N n.a h n.ah n.h sub y 1 1 300 20 1 12 5 TRUE 1 2 2 300 20 1 12 5 TRUE 2 3 3 300 20 1 12 5 TRUE 3 4 4 300 20 1 12 5 TRUE 4 5 5 300 20 1 12 5 TRUE 5 6 6 300 20 1 12 5 FALSE NA 7 7 300 20 1 12 5 FALSE NA 8 8 300 20 1 12 5 FALSE NA 9 9 300 20 1 12 5 FALSE NA 10 10 300 20 1 12 5 FALSE NA 11 11 300 20 1 12 5 FALSE NA 12 12 300 20 1 12 5 FALSE NA 13 13 300 20 2 8 3 TRUE 6 14 14 300 20 2 8 3 TRUE 7 15 15 300 20 2 8 3 TRUE 8 16 16 300 20 2 8 3 FALSE NA 17 17 300 20 2 8 3 FALSE NA 18 18 300 20 2 8 3 FALSE NA 19 19 300 20 2 8 3 FALSE NA 20 20 300 20 2 8 3 FALSE NA "), header=TRUE) close(tmp) des.rei <- twophase(id=list(~id,~id), strata=list(NULL,~h), fpc=list(~N,NULL), subset=~sub, data=rei, method="approx") tot<- svytotal(~y, des.rei) des.rei2 <- twophase(id=list(~id,~id), strata=list(NULL,~h), fpc=list(~N,NULL), subset=~sub, data=rei) tot2<- svytotal(~y, des.rei2) ## based on Sarndal et al (9.4.14) rei$w.ah <- rei$n.ah / rei$n.a a.rei <- aggregate(rei, by=list(rei$h), mean, na.rm=TRUE) a.rei$S.ysh <- tapply(rei$y, rei$h, var, na.rm=TRUE) a.rei$y.u <- sum(a.rei$w.ah * a.rei$y) V <- with(a.rei, sum(N * (N-1) * ((n.ah-1)/(n.a-1) - (n.h-1)/(N-1)) * w.ah * S.ysh / n.h)) V <- V + with(a.rei, sum(N * (N-n.a) * w.ah * (y - y.u)^2 / (n.a-1))) a.rei$f.h<-with(a.rei, n.h/n.ah) Vphase2<-with(a.rei, sum(N*N*w.ah^2* ((1-f.h)/n.h) *S.ysh)) a.rei$f<-with(a.rei, n.a/N) a.rei$delta.h<-with(a.rei, (1/n.h)*(n.a-n.ah)/(n.a-1)) Vphase1<-with(a.rei, sum(N*N*((1-f)/n.a)*( w.ah*(1-delta.h)*S.ysh+ ((n.a)/(n.a-1))*w.ah*(y-y.u)^2))) V Vphase1 Vphase2 vcov(tot) vcov(tot2) ## phase 2 identical all.equal(Vphase2,drop(attr(vcov(tot),"phases")$phase2)) all.equal(Vphase2,drop(attr(vcov(tot2),"phases")$phase2)) ## phase 1 differs by 2.6% for old twophase estimator Vphase1/attr(vcov(tot),"phases")$phase1 all.equal(Vphase1,as.vector(attr(vcov(tot2),"phases")$phase1)) survey/tests/DBIcheck.R0000644000176200001440000000354513517345033014503 0ustar liggesusers library(survey) library(RSQLite) data(api) apiclus1$api_stu<-apiclus1$api.stu apiclus1$comp_imp<-apiclus1$comp.imp dclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc,data=apiclus1) dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey")) m<-svymean(~api00+stype,dclus1) m.db<-svymean(~api00+stype, dbclus1) all.equal(coef(m),coef(m.db)) all.equal(vcov(m), vcov(m.db)) r<-svyratio(~api_stu, ~enroll, design=dclus1) r.db<-svyratio(~api_stu, ~enroll, design=dbclus1) all.equal(coef(r), coef(r.db)) all.equal(SE(r), SE(r.db)) b<-svyby(~api99+api00,~stype, design=dclus1, svymean, deff=TRUE) b.db<-svyby(~api99+api00,~stype, design=dbclus1,svymean, deff=TRUE) all.equal(coef(b), coef(b.db)) all.equal(SE(b), SE(b.db)) all.equal(deff(b), deff(b.db)) l<-svyglm(api00~api99+mobility, design=dclus1) l.db<-svyglm(api00~api99+mobility, design=dbclus1) all.equal(coef(l),coef(l.db)) all.equal(vcov(l), vcov(l.db)) dclus1<-update(dclus1, apidiff=api00-api99) dclus1<-update(dclus1, apipct= apidiff/api99) dbclus1<-update(dbclus1, apidiff=api00-api99) dbclus1<-update(dbclus1, apipct= apidiff/api99) u<-svymean(~api00+apidiff+apipct, dclus1) u.db<-svymean(~api00+apidiff+apipct, dbclus1) all.equal(u, u.db) all.equal(nrow(dclus1),nrow(dbclus1)) all.equal(nrow(subset(dclus1,stype=="E")), nrow(subset(dbclus1,stype=="E"))) ## replicate weights rclus1<-as.svrepdesign(dclus1) db_rclus1<-svrepdesign(weights=~pw, repweights="wt[1-9]+", type="JK1", scale=(1-15/757)*14/15, data="apiclus1rep",dbtype="SQLite", dbname=system.file("api.db",package="survey"),combined.weights=FALSE) m<-svymean(~api00+api99,rclus1) m.db<-svymean(~api00+api99,db_rclus1) all.equal(m,m.db) summary(db_rclus1) s<-svymean(~api00, subset(rclus1, comp_imp=="Yes")) s.db<-svymean(~api00, subset(db_rclus1, comp_imp=="Yes")) all.equal(s,s.db) survey/tests/simdata1.RData0000644000176200001440000020110013517345033015367 0ustar liggesusersxTI;%v-hp  wsjf13;t<,t[uXikۧ^&RRҡLۥuf6RgSd2E+V=ZbRXbŊ+VXbŊ+VXbŊ+VX&RZbŊ+VXbŊ+VXbŊ+wΕn׼{v]_YW[p?zBwyg֜ rm=ք P z[ :Q ~L-<ttd_C>ug6tZA@k;CkïvھAkï 3AS> =o|ZTЍ:uBgď:ou_SpHS.l.x=ȷbU=zsh|TcȦ]Fևo5;s#3)"]4@7/4@[ WnhANC Ю3@ <@ WȼFz54D=BLL$0U0S=n4D_46|-oFX֨ 8 ?66&.l i, }ײSwZ@Z.-Z"hy\ &D,h*ČV k+Ȼ5Mk|5rְY[#j+׭ўp+[0mm 6PTgAi+ mO0 qkmoNmj>j*CyW;Āvvu|t&Q>{ DƠ#G]#G; @_t@;uo u@;/ttLGGȵc#X]KvD8g:xftP'ġNE}T'wB[;5P;/0S0t7Qp} mdtN]. 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regression test for bug reported by Richard Valliant > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > s<-subbootweights(c(1,1),1:2, 50) > stopifnot(all(s$repweights$weights %in% c(0,2))) > > proc.time() user system elapsed 0.140 0.021 0.165 survey/tests/scoping.R0000755000176200001440000000110213517345033014537 0ustar liggesusers ## regression test for testing regression library(survey) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) f<-function(){ form<-acs.46~stype svyglm(formula=form, design = dstrat) } g<-function(form){ svyglm(formula=form, design = dstrat) } f() g(acs.46~stype) f<-function(){ form<-Surv(acs.46)~stype svycoxph(formula=form, design = dstrat) } g<-function(form){ svycoxph(formula=form, design = dstrat) } f() g(Surv(acs.46)~stype) ## check coxph for a single predictor svycoxph(Surv(acs.46)~api00,design=dstrat) survey/tests/na_action.R0000644000176200001440000000044714066261522015040 0ustar liggesusers## from Terry Therneau library(survey) load("naa.rda") fit1e <- svyglm( pseudo ~ age34 + ccr5 + factor(times), design= adata.s,na.action=na.exclude) fit1o <- svyglm( pseudo ~ age34 + ccr5 + factor(times), design= adata.s) all.equal(coef(fit1e),coef(fit1o)) all.equal(vcov(fit1e),vcov(fit1o)) survey/tests/degf-svrepdesign.R0000644000176200001440000000114014556563037016342 0ustar liggesuserslibrary(survey) data(scd) repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1), c(0,1,0,1,1,0)) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights) stopifnot(degf(scdrep)==3) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights, degf=4) stopifnot(degf(scdrep)==4) scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights, degf=2) stopifnot(degf(scdrep)==2) msg<-tryCatch(scdrep<-svrepdesign(data=scd, type="BRR",weights=~I(1000+0*ESA), repweights=repweights, combined.weights=FALSE,degf=10), warning=function(w) w) stopifnot(inherits(msg,"warning")) survey/tests/3stage2phase.R0000644000176200001440000000251213517345033015371 0ustar liggesusers## simulated data with three-stage sample at phase 1, SRS at phase 2 ## motivated by dietary biomarker substudy in HCHS library(survey) load("simdata1.RData") twophase.full = twophase(id=list(~block+house+ind,~1), strata=list(~strat,NULL), probs=list(~P.block+P.house+P.ind,NULL), subset=~phase2, data=simdata1,method='full') twophase.approx = twophase(id=list(~block+house+ind,~1), strata=list(~strat,NULL), probs=list(~P.block+P.house+P.ind,NULL), subset=~phase2, data=simdata1,method='approx') twophase.rep = twophase(id=list(~block,~1), strata=list(~strat,NULL), probs=list(~I(P.block*P.house*P.ind),NULL), subset=~phase2, data=simdata1,method='full') twophase.repapprox = twophase(id=list(~block,~1), strata=list(~strat,NULL), probs=list(~I(P.block*P.house*P.ind),NULL), subset=~phase2, data=simdata1,method='approx') svymean(~age, twophase.full) svymean(~age, twophase.approx) svymean(~age, twophase.rep) svymean(~age, twophase.repapprox) survey/tests/svycontrast.R0000644000176200001440000000072514251531666015510 0ustar liggesuserslibrary(survey) data(api) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) rclus2<-as.svrepdesign(dclus2) model<-svyglm(formula = api00 ~ ell + meals + mobility, design = rclus2) svycontrast(model, c(ell=1,meals=0)) a<-svycontrast(model, c(0,1,0,0)) model2<-svyglm(formula = api00 ~ ell + meals + mobility, design = rclus2,return.replicates=TRUE) svycontrast(model2, c(ell=1,meals=0)) a2<-svycontrast(model2, c(0,1,0,0)) stopifnot(!is.null(a2$replicates)) survey/tests/newquantile.R0000644000176200001440000000052413706504553015441 0ustar liggesusers## quantiles with equal weights library(survey) data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) for(i in 1:9){ print(i) all.equal( as.vector(coef(svyquantile(~ell, dclus1, c(0.2,0.5,0.9), qrule=paste0("hf",i)))), as.vector(quantile(apiclus1$ell, c(0.2,0.5,0.9), type=i)) ) } survey/tests/caleg.R0000644000176200001440000001023513517345033014154 0ustar liggesusers## ## Calibration examples ## ## Example of calibration to first-stage clusters library(survey) data(api) clusters<-table(apiclus2$dnum) clusters<-clusters[clusters>1 & names(clusters)!="639"] apiclus2a<-subset(apiclus2, dnum %in% as.numeric(names(clusters))) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2a) popclusters<-subset(apipop, dnum %in% as.numeric(names(clusters))) pop<-lapply(as.numeric(names(clusters)), function(cluster) { colSums(model.matrix(~api99, model.frame(~api99, subset(popclusters, dnum %in% cluster))))}) names(pop)<-names(clusters) dclus2g<-calibrate(dclus2, ~api99, pop,stage=1) svymean(~api99, dclus2) svymean(~api99, dclus2g) round(svyby(~api99, ~dnum, design=dclus2, svymean),4) round(svyby(~api99, ~dnum, design=dclus2g, svymean),4) ## Averaging to first stage dclus1<- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) pop<-colSums(cbind(1,apipop$enroll),na.rm=TRUE) dclus1g<-calibrate(dclus1, ~enroll, pop, aggregate=1) svytotal(~enroll,dclus1g) svytotal(~api.stu,dclus1g) #variation within clusters should be zero all.equal(0, max(ave(weights(dclus1g),dclus1g$cluster,FUN=var),na.rm=TRUE)) ##bounded weights dclus1g<-calibrate(dclus1, ~enroll, pop) range(weights(dclus1g)/weights(dclus1)) dclus1gb<-calibrate(dclus1, ~enroll, pop, bounds=c(.6,1.5)) range(weights(dclus1gb)/weights(dclus1)) ## Ratio estimators dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) svytotal(~api.stu,dstrat) common<-svyratio(~api.stu, ~enroll, dstrat, separate=FALSE) total.enroll<-sum(apipop$enroll,na.rm=TRUE) predict(common, total=total.enroll) dstratg<-calibrate(dstrat,~enroll-1, total.enroll, variance=1) svytotal(~api.stu, dstratg) ## postStratify vs calibrate in stratified sample (Ben French) set.seed(17) dat<-data.frame(y=rep(0:1,each=100),x=rnorm(200)+2*rep(0:1,each=100), z=rbinom(200,1,.2), fpc=rep(c(100,10000),each=100)) dat$w<-ifelse(dat$y,dat$z,1-dat$z) popw<-data.frame(w=c("0","1"), Freq=c(2000,8000)) des<-svydesign(id=~1,fpc=~fpc, data=dat,strata=~y) postStratify(des,~w,popw)->dps dcal<-calibrate(des,~factor(w), pop=c(10000,8000)) all.equal(SE(svymean(~x,dcal)),SE(svymean(~x,dps))) ## missing data in calibrated design dps$variables$z[1]<-NA summary(svyglm(y~z+x,design=dps,family=quasibinomial)) ## Ratio estimator using the heteroskedasticity parameter (Daniel Oehm) # should match the ratio estmate above dstratgh <- calibrate(dstrat,~enroll-1, total.enroll, variance=apistrat$enroll) svytotal(~api.stu, dstratgh) ## individual boundary constraints as multiplicative values (Daniel Oehm) bnds <- list( lower = c(1, 1, rep(-Inf, nrow(apistrat)-2)), upper = c(1, 1, rep(Inf, nrow(apistrat)-2))) # the first two weights will remain unchanged the others are free to move lapply(bnds, head) dstratg1<-calibrate(dstrat, ~enroll-1, total.enroll, bounds = bnds, variance=apistrat$enroll) svytotal(~api.stu, dstratg1) head(weights(dstrat)) head(weights(dstratg1)) all.equal(weights(dstrat)[1:2], weights(dstratg1)[1:2]) ## individual boundary constraints as constant values (Daniel Oehm) bnds <- list( lower = c(44.21, 44.21, rep(-Inf, nrow(apistrat)-2)), upper = c(44.21, 44.21, rep(Inf, nrow(apistrat)-2))) # the first two weights will remain unchanged lapply(bnds, head) dstratg2<-calibrate(dstrat, ~enroll-1, total.enroll, bounds = bnds, bounds.const = TRUE, variance=apistrat$enroll) svytotal(~api.stu, dstratg2) head(weights(dstrat)) head(weights(dstratg2)) all.equal(round(weights(dstrat)[1:2], 8), round(weights(dstratg2)[1:2]), 8) # minor rounding error but all good # sparse matrix support (Daniel Oehm) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) dclus1g<-calibrate(dclus1, ~stype, pop.totals) svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) dclus1g<-calibrate(dclus1, ~stype, pop.totals, sparse = TRUE) svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) dclus1g<-calibrate(dclus1, ~stype, pop.totals, sparse = TRUE, calfun = "raking") svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) survey/tests/quantile.R0000644000176200001440000000262613707710052014726 0ustar liggesuserslibrary(survey) set.seed(42) df<-data.frame(x=exp(rnorm(1000))) df$y<-round(df$x,1) ddf<-svydesign(id=~1,data=df) rdf<-as.svrepdesign(ddf) SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald") oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE) oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald",df=Inf) oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE, df=Inf) df<-data.frame(x=exp(rnorm(20))) df$y<-round(df$x,1) ddf<-svydesign(id=~1,data=df) rdf<-as.svrepdesign(ddf) SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald") oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE) oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald",df=Inf) oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE, df=Inf) survey/tests/rakecheck.R0000644000176200001440000000305113517345033015017 0ustar liggesuserslibrary(survey) data(api) dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1 <- as.svrepdesign(dclus1) ## population marginal totals for each stratum pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) rclus1r <- rake(rclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) svymean(~api00, rclus1r) svytotal(~enroll, rclus1r) ff<-~stype+sch.wide poptotals<-colSums(model.matrix(ff,model.frame(ff,apipop))) rclus1g<-calibrate(rclus1, ~stype+sch.wide, poptotals,calfun="raking") svymean(~api00,rclus1g) svytotal(~enroll,rclus1g) summary(weights(rclus1g)/weights(rclus1r)) ## Do it for a design without replicate weights dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) svymean(~api00, dclus1r) svytotal(~enroll, dclus1r) dclus1g<-calibrate(dclus1, ~stype+sch.wide, poptotals,calfun="raking") svymean(~api00,dclus1g) svytotal(~enroll,dclus1g) summary(weights(dclus1g)/weights(dclus1r)) ## Example of raking with partial joint distributions pop.table <- xtabs(~stype+sch.wide,apipop) pop.imp<-data.frame(comp.imp=c("No","Yes"),Freq=c(1712,4482)) dclus1r2<-rake(dclus1, list(~stype+sch.wide, ~comp.imp), list(pop.table, pop.imp)) svymean(~api00, dclus1r2) ff1 <-~stype*sch.wide+comp.imp poptotals1<-colSums(model.matrix(ff1,model.frame(ff1,apipop))) dclus1g2<-calibrate(dclus1, ~stype*sch.wide+comp.imp, poptotals1, calfun="raking") svymean(~api00, dclus1g2) summary(weights(dclus1r2)/weights(dclus1g2)) survey/tests/svyby-strings.R0000644000176200001440000000052514221522101015727 0ustar liggesusers ## check that stringsAsFactors fixes the string levels problem data(api, package = "survey") des <- survey::svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) est0 <- survey::svyby(design=des, formula=~cname, by=~both, FUN=survey::svymean, keep.var=TRUE, stringsAsFactors=TRUE) stopifnot(isTRUE(all(dim(est0)==c(2,23)))) survey/tests/anova-svycoxph.R0000644000176200001440000000026714207320246016066 0ustar liggesusers library(survey) example(svycoxph, ask=FALSE) m<-update(model, .~.+I(protime^2)) a<-anova(m,model) b<-anova(m, model,force=TRUE) stopifnot(isTRUE(all.equal(b[2:6],a[c(3,4,6,7,8)]))) survey/tests/poisson.R0000644000176200001440000000063314073741722014577 0ustar liggesusers## check poisson sampling library(survey) data(api) set.seed(2021-7-15) apipop$prob<-apipop$api00/1000 insample<-rbinom(nrow(apipop),1,apipop$prob) apipois<-apipop[insample,] des<-svydesign(id=~1, prob=~prob, pps=poisson_sampling(apipois$prob), data=apipois) stopifnot(isTRUE(all.equal( as.vector(SE(svytotal(~api00,design=des))), as.vector(sqrt(sum( (apipois$api00*weights(des))^2*(1-apipois$prob)))) ))) survey/tests/fpc.R0000644000176200001440000000011613517345033013646 0ustar liggesuserslibrary(survey) ## check many permutations of fpc specification example(fpc) survey/tests/kalton.R0000644000176200001440000000270413517345033014373 0ustar liggesuserslibrary(survey) ab<-expand.grid(a=factor(1:4),b=factor(1:3)) kaltonsample<-ab[rep(1:12,c(20,50,100,30,40,140,50,100,40,310,50,70)),] kaltonpop<-ab[rep(1:12,c(80,60,170,55,40,150,60,165,55,340,200,125)),] jointpop<-colSums(model.matrix(~a*b,kaltonpop)) marginalpop<-colSums(model.matrix(~a+b,kaltonpop)) gregpop<-colSums(model.matrix(~as.numeric(a)+as.numeric(b),kaltonpop)) dkalton<-svydesign(id=~1,data=kaltonsample) dps<-postStratify(dkalton,~a+b,xtabs(~a+b,kaltonpop)) drake<-rake(dkalton, list(~a,~b),list(xtabs(~a,kaltonpop),xtabs(~b,kaltonpop)),control=list(epsilon=0.0001)) dcalps<-calibrate(dkalton, ~a*b, jointpop) dcalrake<-calibrate(dkalton,~a+b, marginalpop, calfun="raking") dlinear<-calibrate(dkalton, ~a+b, marginalpop) dtrunclinear<-calibrate(dkalton, ~a+b, marginalpop,bounds=c(0.5,2.2)) dlogit<-calibrate(dkalton, ~a+b, marginalpop,bounds=c(0.5,2.2),calfun="logit") dgreg<-calibrate(dkalton,~as.numeric(a)+as.numeric(b), gregpop) #table A round(svytable(~a+b,dps)/xtabs(~a+b,kaltonsample),2) round(svytable(~a+b,dcalps)/xtabs(~a+b,kaltonsample),2) #table B round(svytable(~a+b,drake)/xtabs(~a+b,kaltonsample),2) round(svytable(~a+b,dcalrake)/xtabs(~a+b,kaltonsample),2) #table C round(svytable(~a+b,dlinear)/xtabs(~a+b,kaltonsample),2) #table D round(svytable(~a+b,dgreg)/xtabs(~a+b,kaltonsample),2) #table G round(svytable(~a+b,dlogit)/xtabs(~a+b,kaltonsample),2) #table G round(svytable(~a+b,dtrunclinear)/xtabs(~a+b,kaltonsample),2) survey/tests/quantile.Rout.save0000644000176200001440000001240713707710052016411 0ustar liggesusers R version 4.0.2 (2020-06-22) -- "Taking Off Again" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin17.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: ‘survey’ The following object is masked from ‘package:graphics’: dotchart > set.seed(42) > > df<-data.frame(x=exp(rnorm(1000))) > df$y<-round(df$x,1) > ddf<-svydesign(id=~1,data=df) Warning message: In svydesign.default(id = ~1, data = df) : No weights or probabilities supplied, assuming equal probability > rdf<-as.svrepdesign(ddf) > > SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) 0.01 0.1 0.5 0.9 0.99 0.01543792 0.01264453 0.03386225 0.16131948 2.10605799 > > SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) x q0.01 0.01554946 q0.1 0.01425828 q0.5 0.03428012 q0.9 0.16457488 q0.99 1.88672412 > > SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) 0.01 0.1 0.5 0.9 0.99 0.01545209 0.01265608 0.03388011 0.16145776 2.10061576 > > SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) x q0.01 0.01556240 q0.1 0.01420086 q0.5 0.03430175 q0.9 0.16461019 q0.99 1.88818131 > > > oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald") $quantiles 0.01 0.1 0.5 0.9 0.99 y 0.02352941 0.2230769 0.9340909 3.55 9.4 $CIs , , y 0.01 0.1 0.5 0.9 0.99 (lower 0.01594200 0.2020115 0.8578143 3.207543 7.568142 upper) 0.03287947 0.2467045 1.0059815 3.862241 14.978632 > > oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE) Statistic: y q0.01 0.1 q0.1 0.3 q0.5 1.0 q0.9 3.6 q0.99 9.4 SE: y q0.01 0.02547977 q0.1 0.02547977 q0.5 0.02547977 q0.9 0.15287859 q0.99 1.88302540 > > oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald",df=Inf) $quantiles 0.01 0.1 0.5 0.9 0.99 y 0.02352941 0.2230769 0.9340909 3.55 9.4 $CIs , , y 0.01 0.1 0.5 0.9 0.99 (lower 0.01594200 0.2020115 0.8578143 3.207543 7.568142 upper) 0.03287947 0.2467045 1.0059815 3.862241 14.978632 > > oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE, df=Inf) Statistic: y q0.01 0.1 q0.1 0.3 q0.5 1.0 q0.9 3.6 q0.99 9.4 SE: y q0.01 0.02551067 q0.1 0.02551067 q0.5 0.02551067 q0.9 0.15306404 q0.99 1.88450896 > > > > df<-data.frame(x=exp(rnorm(20))) > df$y<-round(df$x,1) > ddf<-svydesign(id=~1,data=df) Warning message: In svydesign.default(id = ~1, data = df) : No weights or probabilities supplied, assuming equal probability > rdf<-as.svrepdesign(ddf) > SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) 0.01 0.1 0.5 0.9 0.99 0.03576771 0.07329496 0.30149917 2.03440784 1.88468597 > > SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE)) x q0.01 0.00000000 q0.1 0.04598541 q0.5 0.25943731 q0.9 2.12343073 q0.99 1.91998924 > > SE(oldsvyquantile(~x,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) 0.01 0.1 0.5 0.9 0.99 0.03637398 0.07470310 0.29938115 2.16381289 2.00708994 > > SE(oldsvyquantile(~x,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,df=Inf)) x q0.01 0.00000000 q0.1 0.04732818 q0.5 0.24957966 q0.9 2.25211815 q0.99 2.04043154 > > > oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald") $quantiles 0.01 0.1 0.5 0.9 0.99 y 0.1 0.15 0.75 2.4 8.68 $CIs , , y 0.01 0.1 0.5 0.9 0.99 (lower 0.100000 0.1000000 0.3674054 1.352279 0.5674674 upper) 1.132533 0.3464541 1.6303784 8.473337 10.2000000 > > oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE) Statistic: y q0.01 0.10 q0.1 0.20 q0.5 0.80 q0.9 2.40 q0.99 8.68 SE: y q0.01 0.00000000 q0.1 0.02611112 q0.5 0.26542051 q0.9 2.10908603 q0.99 1.91777710 > > oldsvyquantile(~y,ddf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE,ties="rounded",interval.type="betaWald",df=Inf) $quantiles 0.01 0.1 0.5 0.9 0.99 y 0.1 0.15 0.75 2.4 8.68 $CIs , , y 0.01 0.1 0.5 0.9 0.99 (lower 0.100000 0.1000000 0.3674054 1.352279 0.5674674 upper) 1.132533 0.3464541 1.6303784 8.473337 10.2000000 > > oldsvyquantile(~y,rdf, c(0.01,0.1,0.5,0.9,0.99),ci=TRUE, df=Inf) Statistic: y q0.01 0.10 q0.1 0.20 q0.5 0.80 q0.9 2.40 q0.99 8.68 SE: y q0.01 0.00000000 q0.1 0.02551067 q0.5 0.25244616 q0.9 2.23558589 q0.99 2.03881114 > > proc.time() user system elapsed 1.791 0.148 2.936 survey/tests/nwts.rda0000644000176200001440000000261513517345033014444 0ustar liggesusersRDX2 X  nwts    S03% @3< [ud<  !9) names stage instit histol control case class data.frame row.names 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 nwtsb    sVO @3< [ud<  !9) stage instit histol control case data.frame 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16survey/tests/naa.rda0000644000176200001440000004575314066261522014222 0ustar liggesusers <]ǾTP)9EMD+al!iע-i(I((Tٲ(mJqw<}?s>kw-:Ι6F6$F?GUuMH$Nv 6'n{G{{=oF| w" weW88xxDD$$dd!(!(# "!#h h"h!GF0AAAAa2!1Ti&f "D0 @9s!GX`ab%K,!X!,GXajk56k!G؀a=fG'2g\<<#x!x# @E؉ a7#@G8pBQcN D8p!!! !! 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You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > ## lonely PSUs by design > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(api) > ## not certainty PSUs by fpc > ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = apiclus1) > summary(ds) Stratified Independent Sampling design (with replacement) svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = apiclus1) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02954 0.02954 0.02954 0.02954 0.02954 0.02954 Stratum Sizes: 61 135 178 197 255 406 413 437 448 510 568 637 716 778 815 obs 13 34 4 13 16 2 1 4 12 21 9 11 37 2 4 design.PSU 13 34 4 13 16 2 1 4 12 21 9 11 37 2 4 actual.PSU 13 34 4 13 16 2 1 4 12 21 9 11 37 2 4 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" > > options(survey.lonely.psu="fail") > try(svymean(~api00,ds)) Error in onestrat(`attr<-`(x[index, , drop = FALSE], "recentering", recentering), : Stratum (413) has only one PSU at stage 1 > try(svymean(~api00, as.svrepdesign(ds))) Error in jknweights(design$strata[, 1], design$cluster[, 1], fpc = fpc, : Stratum413has only one PSU > options(survey.lonely.psu="remove") > svymean(~api00,ds) mean SE api00 644.17 5.8058 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.8058 > options(survey.lonely.psu="certainty") > svymean(~api00,ds) mean SE api00 644.17 5.8058 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.8058 > options(survey.lonely.psu="adjust") > svymean(~api00,ds) mean SE api00 644.17 5.8281 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.8267 > options(survey.lonely.psu="average") > svymean(~api00,ds) mean SE api00 644.17 6.0096 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.8217 > > ## fpc specified > fpc<-ifelse(apiclus1$dnum==413, 1,1000) > ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = apiclus1,fpc=fpc) > summary(ds) Stratified Independent Sampling design svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = apiclus1, fpc = fpc) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02954 0.02954 0.02954 0.02954 0.02954 0.02954 Stratum Sizes: 61 135 178 197 255 406 413 437 448 510 568 637 716 778 815 obs 13 34 4 13 16 2 1 4 12 21 9 11 37 2 4 design.PSU 13 34 4 13 16 2 1 4 12 21 9 11 37 2 4 actual.PSU 13 34 4 13 16 2 1 4 12 21 9 11 37 2 4 Population stratum sizes (PSUs): 135 178 197 255 406 413 437 448 510 568 61 637 716 778 815 1000 1000 1000 1000 1000 1 1000 1000 1000 1000 1000 1000 1000 1000 1000 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" > > options(survey.lonely.psu="fail") > try(svymean(~api00,ds)) mean SE api00 644.17 5.7344 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.7344 > options(survey.lonely.psu="remove") > svymean(~api00,ds) mean SE api00 644.17 5.7344 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.7344 > options(survey.lonely.psu="certainty") > svymean(~api00,ds) mean SE api00 644.17 5.7344 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.7344 > options(survey.lonely.psu="adjust") > svymean(~api00,ds) mean SE api00 644.17 5.7344 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.7344 > options(survey.lonely.psu="average") > svymean(~api00,ds) mean SE api00 644.17 5.7344 > svymean(~api00, as.svrepdesign(ds)) mean SE api00 644.17 5.7501 > > rs<-as.svrepdesign(ds) > svytotal(~api00,rs) total SE api00 3989986 35616 > SE(svytotal(~api00,subset(rs, dnum==413)))==0 [1] TRUE > > ## lonely PSUs after subsetting > ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = subset(apiclus1,dnum !=413)) > ds1<-ds[-31,] > summary(ds1) Stratified Independent Sampling design (with replacement) svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = subset(apiclus1, dnum != 413)) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02954 0.02954 0.02954 0.02954 0.02954 0.02954 Stratum Sizes: 61 135 178 197 255 406 437 448 510 568 637 716 778 815 obs 13 34 4 13 16 1 4 12 21 9 11 37 2 4 design.PSU 13 34 4 13 16 2 4 12 21 9 11 37 2 4 actual.PSU 13 34 4 13 16 1 4 12 21 9 11 37 2 4 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" > > options(survey.lonely.psu="fail") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 > options(survey.lonely.psu="remove") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 > options(survey.lonely.psu="certainty") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 > options(survey.lonely.psu="adjust") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 > options(survey.lonely.psu="average") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 > > ## with adjustment > options(survey.adjust.domain.lonely=TRUE) > ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = subset(apiclus1,dnum !=413)) > ds1<-ds[-31,] > summary(ds1) Stratified Independent Sampling design (with replacement) svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = subset(apiclus1, dnum != 413)) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02954 0.02954 0.02954 0.02954 0.02954 0.02954 Stratum Sizes: 61 135 178 197 255 406 437 448 510 568 637 716 778 815 obs 13 34 4 13 16 1 4 12 21 9 11 37 2 4 design.PSU 13 34 4 13 16 2 4 12 21 9 11 37 2 4 actual.PSU 13 34 4 13 16 1 4 12 21 9 11 37 2 4 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" > > options(survey.lonely.psu="fail") > try(svymean(~api00,ds1)) mean SE api00 645.14 5.8909 Warning message: In onestrat(`attr<-`(x[index, , drop = FALSE], "recentering", recentering), : Stratum (406) has only one PSU at stage 1 > options(survey.lonely.psu="remove") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 Warning message: In onestrat(`attr<-`(x[index, , drop = FALSE], "recentering", recentering), : Stratum (406) has only one PSU at stage 1 > options(survey.lonely.psu="certainty") > svymean(~api00,ds1) mean SE api00 645.14 5.8909 Warning message: In onestrat(`attr<-`(x[index, , drop = FALSE], "recentering", recentering), : Stratum (406) has only one PSU at stage 1 > options(survey.lonely.psu="adjust") > svymean(~api00,ds1) mean SE api00 645.14 5.9119 Warning message: In onestrat(`attr<-`(x[index, , drop = FALSE], "recentering", recentering), : Stratum (406) has only one PSU at stage 1 > options(survey.lonely.psu="average") > svymean(~api00,ds1) mean SE api00 645.14 6.0914 Warning message: In onestrat(`attr<-`(x[index, , drop = FALSE], "recentering", recentering), : Stratum (406) has only one PSU at stage 1 > > ## checks for `svytotal()` > > df_w_singleton <- data.frame( + Stratum = c(1, 1, 2, 2, 3), + PSU = c(1, 2, 3, 4, 5), + Design_Weight = c(10.5, 10.5, 20, 20, 15), + Sex = c("M", "F", "F", "M", "M"), + Age = c(30.5, 40.5, 35, 52, 44), + Height = c(6.2, 5.0, 5.3, 5.7, 5.5) + ) > > design_w_singleton <- survey::svydesign( + data = df_w_singleton, + ids = ~ PSU, strata = ~ Stratum, + weights = ~ Design_Weight + ) > > options("survey.lonely.psu" = "remove") > > stopifnot(all.equal( + target = 126625, + current = as.numeric( + vcov(svytotal(x = ~ Age, design = design_w_singleton)) + ) + )) > > options("survey.lonely.psu" = "certainty") > > stopifnot(all.equal( + target = 126625, + current = as.numeric( + vcov(svytotal(x = ~ Age, design = design_w_singleton)) + ) + )) > > options("survey.lonely.psu" = "adjust") > > stopifnot(all.equal( + target = 127579.8, + current = as.numeric( + vcov(svytotal(x = ~ Age, design = design_w_singleton)) + ), + scale = 127579.8, tolerance = 0.000001 + )) > > proc.time() user system elapsed 0.570 0.040 0.608 survey/tests/nwtco-subcohort.rda0000644000176200001440000000166513517345033016615 0ustar liggesusersY9A,]x Xkx/@ƠVUfFDF{22./߿rbwϻo?>}~o~5z\պި}ݵe +sZ}=}#|vd߳+nh{WשЕv*v+}j|OٸE! (B95_U:=ۅgC}|b y@5ĭw+6\⻋w}PQ9(~?,q+]xY;876u翳[ydtuXXP}qv®OU{Yzy]GVݕ(^4*}uY/;djvzY@=q;ہ+@ VՏo(\ƫ:_fկ,XЅ6ounj]sdt|A(;Ϲ+*M>Q7n~Y}WB7<AUi]|UV?.}>xf>5W>ۥOq]__ˢ:tٞl/;Z]S͗,ނ͋ѝfDmw^Qq#]Γh9<>Ghu>fCclTMvXۮsUt+?<EW#(Gr*OPq?vՇj|/\ջү\Vxut}@wi֎<^]o+ޣkE|bWW\gQ·]y7x(\xS5łVÍGsW,Ab?H~survey/tests/deff.Rout.save0000644000176200001440000000433313517345033015474 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## from Takahiro Tsuchiya > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > kigyo<-read.table(tmp<-textConnection(" obs uriage srs.w pps.w + 1 1 15 100 20 + 2 2 143 100 200 + 3 3 21 100 11 + 4 4 51 100 25 + 5 5 337 100 550 + 6 6 50 100 30 + 7 7 274 100 250 + 8 8 145 100 100 + 9 9 15 100 10 + 10 10 86 100 55 + ",open="r"),header=TRUE) > close(tmp) > des.srs <- svydesign(ids=~1, weights=~srs.w, data=kigyo) > (res.srs <- svymean(~uriage, des.srs, deff=TRUE)) mean SE DEff uriage 113.700 35.626 1.0101 > (SE(res.srs)^2) / ((1-10/1000) * coef(svyvar(~uriage, des.srs)) / 10) uriage uriage 1.010101 > > (tres.srs <- svytotal(~uriage, des.srs, deff=TRUE)) total SE DEff uriage 113700 35626 1.0101 > (SE(tres.srs)^2) / (1000^2 * (1-10/1000) * coef(svyvar(~uriage, des.srs)) / 10) uriage uriage 1.010101 > > > des.pps <- svydesign(ids=~1, weights=~pps.w, data=kigyo) > (res.pps <- svymean(~uriage, des.pps, deff='replace')) mean SE DEff uriage 243.914 48.752 1.9741 > (SE(res.pps)^2) / (coef(svyvar(~uriage, des.pps)) / 10) uriage uriage 1.974067 > (tres.pps <- svytotal(~uriage, des.pps, deff='replace')) total SE DEff uriage 305136 184965 18.157 > (N.hat <- sum(weights(des.pps))) [1] 1251 > (SE(tres.pps)^2) / (N.hat^2 * coef(svyvar(~uriage, des.pps)) / 10) uriage uriage 18.15669 > > proc.time() user system elapsed 0.163 0.021 0.189 survey/tests/regpredict.R0000644000176200001440000000305313517345033015231 0ustar liggesuserslibrary(survey) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) ## regression estimator of total, three ways pop<-data.frame(enroll=sum(apipop$enroll, na.rm=TRUE)) npop <- sum(!is.na(apipop$enroll)) api.reg <- svyglm(api.stu~enroll, design=dstrat) a <- predict(api.reg, newdata=pop, total=npop) b <- svytotal(~api.stu, calibrate(dstrat, ~enroll, pop=c(npop, pop$enroll))) all.equal(as.vector(coef(a)),as.vector(coef(b))) all.equal(as.vector(SE(a)), as.vector(SE(b))) if(!is.null(getOption("DEBUG"))){ ## uses 6194x6194 matrix d <- predict(api.reg, newdata=na.omit(apipop[,"enroll",drop=FALSE])) all.equal(as.vector(coef(a)), as.vector(sum(coef(d)))) all.equal(as.vector(SE(a)), as.vector(sqrt(sum(vcov(d))))) } ## classical ratio estimator, four ways. api.reg2 <- svyglm(api.stu~enroll-1, design=dstrat, family=quasi(link="identity", var="mu")) a <- predict(api.reg2, newdata=pop, total=npop) b <- svytotal(~api.stu, calibrate(dstrat, ~enroll-1, pop= pop$enroll, variance=2)) e <- predict(svyratio(~api.stu, ~enroll, dstrat),total=pop$enroll) all.equal(as.vector(coef(a)),as.vector(coef(b))) all.equal(as.vector(SE(a)), as.vector(SE(b))) all.equal(as.vector(coef(a)),as.vector(e$total)) all.equal(as.vector(SE(a)), as.vector(e$se)) if(!is.null(getOption("DEBUG"))){## uses 6194x6194 matrix d <- predict(api.reg2, newdata=na.omit(apipop[,"enroll",drop=FALSE])) all.equal(as.vector(coef(a)), as.vector(sum(coef(d)))) all.equal(as.vector(SE(a)), as.vector(sqrt(sum(vcov(d))))) } survey/tests/survcurve.Rout.save0000644000176200001440000000511213517345033016630 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > library(survival) Loading required package: splines > > pbc2<-rbind(pbc,pbc) > pbc2$id<-rep(1:418,2) > > dpbc1<-svydesign(id=~1, data=pbc) Warning message: In svydesign.default(id = ~1, data = pbc) : No weights or probabilities supplied, assuming equal probability > dpbc2<-svydesign(id=~id, data=pbc2) Warning message: In svydesign.default(id = ~id, data = pbc2) : No weights or probabilities supplied, assuming equal probability > > s1<-svykm(Surv(time,status>0)~1, subset(dpbc1, bili>6), se=TRUE) > s2<-svykm(Surv(time,status>0)~1, subset(dpbc2, bili>6), se=TRUE) > > (c1<-confint(s1,(1:5)*365)) 0.025 0.975 365 0.6446215 0.8594153 730 0.5410938 0.7766848 1095 0.2683127 0.5103356 1460 0.1444731 0.3722001 1825 0.1009672 0.3204713 > (c2<-confint(s2,(1:5)*365)) 0.025 0.975 365 0.6446215 0.8594153 730 0.5410938 0.7766848 1095 0.2683127 0.5103356 1460 0.1444731 0.3722001 1825 0.1009672 0.3204713 > all.equal(c1, c2) [1] TRUE > > m1<-svycoxph(Surv(time,status>0)~log(bili), design=dpbc1) > m2<-svycoxph(Surv(time,status>0)~log(bili), design=dpbc2) > > d<-data.frame(bili=c(5,10)) > p1<-predict(m1, se=TRUE, newdata=d,type="curve") > p2<-predict(m2, se=TRUE, newdata=d,type="curve") > > (pc1<-confint(p1[[1]],(1:5)*365)) 0.025 0.975 365 0.8410027 0.9266263 730 0.7371114 0.8548312 1095 0.5517779 0.7018583 1460 0.4335073 0.5992819 1825 0.3260899 0.5046241 > (pc2<-confint(p2[[1]],(1:5)*365)) 0.025 0.975 365 0.8409490 0.9267054 730 0.7370152 0.8549432 1095 0.5515848 0.7019513 1460 0.4332252 0.5992968 1825 0.3257172 0.5045795 > all.equal(pc1, pc2) [1] "Mean relative difference: 0.0002070722" > > (q1<-quantile(p1[[2]])) 0.75 0.5 0.25 489 930 1492 > (q2<-quantile(p2[[2]])) 0.75 0.5 0.25 489 930 1492 > all.equal(q1,q2) [1] TRUE > > proc.time() user system elapsed 3.410 0.099 3.519 survey/tests/lonely.psu.R0000755000176200001440000000620114462643117015216 0ustar liggesusers ## lonely PSUs by design library(survey) data(api) ## not certainty PSUs by fpc ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = apiclus1) summary(ds) options(survey.lonely.psu="fail") try(svymean(~api00,ds)) try(svymean(~api00, as.svrepdesign(ds))) options(survey.lonely.psu="remove") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="certainty") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="adjust") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="average") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) ## fpc specified fpc<-ifelse(apiclus1$dnum==413, 1,1000) ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = apiclus1,fpc=fpc) summary(ds) options(survey.lonely.psu="fail") try(svymean(~api00,ds)) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="remove") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="certainty") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="adjust") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) options(survey.lonely.psu="average") svymean(~api00,ds) svymean(~api00, as.svrepdesign(ds)) rs<-as.svrepdesign(ds) svytotal(~api00,rs) SE(svytotal(~api00,subset(rs, dnum==413)))==0 ## lonely PSUs after subsetting ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = subset(apiclus1,dnum !=413)) ds1<-ds[-31,] summary(ds1) options(survey.lonely.psu="fail") svymean(~api00,ds1) options(survey.lonely.psu="remove") svymean(~api00,ds1) options(survey.lonely.psu="certainty") svymean(~api00,ds1) options(survey.lonely.psu="adjust") svymean(~api00,ds1) options(survey.lonely.psu="average") svymean(~api00,ds1) ## with adjustment options(survey.adjust.domain.lonely=TRUE) ds<-svydesign(id = ~1, weights = ~pw, strata = ~dnum, data = subset(apiclus1,dnum !=413)) ds1<-ds[-31,] summary(ds1) options(survey.lonely.psu="fail") try(svymean(~api00,ds1)) options(survey.lonely.psu="remove") svymean(~api00,ds1) options(survey.lonely.psu="certainty") svymean(~api00,ds1) options(survey.lonely.psu="adjust") svymean(~api00,ds1) options(survey.lonely.psu="average") svymean(~api00,ds1) ## checks for `svytotal()` df_w_singleton <- data.frame( Stratum = c(1, 1, 2, 2, 3), PSU = c(1, 2, 3, 4, 5), Design_Weight = c(10.5, 10.5, 20, 20, 15), Sex = c("M", "F", "F", "M", "M"), Age = c(30.5, 40.5, 35, 52, 44), Height = c(6.2, 5.0, 5.3, 5.7, 5.5) ) design_w_singleton <- survey::svydesign( data = df_w_singleton, ids = ~ PSU, strata = ~ Stratum, weights = ~ Design_Weight ) options("survey.lonely.psu" = "remove") stopifnot(all.equal( target = 126625, current = as.numeric( vcov(svytotal(x = ~ Age, design = design_w_singleton)) ) )) options("survey.lonely.psu" = "certainty") stopifnot(all.equal( target = 126625, current = as.numeric( vcov(svytotal(x = ~ Age, design = design_w_singleton)) ) )) options("survey.lonely.psu" = "adjust") stopifnot(all.equal( target = 127579.8, current = as.numeric( vcov(svytotal(x = ~ Age, design = design_w_singleton)) ), scale = 127579.8, tolerance = 0.000001 )) survey/tests/cigsw.rda0000644000176200001440000000644513517610741014573 0ustar liggesusersYwXS"*`0DPVa#, BPR+E[GUb[B*^'V:9iGy{!05B{1-# <7MhAD@ ؙ`OЎ@t"LPLЕ;A"MЇp#$8/A? J YC '\Fp9r>7<93+]㚝мl vp۲4oZjr2&4$!IV{co0Ф'q *!J!KI/R'4G]7."y)ؐ%>K_7d/BNC!|ϡ x^ h^; #‚xI}i$A$A}T,'),Ѽ.cbW?<&{R75>ׂqmǭkr >`Y)EH<9 SmH-- !JQ1gj̝Tw0TAK. [Qx{;YhlȠ`zbԻCL43ݷY4֮bhBqڵ|ֈvY[yN*Z_l3MshQO`OĩaĤ86SQU*xd6!#rGbOmEu\!FaW!a'JA< %= N/BN D+e:Xu" {׈+d-aV|ArtWu`c/aQ5O]~Ƣ]Y\dۿ@<ա>{S|[Sð?HS ].m'3D}LP +gFbzU̫)?Ӵޤ Ίr?}|'1Oq6>t8b;=Y?#v^;uf`u~΋|c No\i%JO`#vc;'`eno(Kks{0~fG.c<%đA}G8CԆh7'vTnº޿"0y\ݫ>c)J#e]QLE\݋|NN1=?zn+F Tc^Axj 0>CJS>z%/=ƅc@b@~#/!.Lt$l]P 14ꢖBP-X s}hNn.muT$D=K-om+@Pڑ'!i{{3}9f6[d]kt4|91'p][r{C`ƝABOB#KA둹fTL"K, խDO_[Fݶ<ЦY?Yq=W5m8e{v*#H.h ;eDܓ ~i(]y;rOH,̟2TK]?kxſJm}NNAEF ~@8=0ti$[_Qn+ qOC^>x+Os灸RRa|ܝ'S[ȧc4}}~o^#xn__ 7m[V}=J<^zѽph*La|ŇmfпGW+%Zܳ}MmCMIԶ;XXXWm ]~ڴoz>z۔vDB'?>Y{9ftw -z,֯ǢE=([7$1J{~O?SNY2X"zoohVˏm~F^]iQf? 2qEe y'u&Ґ&ÐԢt>>A~vL33B%u={c%fig<-/2,*/3M\ZԜtNjqE9w}V-cmuܲif%oQD%::3T,{I/|7+:oDgoà0ڿ{IwyNWCU>h8όRg|Ii=B2}ٔ\:Oϋe fxԍ{CiF&ƺ)iD49eAEM&gsE2t\t++M|D-H`OKN^KVf+9ׯzKg&nXzM59% HOsurvey/tests/svyivreg-var.R0000644000176200001440000000042614204276526015552 0ustar liggesusers library(survey) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) a<-summary(svyglm(api00~ell+meals+mobility, design=dstrat)) b<- summary(svyivreg(api00~ell+meals+mobility, design=dstrat)) stopifnot(isTRUE(all.equal(a$cov.scaled, b$vcov))) survey/tests/kalton.Rout.save0000644000176200001440000000641513517345033016063 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > > ab<-expand.grid(a=factor(1:4),b=factor(1:3)) > > kaltonsample<-ab[rep(1:12,c(20,50,100,30,40,140,50,100,40,310,50,70)),] > > kaltonpop<-ab[rep(1:12,c(80,60,170,55,40,150,60,165,55,340,200,125)),] > > jointpop<-colSums(model.matrix(~a*b,kaltonpop)) > marginalpop<-colSums(model.matrix(~a+b,kaltonpop)) > gregpop<-colSums(model.matrix(~as.numeric(a)+as.numeric(b),kaltonpop)) > > dkalton<-svydesign(id=~1,data=kaltonsample) Warning message: In svydesign.default(id = ~1, data = kaltonsample) : No weights or probabilities supplied, assuming equal probability > > dps<-postStratify(dkalton,~a+b,xtabs(~a+b,kaltonpop)) > > drake<-rake(dkalton, list(~a,~b),list(xtabs(~a,kaltonpop),xtabs(~b,kaltonpop)),control=list(epsilon=0.0001)) > > dcalps<-calibrate(dkalton, ~a*b, jointpop) > dcalrake<-calibrate(dkalton,~a+b, marginalpop, calfun="raking") Loading required package: MASS > dlinear<-calibrate(dkalton, ~a+b, marginalpop) > > dtrunclinear<-calibrate(dkalton, ~a+b, marginalpop,bounds=c(0.5,2.2)) > > dlogit<-calibrate(dkalton, ~a+b, marginalpop,bounds=c(0.5,2.2),calfun="logit") > > dgreg<-calibrate(dkalton,~as.numeric(a)+as.numeric(b), gregpop) > > > #table A > round(svytable(~a+b,dps)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 4.00 1.00 1.38 2 1.20 1.07 1.10 3 1.70 1.20 4.00 4 1.83 1.65 1.79 > round(svytable(~a+b,dcalps)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 4.00 1.00 1.37 2 1.20 1.07 1.10 3 1.70 1.20 4.00 4 1.83 1.65 1.79 > > #table B > round(svytable(~a+b,drake)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 1.81 1.45 2.02 2 1.08 0.87 1.21 3 2.20 1.76 2.45 4 1.83 1.47 2.04 > round(svytable(~a+b,dcalrake)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 1.81 1.45 2.02 2 1.08 0.87 1.21 3 2.20 1.76 2.45 4 1.83 1.47 2.04 > > #table C > round(svytable(~a+b,dlinear)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 1.82 1.50 1.97 2 1.09 0.78 1.24 3 2.19 1.88 2.34 4 1.83 1.52 1.98 > > #table D > round(svytable(~a+b,dgreg)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 1.21 1.17 1.14 2 1.43 1.40 1.36 3 1.66 1.62 1.59 4 1.88 1.85 1.81 > > #table G > round(svytable(~a+b,dlogit)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 1.87 1.46 1.98 2 1.08 0.74 1.27 3 2.17 2.09 2.18 4 1.89 1.49 1.99 > > #table G > round(svytable(~a+b,dtrunclinear)/xtabs(~a+b,kaltonsample),2) b a 1 2 3 1 1.81 1.48 1.99 2 1.08 0.75 1.26 3 2.20 2.00 2.20 4 1.83 1.50 2.00 > > proc.time() user system elapsed 0.248 0.027 0.282 survey/tests/svyby_bug.R0000644000176200001440000000334514360203326015111 0ustar liggesuserslibrary(survey) options(warn=2) ## Caused warnings and unhelpful results in 4.1_1 (Guilherme Jacob) data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svyby(~api99, ~stype, dclus1, svymean ) set.seed(123) apiclus1$api99[ sample.int( nrow(apiclus1) , 5 ) ] <- NA dclus1.na <-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) # subsetting w/ na.rm = FALSE... svymean( ~api99 , subset( dclus1.na , stype == "E" ) , na.rm = FALSE ) svymean( ~api99 , subset( dclus1.na , stype == "H" ) , na.rm = FALSE ) svymean( ~api99 , subset( dclus1.na , stype == "M" ) , na.rm = FALSE ) # ... looks like this: svyby(~api99, ~stype, dclus1.na , svymean ) # subsetting w/ na.rm = TRUE... svymean( ~api99 , subset( dclus1.na , stype == "E" ) , na.rm = TRUE ) svymean( ~api99 , subset( dclus1.na , stype == "H" ) , na.rm = TRUE ) svymean( ~api99 , subset( dclus1.na , stype == "M" ) , na.rm = TRUE ) # ... looks like this svyby(~api99, ~stype, dclus1.na , svymean , na.rm = TRUE ) # Without missing values, this works: svyby(~api99, ~stype, dclus1 , svymean , na.rm = TRUE , covmat = TRUE ) # ... but this breaks! svyby(~api99, ~stype, dclus1.na , svymean , na.rm = TRUE , covmat = TRUE ) # ... and i don't think this is the expected behavior svyby( ~api99, ~stype, dclus1.na , svymean , na.rm.all = TRUE , covmat = TRUE ) svyby( ~api99, ~stype, dclus1.na , svymean , na.rm.all = TRUE , na.rm = TRUE , covmat = TRUE ) ## Now some more as tests svyby(~api99, ~stype, dclus1.na , svytotal , na.rm = TRUE , covmat = TRUE ) svyby(~api99, ~stype, dclus1.na , svyratio , na.rm = TRUE , denominator=~api00, covmat = TRUE ) ff<-function(f,d,...,na.rm=TRUE) svyglm(f,d,...) svyby(api99~1, ~stype, dclus1.na , ff , na.rm = TRUE , covmat = TRUE ) survey/tests/domain.R0000644000176200001440000001016513517345033014352 0ustar liggesusers## ## Domain means can be written as ratio estimators or as regression coefficients ## ## This code checks that subsetting the design object gives the same results as ## these approaches. ## library(survey) data(fpc) dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) dsub<-subset(dfpc,x>4) (m1<-svymean(~x,design=dsub)) ## These should give the same domain estimates and standard errors (m2<-svyby(~x,~I(x>4),design=dfpc, svymean,keep.var=TRUE)) m3<-svyglm(x~I(x>4)+0,design=dfpc) summary(m3) (m4<-svyratio(~I(x*(x>4)),~as.numeric(x>4), dfpc)) stopifnot(isTRUE(all.equal(SE(m2), as.vector(SE(m3))))) stopifnot(isTRUE(all.equal(SE(m2)[2], as.vector(SE(m4))))) ## with strata data(api) dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) m1<-svymean(~enroll, subset(dstrat, comp.imp=="Yes")) m2<-svyglm(enroll~comp.imp-1, dstrat) m3<- svyratio(~I(enroll*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dstrat) stopifnot(isTRUE(all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) ## with calibration dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) (dclus1g3 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069))) m1<-svymean(~api00, subset(dclus1g3, comp.imp=="Yes")) m3<-svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dclus1g3) m2<-svyglm(api00~comp.imp-1, dclus1g3) stopifnot(isTRUE( all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) ## with raking pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) m1<-svymean(~api00, subset(dclus1r, comp.imp=="Yes")) m2<-svyglm(api00~comp.imp-1, dclus1r) m3<-svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dclus1r) stopifnot(isTRUE( all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) ## ## based on bug report from Takahiro Tsuchiya for version 3.4 ## rei<-read.table(tmp<-textConnection( " id N n.a h n.ah n.h sub y 1 1 300 20 1 12 5 TRUE 1 2 2 300 20 1 12 5 TRUE 2 3 3 300 20 1 12 5 TRUE 3 4 4 300 20 1 12 5 TRUE 4 5 5 300 20 1 12 5 TRUE 5 6 6 300 20 1 12 5 FALSE NA 7 7 300 20 1 12 5 FALSE NA 8 8 300 20 1 12 5 FALSE NA 9 9 300 20 1 12 5 FALSE NA 10 10 300 20 1 12 5 FALSE NA 11 11 300 20 1 12 5 FALSE NA 12 12 300 20 1 12 5 FALSE NA 13 13 300 20 2 8 3 TRUE 6 14 14 300 20 2 8 3 TRUE 7 15 15 300 20 2 8 3 TRUE 8 16 16 300 20 2 8 3 FALSE NA 17 17 300 20 2 8 3 FALSE NA 18 18 300 20 2 8 3 FALSE NA 19 19 300 20 2 8 3 FALSE NA 20 20 300 20 2 8 3 FALSE NA "), header=TRUE) close(tmp) des.rei2 <- twophase(id=list(~id,~id), strata=list(NULL,~h), fpc=list(~N,NULL), subset=~sub, data=rei, method="full") tot2<- svytotal(~y, subset(des.rei2, y>3)) rei$y<-rei$y*(rei$y>3) ## based on Sarndal et al (9.4.14) rei$w.ah <- rei$n.ah / rei$n.a a.rei <- aggregate(rei, by=list(rei$h), mean, na.rm=TRUE) a.rei$S.ysh <- tapply(rei$y, rei$h, var, na.rm=TRUE) a.rei$y.u <- sum(a.rei$w.ah * a.rei$y) V <- with(a.rei, sum(N * (N-1) * ((n.ah-1)/(n.a-1) - (n.h-1)/(N-1)) * w.ah * S.ysh / n.h)) V <- V + with(a.rei, sum(N * (N-n.a) * w.ah * (y - y.u)^2 / (n.a-1))) a.rei$f.h<-with(a.rei, n.h/n.ah) Vphase2<-with(a.rei, sum(N*N*w.ah^2* ((1-f.h)/n.h) *S.ysh)) a.rei$f<-with(a.rei, n.a/N) a.rei$delta.h<-with(a.rei, (1/n.h)*(n.a-n.ah)/(n.a-1)) Vphase1<-with(a.rei, sum(N*N*((1-f)/n.a)*( w.ah*(1-delta.h)*S.ysh+ ((n.a)/(n.a-1))*w.ah*(y-y.u)^2))) V Vphase1 Vphase2 vcov(tot2) ## comparing to regression reg<-svyglm(y~I(y<4), design=des.rei2) mn<-svymean(~y, subset(des.rei2,y>3)) all.equal(as.vector(coef(reg))[1],as.vector(coef(mn))) all.equal(as.vector(SE(reg))[1],as.vector(SE(mn))) vcov(mn) vcov(reg) survey/tests/nwts-cch.R0000644000176200001440000000215213517345033014626 0ustar liggesuserslibrary(survey) library(survival) data(nwtco) ntwco<-subset(nwtco, !is.na(edrel)) load("nwtco-subcohort.rda") nwtco$subcohort<-subcohort d_BorganII <- twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(instit,rel)), data=nwtco, subset=~I(rel |subcohort)) ##Coefficient results same as Splus with code from ## http://faculty.washington.edu/norm/software.html ## SE slightly larger due to using sandwich variance. svycoxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganII) ## ## This gives higher standard errors. calibrate() does not recompute the ## finite population correction if a calibration variable happens to predict ## sampling perfectly. It probably should. ## d_BorganIIps<-calibrate(twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), data=nwtco, subset=~I(rel |subcohort)), phase=2, formula=~interaction(instit,rel), epsilon=1e-10) svycoxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganIIps) survey/tests/survcurve.R0000644000176200001440000000136213517345033015146 0ustar liggesuserslibrary(survey) library(survival) pbc2<-rbind(pbc,pbc) pbc2$id<-rep(1:418,2) dpbc1<-svydesign(id=~1, data=pbc) dpbc2<-svydesign(id=~id, data=pbc2) s1<-svykm(Surv(time,status>0)~1, subset(dpbc1, bili>6), se=TRUE) s2<-svykm(Surv(time,status>0)~1, subset(dpbc2, bili>6), se=TRUE) (c1<-confint(s1,(1:5)*365)) (c2<-confint(s2,(1:5)*365)) all.equal(c1, c2) m1<-svycoxph(Surv(time,status>0)~log(bili), design=dpbc1) m2<-svycoxph(Surv(time,status>0)~log(bili), design=dpbc2) d<-data.frame(bili=c(5,10)) p1<-predict(m1, se=TRUE, newdata=d,type="curve") p2<-predict(m2, se=TRUE, newdata=d,type="curve") (pc1<-confint(p1[[1]],(1:5)*365)) (pc2<-confint(p2[[1]],(1:5)*365)) all.equal(pc1, pc2) (q1<-quantile(p1[[2]])) (q2<-quantile(p2[[2]])) all.equal(q1,q2) survey/tests/multistage-rcpp.R0000644000176200001440000002602414526736255016237 0ustar liggesusers## temporarily disable while it's being fixed ## q("no") ## ## Check that multistage() and multistage_rcpp() give same results ## for different options. ## library(survey) # Check for a simple random sample ---- data('api', package = 'survey') api_srs_design <- svydesign( data = apisrs, ids = ~ 1, weights = ~ 1 ) x <- as.matrix(api_srs_design$variables[,c('api00','api99')] / api_srs_design$prob) base_r_result <- survey:::multistage(x = x, clusters = api_srs_design$cluster, stratas = api_srs_design$strata, nPSUs = api_srs_design$fpc$sampsize, fpcs = api_srs_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=TRUE, stage = 1, cal = NULL) rcpp_result <- survey:::multistage_rcpp(x = x, clusters = api_srs_design$cluster, stratas = api_srs_design$strata, nPSUs = api_srs_design$fpc$sampsize, fpcs = api_srs_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=TRUE, stage = 1, cal = NULL) if (!isTRUE(all.equal(base_r_result, rcpp_result))) { stop("Differences between `multistage()` and `multistage_rcpp()` for SRS") } # Check for a stratified simple random sample ---- apistrat_design <- svydesign( data = apistrat, id =~ 1, strata =~ stype, weights =~ pw, fpc =~ fpc ) x <- as.matrix(apistrat_design$variables[,c('api00','api99')] / apistrat_design$prob) base_r_result <- survey:::multistage(x = x, clusters = apistrat_design$cluster, stratas = apistrat_design$strata, nPSUs = apistrat_design$fpc$sampsize, fpcs = apistrat_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=TRUE, stage = 1, cal = NULL) rcpp_result <- survey:::multistage_rcpp(x = x, clusters = apistrat_design$cluster, stratas = apistrat_design$strata, nPSUs = apistrat_design$fpc$sampsize, fpcs = apistrat_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=TRUE, stage = 1, cal = NULL) if (!isTRUE(all.equal(base_r_result, rcpp_result))) { stop("Differences between `multistage()` and `multistage_rcpp()` for stratified sample") } ##_ Check whether expected true zeroes are actually computed as zeroes ---- x <- as.matrix(model.matrix(~ -1 + stype, data = apistrat_design$variables) / (sum(apistrat_design$prob)/apistrat_design$prob)) base_r_result <- survey:::multistage(x = x, clusters = apistrat_design$cluster, stratas = apistrat_design$strata, nPSUs = apistrat_design$fpc$sampsize, fpcs = apistrat_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=TRUE, stage = 1, cal = NULL) rcpp_result <- survey:::multistage_rcpp(x = x, clusters = apistrat_design$cluster, stratas = apistrat_design$strata, nPSUs = apistrat_design$fpc$sampsize, fpcs = apistrat_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=TRUE, stage = 1, cal = NULL) if (!all(rcpp_result == 0) | !all(diag(rcpp_result) >= 0)) { stop("Computations which should equal zero do not.") } # Create a stratified, multistage design ---- data(mu284) ## Create three strata, the third of which has only one PSU mu284_stratified <- rbind( transform(mu284, stratum = 1, y2 = y1 + c(0, 2, 1, 0, -1, 2, 0, 3, 0, 0, -1, -1, 1, -1, 0)), transform(mu284, stratum = 2, y2 = y1 + c(-1, 0, -1, 0, -1, 0, 0, 1, 2, -1, 1, 1, 0, 1, 0)), transform(mu284[1,], stratum = 3, y2 = y1 + 2) ) ## Create domain variables which yield a lonely PSU or a lonely SSU mu284_stratified[['DOMAIN_W_LONELY_PSU']] <- mu284_stratified$stratum == 1 | (mu284_stratified$stratum == 2 & mu284_stratified$id1 == 19) mu284_stratified[['DOMAIN_W_LONELY_SSU']] <- mu284_stratified$stratum == 1 | (mu284_stratified$stratum == 2 & mu284_stratified$id1 == 19 & mu284_stratified$id2 == 1) ## Create a survey design object with no lonely PSUs dmu284_strat <- svydesign(data = subset(mu284_stratified, stratum != 3), strata = ~ stratum, nest = TRUE, id = ~ id1 + id2, fpc = ~ n1 + n2) ## Create a survey design with one lonely PSU dmu284_strat_w_lonely <- svydesign(data = mu284_stratified, strata = ~ stratum, nest = TRUE, id = ~ id1 + id2, fpc = ~ n1 + n2) ## Create subsetted design objects, subsetted to domain with a lonely PSU or SSU dmu284_strat_domain_lonely_psu <- subset(dmu284_strat, DOMAIN_W_LONELY_PSU) dmu284_strat_domain_lonely_ssu <- subset(dmu284_strat, DOMAIN_W_LONELY_SSU) # Check same results for different values of 'lonely PSU' options ---- lonely_psu_options <- c(certainty = 'certainty', remove = 'remove', average = 'average', adjust = 'adjust') one_stage_options <- c(TRUE, FALSE) design_lonely_psu_comparisons <- expand.grid('survey.lonely.psu' = lonely_psu_options, 'one.stage' = one_stage_options, stringsAsFactors = FALSE) design_lonely_psu_comparisons[['results_match']] <- NA ##_ Check for lonely PSUs caused by design ---- for (i in seq_len(nrow(design_lonely_psu_comparisons))) { survey.lonely.psu <- design_lonely_psu_comparisons[['survey.lonely.psu']][i] one.stage <- design_lonely_psu_comparisons[['one.stage']][i] options('survey.lonely.psu' = survey.lonely.psu) x <- as.matrix(dmu284_strat_w_lonely$variables[,c('y1','y2')] / dmu284_strat_w_lonely$prob) base_r_result <- survey:::multistage(x = x, clusters = dmu284_strat_w_lonely$cluster, stratas = dmu284_strat_w_lonely$strata, nPSUs = dmu284_strat_w_lonely$fpc$sampsize, fpcs = dmu284_strat_w_lonely$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage, stage = 1, cal = NULL) rcpp_result <- survey:::multistage_rcpp(x = x, clusters = dmu284_strat_w_lonely$cluster, stratas = dmu284_strat_w_lonely$strata, nPSUs = dmu284_strat_w_lonely$fpc$sampsize, fpcs = dmu284_strat_w_lonely$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage, stage = 1, cal = NULL) design_lonely_psu_comparisons[['results_match']][i] <- isTRUE(all.equal(base_r_result, rcpp_result)) } print(design_lonely_psu_comparisons) if (!all(design_lonely_psu_comparisons$results_match)) { stop("Results for design lonely PSUs differ between base R and Rcpp implementations.") } ##_ Check for domain lonely PSUs caused by subsetting ---- domain_lonely_psu_comparisons <- expand.grid('survey.lonely.psu' = lonely_psu_options, 'survey.adjust.domain.lonely' = c(FALSE, TRUE), 'one.stage' = one_stage_options, stringsAsFactors = FALSE) domain_lonely_psu_comparisons[['results_match']] <- NA for (i in seq_len(nrow(domain_lonely_psu_comparisons))) { options('survey.lonely.psu' = domain_lonely_psu_comparisons[['survey.lonely.psu']][i]) options('survey.adjust.domain.lonely' = domain_lonely_psu_comparisons[['survey.adjust.domain.lonely']][i]) one.stage <- domain_lonely_psu_comparisons[['one.stage']][i] x <- as.matrix(dmu284_strat_domain_lonely_psu$variables[,c('y1','y2')] / dmu284_strat_domain_lonely_psu$prob) suppressWarnings({ base_r_result <- survey:::multistage(x = x, clusters = dmu284_strat_domain_lonely_psu$cluster, stratas = dmu284_strat_domain_lonely_psu$strata, nPSUs = dmu284_strat_domain_lonely_psu$fpc$sampsize, fpcs = dmu284_strat_domain_lonely_psu$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage, stage = 1, cal = NULL) }) rcpp_result <- survey:::multistage_rcpp(x = x, clusters = dmu284_strat_domain_lonely_psu$cluster, stratas = dmu284_strat_domain_lonely_psu$strata, nPSUs = dmu284_strat_domain_lonely_psu$fpc$sampsize, fpcs = dmu284_strat_domain_lonely_psu$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage, stage = 1, cal = NULL) domain_lonely_psu_comparisons[['results_match']][i] <- isTRUE(all.equal(base_r_result, rcpp_result)) } print(domain_lonely_psu_comparisons) if (!all(domain_lonely_psu_comparisons$results_match)) { stop("Results for domain lonely PSUs differ between base R and Rcpp implementations.") } # Checks for edge cases ---- ## Zero records ---- zero_row_design <- dmu284_strat |> subset(id1 == "Nonexistent") zero_row_result <- survey:::multistage_rcpp( x = as.matrix(zero_row_design$variables[,c('y1', 'y2')]), clusters = zero_row_design$cluster, stratas = zero_row_design$strata, nPSUs = zero_row_design$fpc$sampsize, fpcs = zero_row_design$fpc$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage, stage = 1, cal = NULL ) if (any(is.na(zero_row_result)) || any(zero_row_result != 0)) { stop("`multistage_rcpp()` should return zeros for zero-row inputs.") } survey/tests/defftest.R0000644000176200001440000000117013645212133014677 0ustar liggesuserslibrary(survey) data(api) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) # svyglm model mod <- svyglm(api99 ~ enroll + api.stu, design = dclus1, deff = TRUE) #deffs returned from svyglm model - implausibly high deff(mod) #> (Intercept) enroll api.stu #> 351.3500 457.6799 491.5567 # run mod with same data and glm() srs_mod <- glm(api99 ~ enroll + api.stu, data = apiclus1) # manually calculate deffs clust_se <- summary(mod)$coefficients[,2] srs_se <- summary(srs_mod)$coefficients[,2] deffs <- clust_se^2 / srs_se^2 stopifnot(all.equal(deffs, deff(mod))) survey/tests/testoutput/0000755000176200001440000000000014411726371015217 5ustar liggesuserssurvey/tests/testoutput/api.Rout.saved0000644000176200001440000003543313517345033017752 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > options(survey.replicates.mse=TRUE) > example(api) api> library(survey) api> data(api) api> mean(apipop$api00) [1] 664.7126 api> sum(apipop$enroll, na.rm=TRUE) [1] 3811472 api> #stratified sample api> dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) api> summary(dstrat) Stratified Independent Sampling design svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02262 0.02262 0.03587 0.04014 0.05339 0.06623 Stratum Sizes: E H M obs 100 50 50 design.PSU 100 50 50 actual.PSU 100 50 50 Population stratum sizes (PSUs): E H M 4421 755 1018 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "pw" "fpc" api> svymean(~api00, dstrat) mean SE api00 662.29 9.4089 api> svytotal(~enroll, dstrat, na.rm=TRUE) total SE enroll 3687178 114642 api> # one-stage cluster sample api> dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) api> summary(dclus1) 1 - level Cluster Sampling design With (15) clusters. svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02954 0.02954 0.02954 0.02954 0.02954 0.02954 Population size (PSUs): 757 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> svymean(~api00, dclus1) mean SE api00 644.17 23.542 api> svytotal(~enroll, dclus1, na.rm=TRUE) total SE enroll 3404940 932235 api> # two-stage cluster sample api> dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) api> summary(dclus2) 2 - level Cluster Sampling design With (40, 126) clusters. svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.003669 0.037740 0.052840 0.042390 0.052840 0.052840 Population size (PSUs): 757 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "pw" "fpc1" "fpc2" api> svymean(~api00, dclus2) mean SE api00 670.81 30.099 api> svytotal(~enroll, dclus2, na.rm=TRUE) total SE enroll 2639273 799638 api> # two-stage `with replacement' api> dclus2wr<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) api> summary(dclus2wr) 2 - level Cluster Sampling design (with replacement) With (40, 126) clusters. svydesign(id = ~dnum + snum, weights = ~pw, data = apiclus2) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.003669 0.037740 0.052840 0.042390 0.052840 0.052840 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "pw" "fpc1" "fpc2" api> svymean(~api00, dclus2wr) mean SE api00 670.81 30.712 api> svytotal(~enroll, dclus2wr, na.rm=TRUE) total SE enroll 2639273 820261 api> # convert to replicate weights api> rclus1<-as.svrepdesign(dclus1) api> summary(rclus1) Call: as.svrepdesign(dclus1) Unstratified cluster jacknife (JK1) with 15 replicates and MSE variances. Variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> svymean(~api00, rclus1) mean SE api00 644.17 26.335 api> svytotal(~enroll, rclus1, na.rm=TRUE) total SE enroll 3404940 932235 api> # post-stratify on school type api> pop.types<-xtabs(~stype, data=apipop) api> rclus1p<-postStratify(rclus1, ~stype, pop.types) api> dclus1p<-postStratify(dclus1, ~stype, pop.types) api> summary(dclus1p) 1 - level Cluster Sampling design With (15) clusters. postStratify(dclus1, ~stype, pop.types) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.01854 0.03257 0.03257 0.03040 0.03257 0.03257 Population size (PSUs): 757 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> summary(rclus1p) Call: postStratify(rclus1, ~stype, pop.types) Unstratified cluster jacknife (JK1) with 15 replicates and MSE variances. Variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> svymean(~api00, dclus1p) mean SE api00 642.31 23.921 api> svytotal(~enroll, dclus1p, na.rm=TRUE) total SE enroll 3680893 406293 api> svymean(~api00, rclus1p) mean SE api00 642.31 26.936 api> svytotal(~enroll, rclus1p, na.rm=TRUE) total SE enroll 3680893 473434 > > options(survey.replicates.mse=FALSE) > example(api) api> library(survey) api> data(api) api> mean(apipop$api00) [1] 664.7126 api> sum(apipop$enroll, na.rm=TRUE) [1] 3811472 api> #stratified sample api> dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) api> summary(dstrat) Stratified Independent Sampling design svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02262 0.02262 0.03587 0.04014 0.05339 0.06623 Stratum Sizes: E H M obs 100 50 50 design.PSU 100 50 50 actual.PSU 100 50 50 Population stratum sizes (PSUs): E H M 4421 755 1018 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "pw" "fpc" api> svymean(~api00, dstrat) mean SE api00 662.29 9.4089 api> svytotal(~enroll, dstrat, na.rm=TRUE) total SE enroll 3687178 114642 api> # one-stage cluster sample api> dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) api> summary(dclus1) 1 - level Cluster Sampling design With (15) clusters. svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02954 0.02954 0.02954 0.02954 0.02954 0.02954 Population size (PSUs): 757 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> svymean(~api00, dclus1) mean SE api00 644.17 23.542 api> svytotal(~enroll, dclus1, na.rm=TRUE) total SE enroll 3404940 932235 api> # two-stage cluster sample api> dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) api> summary(dclus2) 2 - level Cluster Sampling design With (40, 126) clusters. svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.003669 0.037740 0.052840 0.042390 0.052840 0.052840 Population size (PSUs): 757 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "pw" "fpc1" "fpc2" api> svymean(~api00, dclus2) mean SE api00 670.81 30.099 api> svytotal(~enroll, dclus2, na.rm=TRUE) total SE enroll 2639273 799638 api> # two-stage `with replacement' api> dclus2wr<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) api> summary(dclus2wr) 2 - level Cluster Sampling design (with replacement) With (40, 126) clusters. svydesign(id = ~dnum + snum, weights = ~pw, data = apiclus2) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.003669 0.037740 0.052840 0.042390 0.052840 0.052840 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "pw" "fpc1" "fpc2" api> svymean(~api00, dclus2wr) mean SE api00 670.81 30.712 api> svytotal(~enroll, dclus2wr, na.rm=TRUE) total SE enroll 2639273 820261 api> # convert to replicate weights api> rclus1<-as.svrepdesign(dclus1) api> summary(rclus1) Call: as.svrepdesign(dclus1) Unstratified cluster jacknife (JK1) with 15 replicates. Variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> svymean(~api00, rclus1) mean SE api00 644.17 26.329 api> svytotal(~enroll, rclus1, na.rm=TRUE) total SE enroll 3404940 932235 api> # post-stratify on school type api> pop.types<-xtabs(~stype, data=apipop) api> rclus1p<-postStratify(rclus1, ~stype, pop.types) api> dclus1p<-postStratify(dclus1, ~stype, pop.types) api> summary(dclus1p) 1 - level Cluster Sampling design With (15) clusters. postStratify(dclus1, ~stype, pop.types) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.01854 0.03257 0.03257 0.03040 0.03257 0.03257 Population size (PSUs): 757 Data variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> summary(rclus1p) Call: postStratify(rclus1, ~stype, pop.types) Unstratified cluster jacknife (JK1) with 15 replicates. Variables: [1] "cds" "stype" "name" "sname" "snum" "dname" [7] "dnum" "cname" "cnum" "flag" "pcttest" "api00" [13] "api99" "target" "growth" "sch.wide" "comp.imp" "both" [19] "awards" "meals" "ell" "yr.rnd" "mobility" "acs.k3" [25] "acs.46" "acs.core" "pct.resp" "not.hsg" "hsg" "some.col" [31] "col.grad" "grad.sch" "avg.ed" "full" "emer" "enroll" [37] "api.stu" "fpc" "pw" api> svymean(~api00, dclus1p) mean SE api00 642.31 23.921 api> svytotal(~enroll, dclus1p, na.rm=TRUE) total SE enroll 3680893 406293 api> svymean(~api00, rclus1p) mean SE api00 642.31 26.934 api> svytotal(~enroll, rclus1p, na.rm=TRUE) total SE enroll 3680893 473431 > > proc.time() user system elapsed 0.560 0.033 0.608 survey/tests/testoutput/scoping.Rout.save0000644000176200001440000000631713517345033020476 0ustar liggesusers R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.4.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > ## regression test for testing regression > > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(api) > > dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) > > > f<-function(){ + form<-acs.46~stype + svyglm(formula=form, design = dstrat) + } > > g<-function(form){ + svyglm(formula=form, design = dstrat) + } > f() Stratified Independent Sampling design svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) Call: svyglm(formula = form, design = dstrat) Coefficients: (Intercept) stypeH stypeM 28.7449 0.7551 0.4022 Degrees of Freedom: 133 Total (i.e. Null); 129 Residual (66 observations deleted due to missingness) Null Deviance: 1838 Residual Deviance: 1835 AIC: 719.8 > g(acs.46~stype) Stratified Independent Sampling design svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) Call: svyglm(formula = form, design = dstrat) Coefficients: (Intercept) stypeH stypeM 28.7449 0.7551 0.4022 Degrees of Freedom: 133 Total (i.e. Null); 129 Residual (66 observations deleted due to missingness) Null Deviance: 1838 Residual Deviance: 1835 AIC: 719.8 > > f<-function(){ + form<-Surv(acs.46)~stype + svycoxph(formula=form, design = dstrat) + } > > g<-function(form){ + svycoxph(formula=form, design = dstrat) + } > > f() Call: svycoxph(formula = form, design = dstrat) coef exp(coef) se(coef) z p stypeH -0.677 0.508 0.665 -1.02 0.31 stypeM -0.251 0.778 0.209 -1.20 0.23 Likelihood ratio test= on 2 df, p= n= 134, number of events= 134 (66 observations deleted due to missingness) > g(Surv(acs.46)~stype) Call: svycoxph(formula = form, design = dstrat) coef exp(coef) se(coef) z p stypeH -0.677 0.508 0.665 -1.02 0.31 stypeM -0.251 0.778 0.209 -1.20 0.23 Likelihood ratio test= on 2 df, p= n= 134, number of events= 134 (66 observations deleted due to missingness) > > ## check coxph for a single predictor > svycoxph(Surv(acs.46)~api00,design=dstrat) Call: svycoxph(formula = Surv(acs.46) ~ api00, design = dstrat) coef exp(coef) se(coef) z p api00 6.06e-06 1.00e+00 7.49e-04 0.01 0.99 Likelihood ratio test= on 1 df, p= n= 134, number of events= 134 (66 observations deleted due to missingness) > > proc.time() user system elapsed 1.030 0.059 1.106 survey/tests/testoutput/multistage.Rout.save0000644000176200001440000000227113517345033021205 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## > ## Check that multistage samples still work > ## > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > example(mu284) mu284> data(mu284) mu284> (dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284)) 2 - level Cluster Sampling design With (5, 15) clusters. svydesign(id = ~id1 + id2, fpc = ~n1 + n2, data = mu284) mu284> (ytotal<-svytotal(~y1, dmu284)) total SE y1 15080 2274.3 mu284> vcov(ytotal) y1 y1 5172234 > > > proc.time() user system elapsed 0.167 0.024 0.202 survey/tests/testoutput/README0000644000176200001440000000043313517345033016075 0ustar liggesusersThese are tests involving the printed output. They do not give character-by-character identical results on all systems (and in particular, the CRAN Windows system gives slightly different results from any computer I have tried), so they can't be part of the default R CMD check. survey/tests/testoutput/caleg.Rout.save0000644000176200001440000002072713623322530020103 0ustar liggesusers R version 3.4.0 (2017-04-21) -- "You Stupid Darkness" Copyright (C) 2017 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin15.6.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## > ## Calibration examples > ## > > > ## Example of calibration to first-stage clusters > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(api) > > clusters<-table(apiclus2$dnum) > clusters<-clusters[clusters>1 & names(clusters)!="639"] > apiclus2a<-subset(apiclus2, dnum %in% as.numeric(names(clusters))) > > dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2a) > > popclusters<-subset(apipop, dnum %in% as.numeric(names(clusters))) > > pop<-lapply(as.numeric(names(clusters)), function(cluster) { + colSums(model.matrix(~api99, model.frame(~api99, subset(popclusters, dnum %in% cluster))))}) > > names(pop)<-names(clusters) > > dclus2g<-calibrate(dclus2, ~api99, pop,stage=1) > > svymean(~api99, dclus2) mean SE api99 642.14 31.434 > svymean(~api99, dclus2g) mean SE api99 654.49 29.82 > > round(svyby(~api99, ~dnum, design=dclus2, svymean),4) dnum api99 se 83 83 694.3333 0.0000 132 132 505.0000 0.0000 152 152 574.0000 0.0000 173 173 894.7500 0.0000 198 198 533.7500 0.0000 200 200 589.8000 6.8335 228 228 477.0000 0.0000 295 295 646.4000 0.0000 302 302 903.5000 0.0000 403 403 852.4000 0.0000 452 452 533.0000 0.0000 480 480 614.2000 0.0000 523 523 580.5000 0.0000 534 534 564.6000 0.0000 549 549 896.2000 0.0000 552 552 730.0000 0.0000 570 570 518.4000 7.5478 575 575 800.8000 4.2513 596 596 785.6000 2.4155 620 620 591.6000 10.5869 638 638 560.2000 4.0954 674 674 760.0000 0.0000 679 679 610.2500 0.0000 687 687 718.6667 0.0000 701 701 651.5000 0.0000 711 711 690.5000 0.0000 731 731 702.0000 2.1744 768 768 562.5000 0.0000 781 781 854.4000 0.7456 > > round(svyby(~api99, ~dnum, design=dclus2g, svymean),4) dnum api99 se 83 83 694.3333 0 132 132 505.0000 0 152 152 574.0000 0 173 173 894.7500 0 198 198 533.7500 0 200 200 567.5455 0 228 228 477.0000 0 295 295 646.4000 0 302 302 903.5000 0 403 403 852.4000 0 452 452 533.0000 0 480 480 614.2000 0 523 523 580.5000 0 534 534 564.6000 0 549 549 896.2000 0 552 552 730.0000 0 570 570 548.9444 0 575 575 824.5357 0 596 596 787.5714 0 620 620 609.3750 0 638 638 585.6429 0 674 674 760.0000 0 679 679 610.2500 0 687 687 718.6667 0 701 701 651.5000 0 711 711 690.5000 0 731 731 700.6667 0 768 768 562.5000 0 781 781 851.0000 0 > > ## Averaging to first stage > > dclus1<- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) > pop<-colSums(cbind(1,apipop$enroll),na.rm=TRUE) > > dclus1g<-calibrate(dclus1, ~enroll, pop, aggregate=1) > > svytotal(~enroll,dclus1g) total SE enroll 3811472 0 > svytotal(~api.stu,dclus1g) total SE api.stu 3242857 38967 > > #variation within clusters should be zero > all.equal(0, max(ave(weights(dclus1g),dclus1g$cluster,FUN=var),na.rm=TRUE)) [1] TRUE > > ##bounded weights > dclus1g<-calibrate(dclus1, ~enroll, pop) > range(weights(dclus1g)/weights(dclus1)) [1] 0.7906782 1.7891164 > dclus1gb<-calibrate(dclus1, ~enroll, pop, bounds=c(.6,1.5)) > range(weights(dclus1gb)/weights(dclus1)) [1] 0.7198751 1.5000000 > > ## Ratio estimators > dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) > svytotal(~api.stu,dstrat) total SE api.stu 3086009 99477 > common<-svyratio(~api.stu, ~enroll, dstrat, separate=FALSE) > total.enroll<-sum(apipop$enroll,na.rm=TRUE) > predict(common, total=total.enroll) $total enroll api.stu 3190038 $se enroll api.stu 29565.98 > dstratg<-calibrate(dstrat,~enroll-1, total.enroll, variance=1) > svytotal(~api.stu, dstratg) total SE api.stu 3190038 29566 > > ## postStratify vs calibrate in stratified sample (Ben French) > set.seed(17) > dat<-data.frame(y=rep(0:1,each=100),x=rnorm(200)+2*rep(0:1,each=100), + z=rbinom(200,1,.2), fpc=rep(c(100,10000),each=100)) > dat$w<-ifelse(dat$y,dat$z,1-dat$z) > popw<-data.frame(w=c("0","1"), Freq=c(2000,8000)) > des<-svydesign(id=~1,fpc=~fpc, data=dat,strata=~y) > postStratify(des,~w,popw)->dps > dcal<-calibrate(des,~factor(w), pop=c(10000,8000)) > > all.equal(SE(svymean(~x,dcal)),SE(svymean(~x,dps))) [1] TRUE > > ## missing data in calibrated design > dps$variables$z[1]<-NA > summary(svyglm(y~z+x,design=dps,family=quasibinomial)) Call: svyglm(formula = y ~ z + x, design = dps, family = quasibinomial) Survey design: postStratify(des, ~w, popw) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1203 0.3380 -0.356 0.722 z 6.2118 0.6451 9.630 <2e-16 *** x 2.2602 0.2514 8.992 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 1.919987) Number of Fisher Scoring iterations: 9 > > ## Ratio estimator using the heteroskedasticity parameter (Daniel Oehm) > # should match the ratio estmate above > dstratgh <- calibrate(dstrat,~enroll-1, total.enroll, variance=apistrat$enroll) > svytotal(~api.stu, dstratgh) total SE api.stu 3190038 29566 > > ## individual boundary constraints as multiplicative values (Daniel Oehm) > bnds <- list( + lower = c(1, 1, rep(-Inf, nrow(apistrat)-2)), + upper = c(1, 1, rep(Inf, nrow(apistrat)-2))) # the first two weights will remain unchanged the others are free to move > lapply(bnds, head) $lower [1] 1 1 -Inf -Inf -Inf -Inf $upper [1] 1 1 Inf Inf Inf Inf > dstratg1<-calibrate(dstrat, ~enroll-1, total.enroll, bounds = bnds, variance=apistrat$enroll) > svytotal(~api.stu, dstratg1) total SE api.stu 3190133 29561 > head(weights(dstrat)) 1 2 3 4 5 6 44.21 44.21 44.21 44.21 44.21 44.21 > head(weights(dstratg1)) 1 2 3 4 5 6 44.21000 44.21000 45.72055 45.72055 45.72055 45.72055 > all.equal(weights(dstrat)[1:2], weights(dstratg1)[1:2]) [1] TRUE > > ## individual boundary constraints as constant values (Daniel Oehm) > bnds <- list( + lower = c(44.21, 44.21, rep(-Inf, nrow(apistrat)-2)), + upper = c(44.21, 44.21, rep(Inf, nrow(apistrat)-2))) # the first two weights will remain unchanged > lapply(bnds, head) $lower [1] 44.21 44.21 -Inf -Inf -Inf -Inf $upper [1] 44.21 44.21 Inf Inf Inf Inf > dstratg2<-calibrate(dstrat, ~enroll-1, total.enroll, bounds = bnds, bounds.const = TRUE, variance=apistrat$enroll) > svytotal(~api.stu, dstratg2) total SE api.stu 3190133 29561 > head(weights(dstrat)) 1 2 3 4 5 6 44.21 44.21 44.21 44.21 44.21 44.21 > head(weights(dstratg2)) 1 2 3 4 5 6 44.21000 44.21000 45.72055 45.72055 45.72055 45.72055 > all.equal(round(weights(dstrat)[1:2], 8), round(weights(dstratg2)[1:2]), 8) # minor rounding error but all good [1] TRUE > > # sparse matrix support (Daniel Oehm) > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) > dclus1g<-calibrate(dclus1, ~stype, pop.totals) > svymean(~api00, dclus1g) mean SE api00 642.31 23.921 > svytotal(~enroll, dclus1g) total SE enroll 3680893 406293 > > pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) > dclus1g<-calibrate(dclus1, ~stype, pop.totals, sparse = TRUE) > svymean(~api00, dclus1g) mean SE api00 642.31 23.921 > svytotal(~enroll, dclus1g) total SE enroll 3680893 406293 > > pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) > dclus1g<-calibrate(dclus1, ~stype, pop.totals, sparse = TRUE, calfun = "raking") > svymean(~api00, dclus1g) mean SE api00 642.31 23.921 > svytotal(~enroll, dclus1g) total SE enroll 3680893 406293 > > proc.time() user system elapsed 2.591 0.084 2.712 survey/tests/testoutput/DBIcheck.R0000644000176200001440000000341613517345033016740 0ustar liggesusers library(survey) library(RSQLite) data(api) apiclus1$api_stu<-apiclus1$api.stu apiclus1$comp_imp<-apiclus1$comp.imp dclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc,data=apiclus1) dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey")) m<-svymean(~api00+stype,dclus1) m.db<-svymean(~api00+stype, dbclus1) all.equal(coef(m),coef(m.db)) all.equal(vcov(m), vcov(m.db)) r<-svyratio(~api_stu, ~enroll, design=dclus1) r.db<-svyratio(~api_stu, ~enroll, design=dbclus1) all.equal(coef(r), coef(r.db)) all.equal(SE(r), SE(r.db)) b<-svyby(~api99+api00,~stype, design=dclus1, svymean, deff=TRUE) b.db<-svyby(~api99+api00,~stype, design=dbclus1,svymean, deff=TRUE) all.equal(coef(b), coef(b.db)) all.equal(SE(b), SE(b.db)) all.equal(deff(b), deff(b.db)) l<-svyglm(api00~api99+mobility, design=dclus1) l.db<-svyglm(api00~api99+mobility, design=dbclus1) all.equal(coef(l),coef(l.db)) all.equal(vcov(l), vcov(l.db)) dclus1<-update(dclus1, apidiff=api00-api99) dclus1<-update(dclus1, apipct= apidiff/api99) dbclus1<-update(dbclus1, apidiff=api00-api99) dbclus1<-update(dbclus1, apipct= apidiff/api99) u<-svymean(~api00+apidiff+apipct, dclus1) u.db<-svymean(~api00+apidiff+apipct, dbclus1) all.equal(u, u.db) all.equal(nrow(dclus1),nrow(dbclus1)) ## replicate weights rclus1<-as.svrepdesign(dclus1) db_rclus1<-svrepdesign(weights=~pw, repweights="wt[1-9]+", type="JK1", scale=(1-15/757)*14/15, data="apiclus1rep",dbtype="SQLite", dbname=system.file("api.db",package="survey"),combined.weights=FALSE) m<-svymean(~api00+api99,rclus1) m.db<-svymean(~api00+api99,db_rclus1) all.equal(m,m.db) summary(db_rclus1) s<-svymean(~api00, subset(rclus1, comp_imp=="Yes")) s.db<-svymean(~api00, subset(db_rclus1, comp_imp=="Yes")) all.equal(s,s.db) survey/tests/testoutput/scoping.R0000755000176200001440000000110213517345033016777 0ustar liggesusers ## regression test for testing regression library(survey) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) f<-function(){ form<-acs.46~stype svyglm(formula=form, design = dstrat) } g<-function(form){ svyglm(formula=form, design = dstrat) } f() g(acs.46~stype) f<-function(){ form<-Surv(acs.46)~stype svycoxph(formula=form, design = dstrat) } g<-function(form){ svycoxph(formula=form, design = dstrat) } f() g(Surv(acs.46)~stype) ## check coxph for a single predictor svycoxph(Surv(acs.46)~api00,design=dstrat) survey/tests/testoutput/nwts-cch.Rout.save0000644000176200001440000000610213517345033020552 0ustar liggesusers R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.4.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > library(survival) > data(nwtco) > > ntwco<-subset(nwtco, !is.na(edrel)) > > load("nwtco-subcohort.rda") > nwtco$subcohort<-subcohort > > d_BorganII <- twophase(id=list(~seqno,~seqno), + strata=list(NULL,~interaction(instit,rel)), + data=nwtco, subset=~I(rel |subcohort)) > > ##Coefficient results same as Splus with code from > ## http://faculty.washington.edu/norm/software.html > ## SE slightly larger due to using sandwich variance. > > svycoxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganII) Call: svycoxph(formula = Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), design = d_BorganII) coef exp(coef) se(coef) z p factor(stage)2 0.4629 1.5886 0.1809 2.56 0.0105 factor(stage)3 0.5831 1.7916 0.1785 3.27 0.0011 factor(stage)4 1.0597 2.8854 0.2052 5.16 2.4e-07 factor(histol)2 1.5974 4.9403 0.1334 11.97 < 2e-16 I(age/12) 0.0299 1.0304 0.0334 0.90 0.3697 Likelihood ratio test= on 5 df, p= n= 1062, number of events= 571 > > ## > ## This gives higher standard errors. calibrate() does not recompute the > ## finite population correction if a calibration variable happens to predict > ## sampling perfectly. It probably should. > ## > d_BorganIIps<-calibrate(twophase(id=list(~seqno,~seqno), + strata=list(NULL,~rel), + data=nwtco, subset=~I(rel |subcohort)), + phase=2, formula=~interaction(instit,rel), + epsilon=1e-10) > > svycoxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganIIps) Call: svycoxph(formula = Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), design = d_BorganIIps) coef exp(coef) se(coef) z p factor(stage)2 0.4629 1.5886 0.1808 2.56 0.0104 factor(stage)3 0.5831 1.7916 0.1784 3.27 0.0011 factor(stage)4 1.0597 2.8854 0.2051 5.17 2.4e-07 factor(histol)2 1.5974 4.9403 0.1304 12.25 < 2e-16 I(age/12) 0.0299 1.0304 0.0333 0.90 0.3688 Likelihood ratio test= on 5 df, p= n= 1062, number of events= 571 > > proc.time() user system elapsed 1.823 0.105 1.962 survey/tests/testoutput/caleg.R0000644000176200001440000001023513623322530016407 0ustar liggesusers## ## Calibration examples ## ## Example of calibration to first-stage clusters library(survey) data(api) clusters<-table(apiclus2$dnum) clusters<-clusters[clusters>1 & names(clusters)!="639"] apiclus2a<-subset(apiclus2, dnum %in% as.numeric(names(clusters))) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2a) popclusters<-subset(apipop, dnum %in% as.numeric(names(clusters))) pop<-lapply(as.numeric(names(clusters)), function(cluster) { colSums(model.matrix(~api99, model.frame(~api99, subset(popclusters, dnum %in% cluster))))}) names(pop)<-names(clusters) dclus2g<-calibrate(dclus2, ~api99, pop,stage=1) svymean(~api99, dclus2) svymean(~api99, dclus2g) round(svyby(~api99, ~dnum, design=dclus2, svymean),4) round(svyby(~api99, ~dnum, design=dclus2g, svymean),4) ## Averaging to first stage dclus1<- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) pop<-colSums(cbind(1,apipop$enroll),na.rm=TRUE) dclus1g<-calibrate(dclus1, ~enroll, pop, aggregate=1) svytotal(~enroll,dclus1g) svytotal(~api.stu,dclus1g) #variation within clusters should be zero all.equal(0, max(ave(weights(dclus1g),dclus1g$cluster,FUN=var),na.rm=TRUE)) ##bounded weights dclus1g<-calibrate(dclus1, ~enroll, pop) range(weights(dclus1g)/weights(dclus1)) dclus1gb<-calibrate(dclus1, ~enroll, pop, bounds=c(.6,1.5)) range(weights(dclus1gb)/weights(dclus1)) ## Ratio estimators dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) svytotal(~api.stu,dstrat) common<-svyratio(~api.stu, ~enroll, dstrat, separate=FALSE) total.enroll<-sum(apipop$enroll,na.rm=TRUE) predict(common, total=total.enroll) dstratg<-calibrate(dstrat,~enroll-1, total.enroll, variance=1) svytotal(~api.stu, dstratg) ## postStratify vs calibrate in stratified sample (Ben French) set.seed(17) dat<-data.frame(y=rep(0:1,each=100),x=rnorm(200)+2*rep(0:1,each=100), z=rbinom(200,1,.2), fpc=rep(c(100,10000),each=100)) dat$w<-ifelse(dat$y,dat$z,1-dat$z) popw<-data.frame(w=c("0","1"), Freq=c(2000,8000)) des<-svydesign(id=~1,fpc=~fpc, data=dat,strata=~y) postStratify(des,~w,popw)->dps dcal<-calibrate(des,~factor(w), pop=c(10000,8000)) all.equal(SE(svymean(~x,dcal)),SE(svymean(~x,dps))) ## missing data in calibrated design dps$variables$z[1]<-NA summary(svyglm(y~z+x,design=dps,family=quasibinomial)) ## Ratio estimator using the heteroskedasticity parameter (Daniel Oehm) # should match the ratio estmate above dstratgh <- calibrate(dstrat,~enroll-1, total.enroll, variance=apistrat$enroll) svytotal(~api.stu, dstratgh) ## individual boundary constraints as multiplicative values (Daniel Oehm) bnds <- list( lower = c(1, 1, rep(-Inf, nrow(apistrat)-2)), upper = c(1, 1, rep(Inf, nrow(apistrat)-2))) # the first two weights will remain unchanged the others are free to move lapply(bnds, head) dstratg1<-calibrate(dstrat, ~enroll-1, total.enroll, bounds = bnds, variance=apistrat$enroll) svytotal(~api.stu, dstratg1) head(weights(dstrat)) head(weights(dstratg1)) all.equal(weights(dstrat)[1:2], weights(dstratg1)[1:2]) ## individual boundary constraints as constant values (Daniel Oehm) bnds <- list( lower = c(44.21, 44.21, rep(-Inf, nrow(apistrat)-2)), upper = c(44.21, 44.21, rep(Inf, nrow(apistrat)-2))) # the first two weights will remain unchanged lapply(bnds, head) dstratg2<-calibrate(dstrat, ~enroll-1, total.enroll, bounds = bnds, bounds.const = TRUE, variance=apistrat$enroll) svytotal(~api.stu, dstratg2) head(weights(dstrat)) head(weights(dstratg2)) all.equal(round(weights(dstrat)[1:2], 8), round(weights(dstratg2)[1:2]), 8) # minor rounding error but all good # sparse matrix support (Daniel Oehm) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) dclus1g<-calibrate(dclus1, ~stype, pop.totals) svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) dclus1g<-calibrate(dclus1, ~stype, pop.totals, sparse = TRUE) svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) dclus1g<-calibrate(dclus1, ~stype, pop.totals, sparse = TRUE, calfun = "raking") svymean(~api00, dclus1g) svytotal(~enroll, dclus1g) survey/tests/testoutput/fpc.R0000644000176200001440000000011613517345033016106 0ustar liggesuserslibrary(survey) ## check many permutations of fpc specification example(fpc) survey/tests/testoutput/nwts.rda0000644000176200001440000000261513517345033016704 0ustar liggesusersRDX2 X  nwts    S03% @3< [ud<  !9) names stage instit histol control case class data.frame row.names 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 nwtsb    sVO @3< [ud<  !9) stage instit histol control case data.frame 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16survey/tests/testoutput/bycovmat.Rout.save0000644000176200001440000000745013517345033020657 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(api) > options(survey.replicates.mse=TRUE) > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > rclus1<-as.svrepdesign(dclus1) > > a<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, + covmat=TRUE,drop.empty.groups=FALSE) > b<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, + covmat=TRUE,drop.empty.groups=TRUE) > > stopifnot(all(na.omit( + as.vector(as.matrix(SE(a)))==sqrt(diag(vcov(a))) + ))) > stopifnot(all( + as.vector(as.matrix(SE(b)))==sqrt(diag(vcov(b))) + )) > > rat <- svyratio(~ell+mobility, ~mobility+meals, dclus1,covmat=TRUE) > all <- svytotal(~ell+mobility+meals, dclus1) > > stopifnot(all(abs(vcov(svycontrast(all, + list(quote(ell/mobility), + quote(mobility/mobility), + quote(ell/meals),quote(mobility/meals)))) + -vcov(rat))<1e-10)) > > stopifnot(all(abs(SE(rat)-sqrt(diag(vcov(rat))))<1e-10)) > > rat <- svyratio(~ell+mobility, ~mobility+meals, rclus1,covmat=TRUE) > all <- svytotal(~ell+mobility+meals, rclus1, return.replicates=TRUE) > > con<-svycontrast(all, + list(quote(ell/mobility), + quote(mobility/mobility), + quote(ell/meals),quote(mobility/meals))) > > stopifnot(all(abs(survey:::svrVar(con$replicates, rclus1$scale,rclus1$rscales,mse=rclus1$mse, coef=coef(con))-vcov(rat))<1e-10)) > > options(survey.replicates.mse=FALSE) > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > rclus1<-as.svrepdesign(dclus1) > > a<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, + covmat=TRUE,drop.empty.groups=FALSE) > b<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, + covmat=TRUE,drop.empty.groups=TRUE) > > stopifnot(all(na.omit( + as.vector(as.matrix(SE(a)))==sqrt(diag(vcov(a))) + ))) > stopifnot(all( + as.vector(as.matrix(SE(b)))==sqrt(diag(vcov(b))) + )) > > rat <- svyratio(~ell+mobility, ~mobility+meals, dclus1,covmat=TRUE) > all <- svytotal(~ell+mobility+meals, dclus1) > > stopifnot(all(abs(vcov(svycontrast(all, + list(quote(ell/mobility), + quote(mobility/mobility), + quote(ell/meals),quote(mobility/meals)))) + -vcov(rat))<1e-10)) > > stopifnot(all(abs(SE(rat)-sqrt(diag(vcov(rat))))<1e-10)) > > rat <- svyratio(~ell+mobility, ~mobility+meals, rclus1,covmat=TRUE) > all <- svytotal(~ell+mobility+meals, rclus1, return.replicates=TRUE) > > con<-svycontrast(all, + list(quote(ell/mobility), + quote(mobility/mobility), + quote(ell/meals),quote(mobility/meals))) > > stopifnot(all(abs(survey:::svrVar(con$replicates, rclus1$scale,rclus1$rscales,mse=rclus1$mse, coef=coef(con))-vcov(rat))<1e-10)) > > > > proc.time() user system elapsed 0.299 0.027 0.333 survey/tests/testoutput/fpc.Rout.save0000644000176200001440000000542213517345033017600 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > ## check many permutations of fpc specification > example(fpc) fpc> data(fpc) fpc> fpc stratid psuid weight nh Nh x 1 1 1 3 5 15 2.8 2 1 2 3 5 15 4.1 3 1 3 3 5 15 6.8 4 1 4 3 5 15 6.8 5 1 5 3 5 15 9.2 6 2 1 4 3 12 3.7 7 2 2 4 3 12 6.6 8 2 3 4 3 12 4.2 fpc> withoutfpc<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, variables=~x, fpc+ data=fpc, nest=TRUE) fpc> withoutfpc Stratified Independent Sampling design (with replacement) svydesign(weights = ~weight, ids = ~psuid, strata = ~stratid, variables = ~x, data = fpc, nest = TRUE) fpc> svymean(~x, withoutfpc) mean SE x 5.4481 0.7413 fpc> withfpc<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, fpc+ fpc=~Nh, variables=~x, data=fpc, nest=TRUE) fpc> withfpc Stratified Independent Sampling design svydesign(weights = ~weight, ids = ~psuid, strata = ~stratid, fpc = ~Nh, variables = ~x, data = fpc, nest = TRUE) fpc> svymean(~x, withfpc) mean SE x 5.4481 0.616 fpc> ## Other equivalent forms fpc> withfpc<-svydesign(prob=~I(1/weight), ids=~psuid, strata=~stratid, fpc+ fpc=~Nh, variables=~x, data=fpc, nest=TRUE) fpc> svymean(~x, withfpc) mean SE x 5.4481 0.616 fpc> withfpc<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, fpc+ fpc=~I(nh/Nh), variables=~x, data=fpc, nest=TRUE) fpc> svymean(~x, withfpc) mean SE x 5.4481 0.616 fpc> withfpc<-svydesign(weights=~weight, ids=~interaction(stratid,psuid), fpc+ strata=~stratid, fpc=~I(nh/Nh), variables=~x, data=fpc) fpc> svymean(~x, withfpc) mean SE x 5.4481 0.616 fpc> withfpc<-svydesign(ids=~psuid, strata=~stratid, fpc=~Nh, fpc+ variables=~x,data=fpc,nest=TRUE) fpc> svymean(~x, withfpc) mean SE x 5.4481 0.616 fpc> withfpc<-svydesign(ids=~psuid, strata=~stratid, fpc+ fpc=~I(nh/Nh), variables=~x, data=fpc, nest=TRUE) fpc> svymean(~x, withfpc) mean SE x 5.4481 0.616 > > > proc.time() user system elapsed 0.194 0.025 0.229 survey/tests/testoutput/nwtco-subcohort.rda0000644000176200001440000000166513517345033021055 0ustar liggesusersY9A,]x Xkx/@ƠVUfFDF{22./߿rbwϻo?>}~o~5z\պި}ݵe +sZ}=}#|vd߳+nh{WשЕv*v+}j|OٸE! (B95_U:=ۅgC}|b y@5ĭw+6\⻋w}PQ9(~?,q+]xY;876u翳[ydtuXXP}qv®OU{Yzy]GVݕ(^4*}uY/;djvzY@=q;ہ+@ VՏo(\ƫ:_fկ,XЅ6ounj]sdt|A(;Ϲ+*M>Q7n~Y}WB7<AUi]|UV?.}>xf>5W>ۥOq]__ˢ:tٞl/;Z]S͗,ނ͋ѝfDmw^Qq#]Γh9<>Ghu>fCclTMvXۮsUt+?<EW#(Gr*OPq?vՇj|/\ջү\Vxut}@wi֎<^]o+ޣkE|bWW\gQ·]y7x(\xS5łVÍGsW,Ab?H~survey/tests/testoutput/nwts-cch.R0000644000176200001440000000215213517345033017066 0ustar liggesuserslibrary(survey) library(survival) data(nwtco) ntwco<-subset(nwtco, !is.na(edrel)) load("nwtco-subcohort.rda") nwtco$subcohort<-subcohort d_BorganII <- twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(instit,rel)), data=nwtco, subset=~I(rel |subcohort)) ##Coefficient results same as Splus with code from ## http://faculty.washington.edu/norm/software.html ## SE slightly larger due to using sandwich variance. svycoxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganII) ## ## This gives higher standard errors. calibrate() does not recompute the ## finite population correction if a calibration variable happens to predict ## sampling perfectly. It probably should. ## d_BorganIIps<-calibrate(twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), data=nwtco, subset=~I(rel |subcohort)), phase=2, formula=~interaction(instit,rel), epsilon=1e-10) svycoxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganIIps) survey/tests/testoutput/bycovmat.R0000644000176200001440000000563313517345033017173 0ustar liggesusers library(survey) data(api) options(survey.replicates.mse=TRUE) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) a<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=FALSE) b<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=TRUE) stopifnot(all.equal( as.vector(as.matrix(SE(a))),as.vector(sqrt(diag(vcov(a)))) )) stopifnot(all.equal( as.vector(as.matrix(SE(b))),as.vector(sqrt(diag(vcov(b)))) )) rat <- svyratio(~ell+mobility, ~mobility+meals, dclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, dclus1) stopifnot(all(abs(vcov(svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals)))) -vcov(rat))<1e-10)) stopifnot(all(abs(SE(rat)-sqrt(diag(vcov(rat))))<1e-10)) rat <- svyratio(~ell+mobility, ~mobility+meals, rclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, rclus1, return.replicates=TRUE) con<-svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals))) stopifnot(all(abs(survey:::svrVar(con$replicates, rclus1$scale,rclus1$rscales,mse=rclus1$mse, coef=coef(con))-vcov(rat))<1e-10)) options(survey.replicates.mse=FALSE) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) a<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=FALSE) b<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=TRUE) stopifnot(all.equal( as.vector(as.matrix(SE(a))), as.vector(sqrt(diag(vcov(a)))) )) stopifnot(all.equal( as.vector(as.matrix(SE(b))), as.vector(sqrt(diag(vcov(b)))) )) rat <- svyratio(~ell+mobility, ~mobility+meals, dclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, dclus1) stopifnot(all(abs(vcov(svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals)))) -vcov(rat))<1e-10)) stopifnot(all(abs(SE(rat)-sqrt(diag(vcov(rat))))<1e-10)) rat <- svyratio(~ell+mobility, ~mobility+meals, rclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, rclus1, return.replicates=TRUE) con<-svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals))) stopifnot(all(abs(survey:::svrVar(con$replicates, rclus1$scale,rclus1$rscales,mse=rclus1$mse, coef=coef(con))-vcov(rat))<1e-10)) survey/tests/testoutput/multistage.R0000644000176200001440000000012313517345033017512 0ustar liggesusers## ## Check that multistage samples still work ## library(survey) example(mu284) survey/tests/testoutput/pps.Rout.save0000644000176200001440000000763113517345033017636 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(election) > > dpps<- svydesign(id=~1, weights=~wt, fpc=~p, data=election_pps, pps="brewer") > dppswr <-svydesign(id=~1, weights=~wt, data=election_pps) > svytotal(~Bush+Kerry+Nader, dpps) total SE Bush 64518472 2447629 Kerry 51202102 2450787 Nader 478530 102420 > svytotal(~Bush+Kerry+Nader, dppswr) total SE Bush 64518472 2671455 Kerry 51202102 2679433 Nader 478530 105303 > > ##subsets > svytotal(~Bush+Kerry+Nader, subset(dpps, Nader>0)) total SE Bush 34944285 5399833 Kerry 25581714 4028434 Nader 478530 102420 > > ##multistage: should agree with STRS analysis > data(api) > dclus2<-svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2) > dclus2pps<-svydesign(id = ~dnum + snum, fpc = ~I(40/fpc1) + I(pmin(1,5/fpc2)), data = apiclus2) > > all.equal(svytotal(~sch.wide,dclus2), svytotal(~sch.wide,dclus2pps)) [1] TRUE > all.equal(svymean(~sch.wide,dclus2), svymean(~sch.wide,dclus2pps)) [1] TRUE > all.equal(svytotal(~enroll,dclus2), svytotal(~enroll,dclus2pps)) [1] TRUE > > ## the new without-replacement methods > data(election) > dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") > dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") > dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) > dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) > dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) > ## Yates-Grundy type > dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") > dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") > > ## The with-replacement approximation > svytotal(~Bush+Kerry+Nader, dpps_ht) total SE Bush 64518472 2604404 Kerry 51202102 2523712 Nader 478530 102326 > svytotal(~Bush+Kerry+Nader, dpps_yg) total SE Bush 64518472 2406526 Kerry 51202102 2408091 Nader 478530 101664 > svytotal(~Bush+Kerry+Nader, dpps_hr) total SE Bush 64518472 2624662 Kerry 51202102 2525222 Nader 478530 102793 > svytotal(~Bush+Kerry+Nader, dpps_hryg) total SE Bush 64518472 2436738 Kerry 51202102 2439845 Nader 478530 102016 > svytotal(~Bush+Kerry+Nader, dpps_hr1) total SE Bush 64518472 2472753 Kerry 51202102 2426842 Nader 478530 102595 > svytotal(~Bush+Kerry+Nader, dpps_br) total SE Bush 64518472 2447629 Kerry 51202102 2450787 Nader 478530 102420 > svytotal(~Bush+Kerry+Nader, dpps_ov) total SE Bush 64518472 2939608 Kerry 51202102 1964632 Nader 478530 104373 > > ## subsets > svytotal(~Bush+Kerry+Nader, subset(dpps_ht, Nader>0)) total SE Bush 34944285 5406348 Kerry 25581714 4047741 Nader 478530 102326 > svytotal(~Bush+Kerry+Nader, subset(dpps_hryg, Nader>0)) total SE Bush 34944285 5377659 Kerry 25581714 4010908 Nader 478530 102016 > > ## counts > svyby(~Bush+Kerry+Nader,~I(Nader>0), unwtd.count,design=dpps_ht) I(Nader > 0) counts se FALSE FALSE 19 0 TRUE TRUE 21 0 > > proc.time() user system elapsed 1.946 0.058 2.017 survey/tests/testoutput/quantile-new.R0000644000176200001440000000135214411725742017755 0ustar liggesusers## From Ben Schneider, https://github.com/bschneidr/r-forge-survey-mirror/pull/7 library(survey) data('api', package = 'survey') boot_design <- svydesign( ids = ~ 1, strata = ~ stype, weights = ~ pw, data = apistrat, ) |> as.svrepdesign(type = "boot") # Attempt to estimate variance of quantile using direct replication ---- new <- svyquantile( x = ~ api00 + api99, quantiles = c(0.25, 0.75), design = boot_design, interval.type = "quantile", return.replicates = TRUE ) print(new) old <- oldsvyquantile( x = ~ api00 + api99, quantiles = c(0.25, 0.75), design = boot_design, interval.type = "quantile", return.replicates = TRUE ) print(old) confint(new) confint(old) survey/tests/testoutput/quantile-new.Rout.save0000644000176200001440000000407114411726510021435 0ustar liggesusers R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## From Ben Schneider, https://github.com/bschneidr/r-forge-survey-mirror/pull/7 > library(survey) > data('api', package = 'survey') > > boot_design <- svydesign( + ids = ~ 1, strata = ~ stype, + weights = ~ pw, + data = apistrat, + ) |> as.svrepdesign(type = "boot") > > # Attempt to estimate variance of quantile using direct replication ---- > new <- svyquantile( + x = ~ api00 + api99, + quantiles = c(0.25, 0.75), + design = boot_design, + interval.type = "quantile", + return.replicates = TRUE + ) > > print(new) Statistic: api00 api99 q0.25 565 526 q0.75 756 728 SE: api00 api99 q0.25 19.22732 14.45368 q0.75 13.72320 19.50807 > > old <- oldsvyquantile( + x = ~ api00 + api99, + quantiles = c(0.25, 0.75), + design = boot_design, + interval.type = "quantile", + return.replicates = TRUE + ) > > print(old) Statistic: api00 api99 q0.25 562.2056 525.4800 q0.75 755.1226 726.7813 SE: api00 api99 q0.25 18.91980 14.53474 q0.75 14.13114 18.73837 > > confint(new) 2.5 % 97.5 % q0.25_api00 527.3152 602.6848 q0.75_api00 729.1030 782.8970 q0.25_api99 497.6713 554.3287 q0.75_api99 689.7649 766.2351 > confint(old) 2.5 % 97.5 % q0.25_api00 525.1235 599.2877 q0.75_api00 727.4261 782.8191 q0.25_api99 496.9924 553.9676 q0.75_api99 690.0547 763.5078 > survey/tests/testoutput/DBIcheck.Rout.save0000644000176200001440000001002613517345033020420 0ustar liggesusers R version 3.4.1 (2017-06-30) -- "Single Candle" Copyright (C) 2017 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin15.6.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: ‘survey’ The following object is masked from ‘package:graphics’: dotchart > library(RSQLite) > > data(api) > apiclus1$api_stu<-apiclus1$api.stu > apiclus1$comp_imp<-apiclus1$comp.imp > dclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc,data=apiclus1) > dbclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, + data="apiclus1",dbtype="SQLite", dbname=system.file("api.db",package="survey")) > > m<-svymean(~api00+stype,dclus1) > m.db<-svymean(~api00+stype, dbclus1) > all.equal(coef(m),coef(m.db)) [1] TRUE > all.equal(vcov(m), vcov(m.db)) [1] TRUE > > r<-svyratio(~api_stu, ~enroll, design=dclus1) > r.db<-svyratio(~api_stu, ~enroll, design=dbclus1) > all.equal(coef(r), coef(r.db)) [1] TRUE > all.equal(SE(r), SE(r.db)) [1] TRUE > > b<-svyby(~api99+api00,~stype, design=dclus1, svymean, deff=TRUE) > b.db<-svyby(~api99+api00,~stype, design=dbclus1,svymean, deff=TRUE) > all.equal(coef(b), coef(b.db)) [1] TRUE > all.equal(SE(b), SE(b.db)) [1] TRUE > all.equal(deff(b), deff(b.db)) [1] TRUE > > l<-svyglm(api00~api99+mobility, design=dclus1) > l.db<-svyglm(api00~api99+mobility, design=dbclus1) > all.equal(coef(l),coef(l.db)) [1] TRUE > all.equal(vcov(l), vcov(l.db)) [1] TRUE > > dclus1<-update(dclus1, apidiff=api00-api99) > dclus1<-update(dclus1, apipct= apidiff/api99) > dbclus1<-update(dbclus1, apidiff=api00-api99) > dbclus1<-update(dbclus1, apipct= apidiff/api99) > > u<-svymean(~api00+apidiff+apipct, dclus1) > u.db<-svymean(~api00+apidiff+apipct, dbclus1) > all.equal(u, u.db) [1] TRUE > > all.equal(nrow(dclus1),nrow(dbclus1)) [1] TRUE > > ## replicate weights > rclus1<-as.svrepdesign(dclus1) > db_rclus1<-svrepdesign(weights=~pw, repweights="wt[1-9]+", type="JK1", scale=(1-15/757)*14/15, + data="apiclus1rep",dbtype="SQLite", dbname=system.file("api.db",package="survey"),combined.weights=FALSE) > m<-svymean(~api00+api99,rclus1) > m.db<-svymean(~api00+api99,db_rclus1) > all.equal(m,m.db) [1] TRUE > > summary(db_rclus1) DB-backed replicate weight design Call: svrepdesign(weights = ~pw, repweights = "wt[1-9]+", type = "JK1", scale = (1 - 15/757) * 14/15, data = "apiclus1rep", dbtype = "SQLite", dbname = system.file("api.db", package = "survey"), combined.weights = FALSE) Unstratified cluster jacknife (JK1) with 15 replicates. Variables: [1] "row_names" "cds" "stype" "name" "sname" [6] "snum" "dname" "dnum" "cname" "cnum" [11] "flag" "pcttest" "api00" "api99" "target" [16] "growth" "sch_wide" "comp_imp" "both" "awards" [21] "meals" "ell" "yr_rnd" "mobility" "acs_k3" [26] "acs_46" "acs_core" "pct_resp" "not_hsg" "hsg" [31] "some_col" "col_grad" "grad_sch" "avg_ed" "full__1" [36] "emer" "enroll" "api_stu" "fpc" "pw" [41] "row_names:1" "wt1" "wt2" "wt3" "wt4" [46] "wt5" "wt6" "wt7" "wt8" "wt9" [51] "wt10" "wt11" "wt12" "wt13" "wt14" [56] "wt15" > > s<-svymean(~api00, subset(rclus1, comp_imp=="Yes")) > s.db<-svymean(~api00, subset(db_rclus1, comp_imp=="Yes")) > all.equal(s,s.db) [1] TRUE > survey/tests/testoutput/pps.R0000644000176200001440000000353213517345033016145 0ustar liggesuserslibrary(survey) data(election) dpps<- svydesign(id=~1, weights=~wt, fpc=~p, data=election_pps, pps="brewer") dppswr <-svydesign(id=~1, weights=~wt, data=election_pps) svytotal(~Bush+Kerry+Nader, dpps) svytotal(~Bush+Kerry+Nader, dppswr) ##subsets svytotal(~Bush+Kerry+Nader, subset(dpps, Nader>0)) ##multistage: should agree with STRS analysis data(api) dclus2<-svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2) dclus2pps<-svydesign(id = ~dnum + snum, fpc = ~I(40/fpc1) + I(pmin(1,5/fpc2)), data = apiclus2) all.equal(svytotal(~sch.wide,dclus2), svytotal(~sch.wide,dclus2pps)) all.equal(svymean(~sch.wide,dclus2), svymean(~sch.wide,dclus2pps)) all.equal(svytotal(~enroll,dclus2), svytotal(~enroll,dclus2pps)) ## the new without-replacement methods data(election) dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) ## Yates-Grundy type dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") ## The with-replacement approximation svytotal(~Bush+Kerry+Nader, dpps_ht) svytotal(~Bush+Kerry+Nader, dpps_yg) svytotal(~Bush+Kerry+Nader, dpps_hr) svytotal(~Bush+Kerry+Nader, dpps_hryg) svytotal(~Bush+Kerry+Nader, dpps_hr1) svytotal(~Bush+Kerry+Nader, dpps_br) svytotal(~Bush+Kerry+Nader, dpps_ov) ## subsets svytotal(~Bush+Kerry+Nader, subset(dpps_ht, Nader>0)) svytotal(~Bush+Kerry+Nader, subset(dpps_hryg, Nader>0)) ## counts svyby(~Bush+Kerry+Nader,~I(Nader>0), unwtd.count,design=dpps_ht) survey/tests/testoutput/api.R0000644000176200001440000000016413517345033016112 0ustar liggesuserslibrary(survey) options(survey.replicates.mse=TRUE) example(api) options(survey.replicates.mse=FALSE) example(api) survey/tests/bycovmat.R0000644000176200001440000000563313517345033014733 0ustar liggesusers library(survey) data(api) options(survey.replicates.mse=TRUE) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) a<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=FALSE) b<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=TRUE) stopifnot(all.equal( as.vector(as.matrix(SE(a))),as.vector(sqrt(diag(vcov(a)))) )) stopifnot(all.equal( as.vector(as.matrix(SE(b))),as.vector(sqrt(diag(vcov(b)))) )) rat <- svyratio(~ell+mobility, ~mobility+meals, dclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, dclus1) stopifnot(all(abs(vcov(svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals)))) -vcov(rat))<1e-10)) stopifnot(all(abs(SE(rat)-sqrt(diag(vcov(rat))))<1e-10)) rat <- svyratio(~ell+mobility, ~mobility+meals, rclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, rclus1, return.replicates=TRUE) con<-svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals))) stopifnot(all(abs(survey:::svrVar(con$replicates, rclus1$scale,rclus1$rscales,mse=rclus1$mse, coef=coef(con))-vcov(rat))<1e-10)) options(survey.replicates.mse=FALSE) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) a<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=FALSE) b<-svyby(~api00+api99, ~comp.imp+sch.wide,design=rclus1,svymean, covmat=TRUE,drop.empty.groups=TRUE) stopifnot(all.equal( as.vector(as.matrix(SE(a))), as.vector(sqrt(diag(vcov(a)))) )) stopifnot(all.equal( as.vector(as.matrix(SE(b))), as.vector(sqrt(diag(vcov(b)))) )) rat <- svyratio(~ell+mobility, ~mobility+meals, dclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, dclus1) stopifnot(all(abs(vcov(svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals)))) -vcov(rat))<1e-10)) stopifnot(all(abs(SE(rat)-sqrt(diag(vcov(rat))))<1e-10)) rat <- svyratio(~ell+mobility, ~mobility+meals, rclus1,covmat=TRUE) all <- svytotal(~ell+mobility+meals, rclus1, return.replicates=TRUE) con<-svycontrast(all, list(quote(ell/mobility), quote(mobility/mobility), quote(ell/meals),quote(mobility/meals))) stopifnot(all(abs(survey:::svrVar(con$replicates, rclus1$scale,rclus1$rscales,mse=rclus1$mse, coef=coef(con))-vcov(rat))<1e-10)) survey/tests/badcal.R0000644000176200001440000000063413517345033014311 0ustar liggesusers## ## Calibration with badly-scaled initial weights (bug report by Takahiro Tsuchiya) ## library(survey) data <- data.frame(x=c(1,1,1,1,2,2,2,2,2,2), w=rep(10,10)) des <- svydesign(ids=~1, weights=~w, data=data) des.c <- calibrate(des, ~factor(x), c(10000, 5000)) des.r <- calibrate(des, ~factor(x), c(10000, 5000), calfun='raking') stopifnot(all.equal(svytotal(~factor(x), des.c), svytotal(~factor(x), des.r))) survey/tests/svyby_se.R0000644000176200001440000000042613714425162014746 0ustar liggesuserslibrary(survey) data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) a<-svyby(~enroll,~stype,design=dstrat,svytotal,vartype=c("ci","se")) b<-svyby(~enroll,~stype,design=dstrat,svytotal,vartype=c("se","ci")) stopifnot(all.equal(SE(a),SE(b))) survey/tests/rss_scores.R0000644000176200001440000000114714114021600015251 0ustar liggesusers## Example from Rao, Scott, and Skinner 1998 Statistica Sinica library(survey) data(myco) dmyco<-svydesign(id=~1, strata=~interaction(Age,leprosy),weights=~wt,data=myco) m_full<-svyglm(leprosy~I((Age+7.5)^-2)+Scar, family=quasibinomial, design=dmyco) m_null<-svyglm(leprosy~I((Age+7.5)^-2), family=quasibinomial, design=dmyco) stopifnot(isTRUE(all.equal(coef(m_null), c(`(Intercept)`=-4.6, `I((Age + 7.5)^-2)`=-427),tol=1e-2))) s<-svyscoretest(m_full, ~Scar) stopifnot(abs(s[1]-10.73)<0.05) t<-svyscoretest(m_full,~Scar,method="individual") stopifnot(abs(coef(t)- -32.61)<0.1) stopifnot(abs(vcov(t)-99.1)<0.1) survey/tests/multistage.R0000644000176200001440000000012313517345033015252 0ustar liggesusers## ## Check that multistage samples still work ## library(survey) example(mu284) survey/tests/nwts.R0000644000176200001440000000237213517345033014077 0ustar liggesusers ## examples from Breslow & Chatterjee: Applied Statistics 1999 No. 4, p458 ## data from Norman Breslow's web page. library(survey) load("nwts.rda") nwtsnb<-nwts nwtsnb$case<-nwts$case-nwtsb$case nwtsnb$control<-nwts$control-nwtsb$control a<-rbind(nwtsb,nwtsnb) a$in.ccs<-rep(c(TRUE,FALSE),each=16) b<-rbind(a,a) b$rel<-rep(c(1,0),each=32) b$n<-ifelse(b$rel,b$case,b$control) index<-rep(1:64,b$n) nwt.exp<-b[index,c(1:3,6,7)] nwt.exp$id<-1:4088 dccs2<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,rel)),data=nwt.exp) dccs8<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,stage,rel)),data=nwt.exp) gccs8<-calibrate(dccs2,phase=2,formula=~interaction(instit,stage,rel)) summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs2)) summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs8)) summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=gccs8)) ## check subsets of calibrated designs. summary(svyglm(rel~factor(stage), family=quasibinomial,design=subset(dccs8,histol==1))) summary(svyglm(rel~factor(stage), family=quasibinomial,design=subset(gccs8,histol==1))) survey/tests/regpredict.Rout.save0000644000176200001440000000472013517345033016720 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(api) > dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) > > > ## regression estimator of total, three ways > pop<-data.frame(enroll=sum(apipop$enroll, na.rm=TRUE)) > npop <- sum(!is.na(apipop$enroll)) > > api.reg <- svyglm(api.stu~enroll, design=dstrat) > a <- predict(api.reg, newdata=pop, total=npop) > b <- svytotal(~api.stu, calibrate(dstrat, ~enroll, pop=c(npop, pop$enroll))) > > all.equal(as.vector(coef(a)),as.vector(coef(b))) [1] TRUE > all.equal(as.vector(SE(a)), as.vector(SE(b))) [1] TRUE > if(!is.null(getOption("DEBUG"))){ ## uses 6194x6194 matrix + d <- predict(api.reg, newdata=na.omit(apipop[,"enroll",drop=FALSE])) + all.equal(as.vector(coef(a)), as.vector(sum(coef(d)))) + all.equal(as.vector(SE(a)), as.vector(sqrt(sum(vcov(d))))) + } > > ## classical ratio estimator, four ways. > api.reg2 <- svyglm(api.stu~enroll-1, design=dstrat, + family=quasi(link="identity", var="mu")) > > a <- predict(api.reg2, newdata=pop, total=npop) > b <- svytotal(~api.stu, + calibrate(dstrat, ~enroll-1, pop= pop$enroll, variance=2)) > e <- predict(svyratio(~api.stu, ~enroll, dstrat),total=pop$enroll) > > all.equal(as.vector(coef(a)),as.vector(coef(b))) [1] TRUE > all.equal(as.vector(SE(a)), as.vector(SE(b))) [1] TRUE > all.equal(as.vector(coef(a)),as.vector(e$total)) [1] TRUE > all.equal(as.vector(SE(a)), as.vector(e$se)) [1] TRUE > if(!is.null(getOption("DEBUG"))){## uses 6194x6194 matrix + d <- predict(api.reg2, newdata=na.omit(apipop[,"enroll",drop=FALSE])) + all.equal(as.vector(coef(a)), as.vector(sum(coef(d)))) + all.equal(as.vector(SE(a)), as.vector(sqrt(sum(vcov(d))))) + } > > proc.time() user system elapsed 0.239 0.026 0.272 survey/tests/svyolr-rake-subset.R0000644000176200001440000000145614204303154016657 0ustar liggesuserslibrary(survey) data(api) dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) dclus1<-update(dclus1, mealcat=cut(meals,c(0,25,50,75,100))) ## population marginal totals for each stratum pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) ## rake with the population totals dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) # works m <- svyolr(mealcat~avg.ed+mobility+stype, design=dclus1) # fails in 4.1 (should work because svyolr's default na.action is na.omit) m2 <- svyolr(mealcat~avg.ed+mobility+stype, design=dclus1r) # fails in 4.1 (should work because NA values are subsetted out) m3 <- svyolr(mealcat~avg.ed+mobility+stype, design=subset( dclus1r , !is.na( avg.ed ) ) ) survey/tests/logranktest.R0000644000176200001440000000734213714137352015445 0ustar liggesusers## StackOverflow 63333282 library(survey) D = data.frame(unique_id = 1:135, weights = rep(1,135), event_time = c(0.53512437, 1.35655869, 2.00414189, 2.37276648, 3.20343526, 0.96618494, 2.57894309, 0.94575080, 1.25347833, 1.44416450, 5.04038200, 7.80587169 , 6.53631154, 6.31914568, 7.00146597, 9.67616088, 7.94212358, 9.70693890, 10.67575835, 10.06764688, 12.29175616, 13.60092871, 13.12508566, 14.66417522, 15.35250691, 0.93368707, 0.19087611, 3.15533767, 4.40821633, 17.54334957, 17.95177642, 15.50903946, 16.48376185, 20.87956697, 21.24571398, 22.34297263, 23.36042629, 21.01760215, 23.84785038, 26.06105822, 4.16866350, 1.96922485, 0.66199008, 6.76987830, 1.55617685, 0.19095871, 3.13291784, 5.43159409, 9.55805671, 4.31437322, 0.78259860, 5.26415156, 3.45095686, 1.69128712, 8.41942426, 3.33748695, 6.08516173, 2.72897404, 0.22789783, 0.86348009, 2.35707587, 2.97477615, 12.33273800, 0.58532123, 0.14586238, 10.67948547, 4.07655972, 3.94405136, 0.37226898, 1.42558725, 1.47680658, 4.22506540, 1.56703478, 8.37484756, 12.54015087, 1.80994787, 3.66453633, 1.02834532, 1.99065652, 1.23577436, 16.21981618, 14.35039798, 4.15321606, 2.79740679, 0.35538726, 7.46823358, 1.66329088, 7.46525382, 2.62734831, 3.19057957, 0.33317193, 0.09122886, 9.14616245, 2.48542578, 2.37569263, 5.48499630, 2.22749399, 2.64816296, 0.97101545, 1.42468625, 1.27668904, 0.03692447, 1.98783210, 5.47692729, 3.88316178, 0.32921277, 1.77225345, 9.33268901, 2.44517775, 1.49813702, 2.56059172, 3.43194832, 1.22955630, 3.56263947, 9.07060099, 3.58312362, 2.22755370, 4.24783776, 3.46364804, 1.61671354, 11.10973565, 7.18764270, 1.80400046, 6.39833474, 6.72825192, 6.46063344, 5.76855531, 5.27157807, 4.66154734, 3.50019718, 2.27156678, 3.28531594, 2.35699896, 2.94956000, 8.85381736), event_flag = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0), group = c(rep("group1", 40), rep("group2", 95))) svykm_formula = as.formula("Surv(event_time, event_flag) ~ group") svy_design = svydesign( ids = ~unique_id , weights = ~weights, data = D ) a<-svylogrank(formula = svykm_formula, design = svy_design) ii<-with(D, order(event_time, event_flag)) svy_design2 = svydesign( ids = ~unique_id , weights = ~weights, data = D[ii,] ) b<-svylogrank(formula = svykm_formula, design = svy_design2) d<-svylogrank(formula = svykm_formula, design = svy_design, method = "large") f<- svylogrank(formula = svykm_formula, design = svy_design, method = "score") stopifnot(all.equal(a[[2]],b[[2]])) stopifnot(all.equal(a[[2]],d[[2]])) stopifnot(all.equal(a[[2]],f[-1])) survey/tests/rakecheck.Rout.save0000644000176200001440000001112313517345033016503 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > > data(api) > dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > rclus1 <- as.svrepdesign(dclus1) > > ## population marginal totals for each stratum > pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) > pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) > > rclus1r <- rake(rclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) > > svymean(~api00, rclus1r) mean SE api00 641.23 26.873 > svytotal(~enroll, rclus1r) total SE enroll 3647300 463511 > > ff<-~stype+sch.wide > poptotals<-colSums(model.matrix(ff,model.frame(ff,apipop))) > rclus1g<-calibrate(rclus1, ~stype+sch.wide, poptotals,calfun="raking") Loading required package: MASS > > svymean(~api00,rclus1g) mean SE api00 641.23 26.874 > svytotal(~enroll,rclus1g) total SE enroll 3647280 463582 > > summary(weights(rclus1g)/weights(rclus1r)) V1 V2 V3 V4 V5 V6 Min. :1 Min. :1 Min. :1 Min. :1 Min. :1 Min. :1 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1 Median :1 Median :1 Median :1 Median :1 Median :1 Median :1 Mean :1 Mean :1 Mean :1 Mean :1 Mean :1 Mean :1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 Max. :1 Max. :1 Max. :1 Max. :1 Max. :1 Max. :1 NA's :11 NA's :4 NA's :2 NA's :13 NA's :2 NA's :4 V7 V8 V9 V10 V11 Min. :1 Min. :1 Min. :1 Min. :1 Min. :1 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1 Median :1 Median :1 Median :1 Median :1 Median :1 Mean :1 Mean :1 Mean :1 Mean :1 Mean :1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 Max. :1 Max. :1 Max. :1 Max. :1 Max. :1 NA's :4 NA's :16 NA's :9 NA's :34 NA's :21 V12 V13 V14 V15 Min. :0.9997 Min. :1 Min. :1 Min. :1 1st Qu.:1.0001 1st Qu.:1 1st Qu.:1 1st Qu.:1 Median :1.0001 Median :1 Median :1 Median :1 Mean :1.0000 Mean :1 Mean :1 Mean :1 3rd Qu.:1.0001 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 Max. :1.0002 Max. :1 Max. :1 Max. :1 NA's :37 NA's :13 NA's :1 NA's :12 > > > ## Do it for a design without replicate weights > dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) > > svymean(~api00, dclus1r) mean SE api00 641.23 23.704 > svytotal(~enroll, dclus1r) total SE enroll 3647300 400603 > > dclus1g<-calibrate(dclus1, ~stype+sch.wide, poptotals,calfun="raking") > > svymean(~api00,dclus1g) mean SE api00 641.23 23.704 > svytotal(~enroll,dclus1g) total SE enroll 3647280 400603 > > summary(weights(dclus1g)/weights(dclus1r)) Min. 1st Qu. Median Mean 3rd Qu. Max. 1 1 1 1 1 1 > > > > ## Example of raking with partial joint distributions > pop.table <- xtabs(~stype+sch.wide,apipop) > pop.imp<-data.frame(comp.imp=c("No","Yes"),Freq=c(1712,4482)) > dclus1r2<-rake(dclus1, list(~stype+sch.wide, ~comp.imp), + list(pop.table, pop.imp)) > svymean(~api00, dclus1r2) mean SE api00 642.62 22.732 > > ff1 <-~stype*sch.wide+comp.imp > > poptotals1<-colSums(model.matrix(ff1,model.frame(ff1,apipop))) > dclus1g2<-calibrate(dclus1, ~stype*sch.wide+comp.imp, poptotals1, calfun="raking") > > svymean(~api00, dclus1g2) mean SE api00 642.61 22.731 > > summary(weights(dclus1r2)/weights(dclus1g2)) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.999 1.000 1.000 1.000 1.000 1.002 > > proc.time() user system elapsed 0.459 0.032 0.499 survey/tests/mtcars-var.R0000644000176200001440000000072014141640222015146 0ustar liggesuserslibrary(survey) # don't throw an error on domains of size 1, just return NA input <- mtcars input$carb <- factor(input$carb) design <- svydesign(ids = ~0, weights = NULL, data = input) svyby( ~mpg, ~carb, design, svyvar ) ## same n with na.rm=TRUE as subset(, !is.na) input$mpg[1]<-NA design <- svydesign(ids = ~0, weights = NULL, data = input) stopifnot(all.equal(svyvar(~mpg, design, na.rm=TRUE), svyvar(~mpg, subset(design, !is.na(mpg))))) survey/tests/contrast-replicates.R0000644000176200001440000000133113642747466017103 0ustar liggesusers## test use of replicates in svyby, svycontrast library(survey) data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) rclus1<-as.svrepdesign(dclus1) meanlogs_without<-svyby(~log(enroll),~stype,svymean, design=rclus1,covmat=TRUE) c_without<-svycontrast(meanlogs_without, quote(exp(E-H))) vcov(c_without) meanlogs_with<-svyby(~log(enroll),~stype,svymean, design=rclus1,covmat=TRUE,return.replicates=TRUE) c_with<-svycontrast(meanlogs_with, quote(exp(E-H))) v_with<- vcov(rclus1, c_with$replicates) r<- attr(meanlogs_with, "replicates") vr_with<-vcov(rclus1,exp(r[,1]-r[,2])) stopifnot(all.equal(as.numeric(v_with),as.numeric(vr_with))) stopifnot(all.equal(as.numeric(v_with),as.numeric(vcov(c_with)))) survey/tests/twophase.Rout.save0000644000176200001440000002136014567002133016416 0ustar liggesusers R Under development (unstable) (2024-01-29 r85841) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > > ## two-phase simple random sampling. > data(pbc, package="survival") > pbc$id<-1:nrow(pbc) > pbc$randomized<-with(pbc, !is.na(trt) & trt>-9) > (d2pbc<-twophase(id=list(~id,~id), data=pbc, subset=~I(!randomized))) Two-phase sparse-matrix design: twophase2(id = id, strata = strata, probs = probs, fpc = fpc, subset = subset, data = data, pps = pps) Phase 1: Independent Sampling design (with replacement) svydesign(ids = ~id) Phase 2: Independent Sampling design svydesign(ids = ~id, fpc = `*phase1*`) > m<-svymean(~bili, d2pbc) > all.equal(as.vector(coef(m)),with(pbc, mean(bili[!randomized]))) [1] TRUE > all.equal(as.vector(SE(m)), + with(pbc, sd(bili[!randomized])/sqrt(sum(!randomized))), + tolerance=0.002) [1] TRUE > > ## two-stage sampling as two-phase > data(mu284) > ii<-with(mu284, c(1:15, rep(1:5,n2[1:5]-3))) > mu284.1<-mu284[ii,] > mu284.1$id<-1:nrow(mu284.1) > mu284.1$sub<-rep(c(TRUE,FALSE),c(15,34-15)) > dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) > ## first phase cluster sample, second phase stratified within cluster > (d2mu284<-twophase(id=list(~id1,~id),strata=list(NULL,~id1), + fpc=list(~n1,NULL),data=mu284.1,subset=~sub,method="approx")) Two-phase design: twophase(id = list(~id1, ~id), strata = list(NULL, ~id1), fpc = list(~n1, NULL), data = mu284.1, subset = ~sub, method = "approx") Phase 1: 1 - level Cluster Sampling design With (5) clusters. svydesign(ids = ~id1, fpc = ~n1) Phase 2: Stratified Independent Sampling design svydesign(ids = ~id, strata = ~id1, fpc = `*phase1*`) > (d22mu284<-twophase(id=list(~id1,~id),strata=list(NULL,~id1), + fpc=list(~n1,NULL),data=mu284.1,subset=~sub,method="full")) Two-phase sparse-matrix design: twophase2(id = id, strata = strata, probs = probs, fpc = fpc, subset = subset, data = data, pps = pps) Phase 1: 1 - level Cluster Sampling design With (5) clusters. svydesign(ids = ~id1, fpc = ~n1) Phase 2: Stratified Independent Sampling design svydesign(ids = ~id, strata = ~id1, fpc = `*phase1*`) > summary(d2mu284) Two-phase design: twophase(id = list(~id1, ~id), strata = list(NULL, ~id1), fpc = list(~n1, NULL), data = mu284.1, subset = ~sub, method = "approx") Phase 1: 1 - level Cluster Sampling design With (5) clusters. svydesign(ids = ~id1, fpc = ~n1) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1 0.1 0.1 0.1 0.1 0.1 Population size (PSUs): 50 Phase 2: Stratified Independent Sampling design svydesign(ids = ~id, strata = ~id1, fpc = `*phase1*`) Probabilities: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3333 0.3750 0.4286 0.4674 0.6000 0.6000 Stratum Sizes: 19 31 45 47 50 obs 3 3 3 3 3 design.PSU 3 3 3 3 3 actual.PSU 3 3 3 3 3 Population stratum sizes (PSUs): 19 31 45 47 50 5 7 8 5 9 Data variables: [1] "id1" "n1" "id2" "y1" "n2" "id" "sub" > t1<-svytotal(~y1, dmu284) > t2<-svytotal(~y1, d2mu284) > t22<-svytotal(~y1,d22mu284) > m1<-svymean(~y1, dmu284) > m2<-svymean(~y1, d2mu284) > m22<-svymean(~y1, d22mu284) > all.equal(coef(t1),coef(t2)) [1] TRUE > all.equal(coef(t1),coef(t22)) [1] TRUE > all.equal(coef(m1),coef(m2)) [1] TRUE > all.equal(coef(m1),coef(m22)) [1] TRUE > all.equal(as.vector(SE(m1)),as.vector(SE(m2))) [1] TRUE > all.equal(as.vector(SE(m1)),as.vector(SE(m22))) [1] TRUE > all.equal(as.vector(SE(t1)),as.vector(SE(t2))) [1] TRUE > all.equal(as.vector(SE(t1)),as.vector(SE(t22))) [1] TRUE > > ## case-cohort design > ##this example requires R 2.3.1 or later for cch and data. > library("survival") > data(nwtco, package="survival") > ## unstratified, equivalent to Lin & Ying (1993) > print(dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), + subset=~I(in.subcohort | rel), data=nwtco)) Two-phase sparse-matrix design: twophase2(id = id, strata = strata, probs = probs, fpc = fpc, subset = subset, data = data, pps = pps) Phase 1: Independent Sampling design (with replacement) svydesign(ids = ~seqno) Phase 2: Stratified Independent Sampling design svydesign(ids = ~seqno, strata = ~rel, fpc = `*phase1*`) > cch1<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), + design=dcchs) > dcchs2<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), + subset=~I(in.subcohort | rel), data=nwtco,method="approx") > cch1.2<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), + design=dcchs) > all.equal(coef(cch1),coef(cch1.2)) [1] TRUE > all.equal(SE(cch1),SE(cch1.2)) [1] TRUE > ## Using survival::cch > subcoh <- nwtco$in.subcohort > selccoh <- with(nwtco, rel==1|subcoh==1) > ccoh.data <- nwtco[selccoh,] > ccoh.data$subcohort <- subcoh[selccoh] > cch2<-cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), + data =ccoh.data, subcoh = ~subcohort, id=~seqno, + cohort.size=4028, method="LinYing", robust=TRUE) > > print(all.equal(as.vector(coef(cch1)),as.vector(coef(cch2)))) [1] TRUE > ## cch has smaller variances by a factor of 1.0005 because > ## there is a (n/(n-1)) in the survey phase1 varianace > print(all.equal(as.vector(SE(cch1)), as.vector(SE(cch2)),tolerance=0.0006)) [1] TRUE > > > ## bug report from Takahiro Tsuchiya for version 3.4 > ## We used to not match Sarndal exactly, because our old phase-one > ## estimator had a small bias for finite populations > rei<-read.table(tmp<-textConnection( + " id N n.a h n.ah n.h sub y + 1 1 300 20 1 12 5 TRUE 1 + 2 2 300 20 1 12 5 TRUE 2 + 3 3 300 20 1 12 5 TRUE 3 + 4 4 300 20 1 12 5 TRUE 4 + 5 5 300 20 1 12 5 TRUE 5 + 6 6 300 20 1 12 5 FALSE NA + 7 7 300 20 1 12 5 FALSE NA + 8 8 300 20 1 12 5 FALSE NA + 9 9 300 20 1 12 5 FALSE NA + 10 10 300 20 1 12 5 FALSE NA + 11 11 300 20 1 12 5 FALSE NA + 12 12 300 20 1 12 5 FALSE NA + 13 13 300 20 2 8 3 TRUE 6 + 14 14 300 20 2 8 3 TRUE 7 + 15 15 300 20 2 8 3 TRUE 8 + 16 16 300 20 2 8 3 FALSE NA + 17 17 300 20 2 8 3 FALSE NA + 18 18 300 20 2 8 3 FALSE NA + 19 19 300 20 2 8 3 FALSE NA + 20 20 300 20 2 8 3 FALSE NA + "), header=TRUE) > close(tmp) > > des.rei <- twophase(id=list(~id,~id), strata=list(NULL,~h), + fpc=list(~N,NULL), subset=~sub, data=rei, method="approx") > tot<- svytotal(~y, des.rei) > des.rei2 <- twophase(id=list(~id,~id), strata=list(NULL,~h), + fpc=list(~N,NULL), subset=~sub, data=rei) > tot2<- svytotal(~y, des.rei2) > > ## based on Sarndal et al (9.4.14) > rei$w.ah <- rei$n.ah / rei$n.a > a.rei <- aggregate(rei, by=list(rei$h), mean, na.rm=TRUE) > a.rei$S.ysh <- tapply(rei$y, rei$h, var, na.rm=TRUE) > a.rei$y.u <- sum(a.rei$w.ah * a.rei$y) > V <- with(a.rei, sum(N * (N-1) * ((n.ah-1)/(n.a-1) - (n.h-1)/(N-1)) * w.ah * S.ysh / n.h)) > V <- V + with(a.rei, sum(N * (N-n.a) * w.ah * (y - y.u)^2 / (n.a-1))) > > a.rei$f.h<-with(a.rei, n.h/n.ah) > Vphase2<-with(a.rei, sum(N*N*w.ah^2* ((1-f.h)/n.h) *S.ysh)) > > a.rei$f<-with(a.rei, n.a/N) > a.rei$delta.h<-with(a.rei, (1/n.h)*(n.a-n.ah)/(n.a-1)) > Vphase1<-with(a.rei, sum(N*N*((1-f)/n.a)*( w.ah*(1-delta.h)*S.ysh+ ((n.a)/(n.a-1))*w.ah*(y-y.u)^2))) > > V [1] 36522.63 > Vphase1 [1] 24072.63 > Vphase2 [1] 12450 > vcov(tot) y y 35911.05 attr(,"phases") attr(,"phases")$phase1 [,1] [1,] 23461.05 attr(,"phases")$phase2 y y 12450 > vcov(tot2) [,1] [1,] 36522.63 attr(,"phases") attr(,"phases")$phase1 [,1] [1,] 24072.63 attr(,"phases")$phase2 [,1] [1,] 12450 > ## phase 2 identical > all.equal(Vphase2,drop(attr(vcov(tot),"phases")$phase2)) [1] TRUE > all.equal(Vphase2,drop(attr(vcov(tot2),"phases")$phase2)) [1] TRUE > ## phase 1 differs by 2.6% for old twophase estimator > Vphase1/attr(vcov(tot),"phases")$phase1 [,1] [1,] 1.026068 > all.equal(Vphase1,as.vector(attr(vcov(tot2),"phases")$phase1)) [1] TRUE > > > proc.time() user system elapsed 0.826 0.055 0.878 survey/tests/testSUMMER/0000755000176200001440000000000014571436153014672 5ustar liggesuserssurvey/tests/testSUMMER/SUMMER.R0000644000176200001440000000166214571436153016032 0ustar liggesusers## CRAN won't allow this test unless INLA is a listed dependency ## which is impossible, so the test is hidden away here. library(survey) data("DemoData2",package="SUMMER") data("DemoMap2", package="SUMMER") if(require("INLA",quietly=TRUE)){ INLA::inla.setOption(num.threads="1:1") library(survey) des0 <- svydesign(ids = ~clustid+id, strata = ~strata, weights = ~weights, data = DemoData2, nest = TRUE) Xmat <- aggregate(age~region, data = DemoData2, FUN = mean) cts.cov.res <- svysmoothArea(tobacco.use ~ age, domain = ~region, design = des0, adj.mat = DemoMap2$Amat, X.domain = Xmat, pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3) print(cts.cov.res) plot(cts.cov.res) summary(cts.cov.res) } survey/tests/raowuboot.R0000644000176200001440000000023313517345033015117 0ustar liggesusers## regression test for bug reported by Richard Valliant library(survey) s<-subbootweights(c(1,1),1:2, 50) stopifnot(all(s$repweights$weights %in% c(0,2))) survey/tests/glm-scoping.R0000644000176200001440000000132313517345033015316 0ustar liggesusers## bug report from Thomas Leeper, fixed in version 3.32-3 library("survey") data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) # pass `family` directly (WORKS!) svyglm(api00~ell+meals+mobility, design=dstrat, family = gaussian()) # passing `family` via ... (WORKS!) myfun1 <- function(formula, design, ...) { svyglm(formula, design = design, ...) } myfun1(api00~ell+meals+mobility, design=dstrat, family = gaussian()) # passing `family` via default argument (DOES NOT WORK!) myfun2 <- function(formula, design, family = gaussian()) { svyglm(formula, design = design, family = family) } myfun2(api00~ell+meals+mobility, design=dstrat, family = gaussian()) survey/tests/coxph-termtest.R0000644000176200001440000000216114141071074016061 0ustar liggesuserslibrary(survey) library(survival) set.seed(2021-6-25) test1 <- list(time=c(4,3,1,1,2,2,3), status=c(1,1,1,0,1,1,0), x1=as.factor(rbinom(7, 2, 0.5)), x=c(0,2,1,1,1,0,0)) # Fit a stratified model mod_c <- coxph(Surv(time, status) ~ x1 + x, test1) mod_d <- coxph(Surv(time, status) ~ x + x1, test1) stopifnot(all.equal(regTermTest(mod_c, ~x1, df = Inf)[c("chisq","df","test.terms","p")], regTermTest(mod_d, ~x1, df = Inf)[c("chisq","df","test.terms","p")])) data(pbc, package="survival") pbc$randomized<-with(pbc, !is.na(trt) & trt>0) biasmodel<-glm(randomized~age*edema,data=pbc,family=binomial) pbc$randprob<-fitted(biasmodel) if (is.null(pbc$albumin)) pbc$albumin<-pbc$alb ##pre2.9.0 dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized)) library(splines) model<-svycoxph(formula = Surv(time, status > 0) ~ bili + protime + albumin+ns(bili,4)[,1:3], design = dpbc) test<-regTermTest(model, ~ns(bili,4)[,1:3],method="LRT") stopifnot(all.equal(test$chisq, 47.314, tolerance=1e-4)) stopifnot(all.equal(test$lambda, c(1.4764260, 1.0109836, 0.6923415),tolerance=1e-4)) survey/tests/deff.R0000644000176200001440000000201713517345033014004 0ustar liggesusers## from Takahiro Tsuchiya library(survey) kigyo<-read.table(tmp<-textConnection(" obs uriage srs.w pps.w 1 1 15 100 20 2 2 143 100 200 3 3 21 100 11 4 4 51 100 25 5 5 337 100 550 6 6 50 100 30 7 7 274 100 250 8 8 145 100 100 9 9 15 100 10 10 10 86 100 55 ",open="r"),header=TRUE) close(tmp) des.srs <- svydesign(ids=~1, weights=~srs.w, data=kigyo) (res.srs <- svymean(~uriage, des.srs, deff=TRUE)) (SE(res.srs)^2) / ((1-10/1000) * coef(svyvar(~uriage, des.srs)) / 10) (tres.srs <- svytotal(~uriage, des.srs, deff=TRUE)) (SE(tres.srs)^2) / (1000^2 * (1-10/1000) * coef(svyvar(~uriage, des.srs)) / 10) des.pps <- svydesign(ids=~1, weights=~pps.w, data=kigyo) (res.pps <- svymean(~uriage, des.pps, deff='replace')) (SE(res.pps)^2) / (coef(svyvar(~uriage, des.pps)) / 10) (tres.pps <- svytotal(~uriage, des.pps, deff='replace')) (N.hat <- sum(weights(des.pps))) (SE(tres.pps)^2) / (N.hat^2 * coef(svyvar(~uriage, des.pps)) / 10) survey/tests/pps.R0000644000176200001440000000353213517345033013705 0ustar liggesuserslibrary(survey) data(election) dpps<- svydesign(id=~1, weights=~wt, fpc=~p, data=election_pps, pps="brewer") dppswr <-svydesign(id=~1, weights=~wt, data=election_pps) svytotal(~Bush+Kerry+Nader, dpps) svytotal(~Bush+Kerry+Nader, dppswr) ##subsets svytotal(~Bush+Kerry+Nader, subset(dpps, Nader>0)) ##multistage: should agree with STRS analysis data(api) dclus2<-svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2) dclus2pps<-svydesign(id = ~dnum + snum, fpc = ~I(40/fpc1) + I(pmin(1,5/fpc2)), data = apiclus2) all.equal(svytotal(~sch.wide,dclus2), svytotal(~sch.wide,dclus2pps)) all.equal(svymean(~sch.wide,dclus2), svymean(~sch.wide,dclus2pps)) all.equal(svytotal(~enroll,dclus2), svytotal(~enroll,dclus2pps)) ## the new without-replacement methods data(election) dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) ## Yates-Grundy type dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") ## The with-replacement approximation svytotal(~Bush+Kerry+Nader, dpps_ht) svytotal(~Bush+Kerry+Nader, dpps_yg) svytotal(~Bush+Kerry+Nader, dpps_hr) svytotal(~Bush+Kerry+Nader, dpps_hryg) svytotal(~Bush+Kerry+Nader, dpps_hr1) svytotal(~Bush+Kerry+Nader, dpps_br) svytotal(~Bush+Kerry+Nader, dpps_ov) ## subsets svytotal(~Bush+Kerry+Nader, subset(dpps_ht, Nader>0)) svytotal(~Bush+Kerry+Nader, subset(dpps_hryg, Nader>0)) ## counts svyby(~Bush+Kerry+Nader,~I(Nader>0), unwtd.count,design=dpps_ht) survey/tests/badcal.Rout.save0000644000176200001440000000235613517345033016001 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## > ## Calibration with badly-scaled initial weights (bug report by Takahiro Tsuchiya) > ## > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data <- data.frame(x=c(1,1,1,1,2,2,2,2,2,2), w=rep(10,10)) > des <- svydesign(ids=~1, weights=~w, data=data) > des.c <- calibrate(des, ~factor(x), c(10000, 5000)) > des.r <- calibrate(des, ~factor(x), c(10000, 5000), calfun='raking') Loading required package: MASS > stopifnot(all.equal(svytotal(~factor(x), des.c), svytotal(~factor(x), des.r))) > > proc.time() user system elapsed 0.162 0.025 0.194 survey/tests/check.Rout.save0000755000176200001440000001301513517345033015645 0ustar liggesusers R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(fpc) > ## test various possibilities for svydesign > a<-svydesign(weights=~weight, ids=~psuid, strata=~stratid, variables=~x, data=fpc, nest=TRUE) > a Stratified Independent Sampling design (with replacement) svydesign(weights = ~weight, ids = ~psuid, strata = ~stratid, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.4481 0.7413 > a<-svydesign(weights=~weight, ids=~0, strata=~stratid, variables=~x, data=fpc, nest=TRUE) > a Stratified Independent Sampling design (with replacement) svydesign(weights = ~weight, ids = ~0, strata = ~stratid, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.4481 0.7413 > a<-svydesign(weights=1, ids=~0, strata=~stratid, variables=~x, data=fpc, nest=TRUE) > a Stratified Independent Sampling design (with replacement) svydesign(weights = 1, ids = ~0, strata = ~stratid, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 44.2 6.237 > a<-svydesign(ids=~0, strata=~stratid, variables=~x, data=fpc, nest=TRUE) Warning message: In svydesign.default(ids = ~0, strata = ~stratid, variables = ~x, : No weights or probabilities supplied, assuming equal probability > a Stratified Independent Sampling design (with replacement) svydesign(ids = ~0, strata = ~stratid, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.525 0.7796 > a<-svydesign(ids=~0, strata=~stratid, prob=~I(1/weight),variables=~x, data=fpc, nest=TRUE) > a Stratified Independent Sampling design (with replacement) svydesign(ids = ~0, strata = ~stratid, prob = ~I(1/weight), variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.4481 0.7413 > a<-svydesign(ids=~psuid, strata=~stratid, variables=~x, data=fpc, nest=TRUE) Warning message: In svydesign.default(ids = ~psuid, strata = ~stratid, variables = ~x, : No weights or probabilities supplied, assuming equal probability > a Stratified Independent Sampling design (with replacement) svydesign(ids = ~psuid, strata = ~stratid, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.525 0.7796 > a<-svydesign(ids=~psuid, variables=~x, data=fpc, nest=TRUE) Warning message: In svydesign.default(ids = ~psuid, variables = ~x, data = fpc, nest = TRUE) : No weights or probabilities supplied, assuming equal probability > a 1 - level Cluster Sampling design (with replacement) With (5) clusters. svydesign(ids = ~psuid, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.525 0.838 > a<-svydesign(ids=~psuid, weights=~weight, variables=~x, data=fpc, nest=TRUE) > a 1 - level Cluster Sampling design (with replacement) With (5) clusters. svydesign(ids = ~psuid, weights = ~weight, variables = ~x, data = fpc, nest = TRUE) > svymean(~x,a) mean SE x 5.4481 0.7938 > a<-svydesign(ids=~stratid+psuid, weights=~weight, variables=~x, data=fpc) > a 2 - level Cluster Sampling design (with replacement) With (2, 8) clusters. svydesign(ids = ~stratid + psuid, weights = ~weight, variables = ~x, data = fpc) > svymean(~x,a) mean SE x 5.4481 0.5465 > a<-svydesign(ids=~stratid+psuid, variables=~x, data=fpc) Warning message: In svydesign.default(ids = ~stratid + psuid, variables = ~x, data = fpc) : No weights or probabilities supplied, assuming equal probability > a 2 - level Cluster Sampling design (with replacement) With (2, 8) clusters. svydesign(ids = ~stratid + psuid, variables = ~x, data = fpc) > svymean(~x,a) mean SE x 5.525 0.5188 > a<-svydesign(weights=fpc$weight, ids=fpc$psuid, strata=fpc$stratid, variables=fpc[,"x",drop=FALSE], nest=TRUE) > a Stratified Independent Sampling design (with replacement) svydesign(weights = fpc$weight, ids = fpc$psuid, strata = fpc$stratid, variables = fpc[, "x", drop = FALSE], nest = TRUE) > svymean(~x,a) mean SE x 5.4481 0.7413 > a<-svydesign(weights=fpc$weight, ids=fpc$psuid, strata=fpc$stratid, variables=fpc[,4:6], nest=TRUE) > a Stratified Independent Sampling design (with replacement) svydesign(weights = fpc$weight, ids = fpc$psuid, strata = fpc$stratid, variables = fpc[, 4:6], nest = TRUE) > svymean(~x,a) mean SE x 5.4481 0.7413 > > a<-svydesign(weights=fpc$weight, ids=fpc$psuid, variables=fpc[,4:6], fpc=rep(27,8)) > a 1 - level Cluster Sampling design With (5) clusters. svydesign(weights = fpc$weight, ids = fpc$psuid, variables = fpc[, 4:6], fpc = rep(27, 8)) > svymean(~x,a) mean SE x 5.4481 0.7165 > > a<-svydesign(weights=fpc$weight, ids=fpc$psuid, strata=fpc$stratid, nest=TRUE, variables=fpc[,4:6], fpc=fpc$Nh) > a Stratified Independent Sampling design svydesign(weights = fpc$weight, ids = fpc$psuid, strata = fpc$stratid, nest = TRUE, variables = fpc[, 4:6], fpc = fpc$Nh) > svymean(~x,a) mean SE x 5.4481 0.616 > > proc.time() user system elapsed 0.201 0.024 0.232 survey/tests/nwts.Rout.save0000644000176200001440000001632114567002133015560 0ustar liggesusers R Under development (unstable) (2024-01-29 r85841) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > ## examples from Breslow & Chatterjee: Applied Statistics 1999 No. 4, p458 > ## data from Norman Breslow's web page. > library(survey) Loading required package: grid Loading required package: Matrix Loading required package: survival Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > load("nwts.rda") > nwtsnb<-nwts > nwtsnb$case<-nwts$case-nwtsb$case > nwtsnb$control<-nwts$control-nwtsb$control > > a<-rbind(nwtsb,nwtsnb) > a$in.ccs<-rep(c(TRUE,FALSE),each=16) > > b<-rbind(a,a) > b$rel<-rep(c(1,0),each=32) > b$n<-ifelse(b$rel,b$case,b$control) > > index<-rep(1:64,b$n) > > nwt.exp<-b[index,c(1:3,6,7)] > nwt.exp$id<-1:4088 > > dccs2<-twophase(id=list(~id,~id),subset=~in.ccs, + strata=list(NULL,~interaction(instit,rel)),data=nwt.exp) > > dccs8<-twophase(id=list(~id,~id),subset=~in.ccs, + strata=list(NULL,~interaction(instit,stage,rel)),data=nwt.exp) > > gccs8<-calibrate(dccs2,phase=2,formula=~interaction(instit,stage,rel)) > > summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs2)) Call: svyglm(formula = rel ~ factor(stage) * factor(histol), design = dccs2, family = quasibinomial) Survey design: twophase2(id = id, strata = strata, probs = probs, fpc = fpc, subset = subset, data = data, pps = pps) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.5701 0.1288 -19.955 < 2e-16 *** factor(stage)2 0.5482 0.1979 2.769 0.005708 ** factor(stage)3 0.4791 0.2032 2.359 0.018515 * factor(stage)4 1.0037 0.2592 3.872 0.000114 *** factor(histol)2 1.3505 0.3107 4.346 1.51e-05 *** factor(stage)2:factor(histol)2 0.1152 0.4410 0.261 0.793876 factor(stage)3:factor(histol)2 0.5066 0.4241 1.194 0.232548 factor(stage)4:factor(histol)2 0.9785 0.6214 1.575 0.115615 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 1.000876) Number of Fisher Scoring iterations: 4 > summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs8)) Call: svyglm(formula = rel ~ factor(stage) * factor(histol), design = dccs8, family = quasibinomial) Survey design: twophase2(id = id, strata = strata, probs = probs, fpc = fpc, subset = subset, data = data, pps = pps) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.71604 0.10827 -25.085 < 2e-16 *** factor(stage)2 0.78141 0.14726 5.306 1.35e-07 *** factor(stage)3 0.80093 0.15250 5.252 1.80e-07 *** factor(stage)4 1.07293 0.17817 6.022 2.33e-09 *** factor(histol)2 1.45836 0.31780 4.589 4.96e-06 *** factor(stage)2:factor(histol)2 -0.04743 0.43495 -0.109 0.913 factor(stage)3:factor(histol)2 0.28064 0.41298 0.680 0.497 factor(stage)4:factor(histol)2 0.90983 0.63774 1.427 0.154 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 1.000876) Number of Fisher Scoring iterations: 4 > summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=gccs8)) Call: svyglm(formula = rel ~ factor(stage) * factor(histol), design = gccs8, family = quasibinomial) Survey design: calibrate(dccs2, phase = 2, formula = ~interaction(instit, stage, rel)) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.71604 0.10878 -24.968 < 2e-16 *** factor(stage)2 0.78141 0.14729 5.305 1.35e-07 *** factor(stage)3 0.80093 0.15212 5.265 1.68e-07 *** factor(stage)4 1.07293 0.17905 5.993 2.77e-09 *** factor(histol)2 1.45836 0.31757 4.592 4.88e-06 *** factor(stage)2:factor(histol)2 -0.04743 0.43432 -0.109 0.913 factor(stage)3:factor(histol)2 0.28064 0.41231 0.681 0.496 factor(stage)4:factor(histol)2 0.90983 0.63187 1.440 0.150 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 1.000876) Number of Fisher Scoring iterations: 4 > > ## check subsets of calibrated designs. > summary(svyglm(rel~factor(stage), + family=quasibinomial,design=subset(dccs8,histol==1))) Call: svyglm(formula = rel ~ factor(stage), design = subset(dccs8, histol == 1), family = quasibinomial) Survey design: subset(dccs8, histol == 1) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.7160 0.1083 -25.085 < 2e-16 *** factor(stage)2 0.7814 0.1473 5.306 1.48e-07 *** factor(stage)3 0.8009 0.1525 5.252 1.97e-07 *** factor(stage)4 1.0729 0.1782 6.022 2.73e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 1.001333) Number of Fisher Scoring iterations: 4 Warning messages: 1: In `[.twophase2`(x, r, ) : 1 strata have only one PSU in this subset. 2: In summary.glm(g) : observations with zero weight not used for calculating dispersion 3: In summary.glm(glm.object) : observations with zero weight not used for calculating dispersion 4: In `[.twophase2`(design, nas == 0, ) : 1 strata have only one PSU in this subset. 5: In `[.twophase2`(design, nas == 0, ) : 1 strata have only one PSU in this subset. > summary(svyglm(rel~factor(stage), + family=quasibinomial,design=subset(gccs8,histol==1))) Call: svyglm(formula = rel ~ factor(stage), design = subset(gccs8, histol == 1), family = quasibinomial) Survey design: subset(gccs8, histol == 1) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.7160 0.1082 -25.105 < 2e-16 *** factor(stage)2 0.7814 0.1457 5.363 1.10e-07 *** factor(stage)3 0.8009 0.1504 5.324 1.34e-07 *** factor(stage)4 1.0729 0.1759 6.101 1.70e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 1.001333) Number of Fisher Scoring iterations: 4 Warning messages: 1: In summary.glm(g) : observations with zero weight not used for calculating dispersion 2: In summary.glm(glm.object) : observations with zero weight not used for calculating dispersion > > > proc.time() user system elapsed 0.872 0.065 0.934 survey/tests/qrule-swiss.R0000644000176200001440000000100014526736255015400 0ustar liggesuserslibrary(survey) data_no_strat <- data.frame(x = 1:10, id = 1:10, fpc = 10, probs = 1) design_no_strat <- svydesign(id = ~id, probs = ~probs, data = data_no_strat, fpc = ~fpc) quantiles <- c(0.01, 0.05, 0.1, 0.15, seq(21, 81, 10)*0.01, 0.85, 0.9, 0.95, 0.99) res <- svyquantile(~x, design_no_strat, quantiles, ci = TRUE, interval.type = "mean", qrule = "hf1") stopifnot(all(diff(res$x[,"quantile"])>=0)) survey/tests/quantiles-chile.R0000644000176200001440000000170513706504553016176 0ustar liggesuserslibrary(survey) datos <- readRDS("datos_ejemplo.rds") design <- svydesign(id = ~id_directorio, strata = ~estrato, weights = ~f_pers, check.strata = TRUE, data = datos) set.seed(234262762) repdesign <- as.svrepdesign(design, type = "subbootstrap", replicates=20) options(survey.lonely.psu="remove") values<-datos$ing_t_p[datos$CL_GRUPO_OCU_08=="ISCO08_6"] suppressWarnings({ f0<-coef(svyquantile(~ing_t_p, subset(design,CL_GRUPO_OCU_08=="ISCO08_6"),quantiles=c(0.5), qrule="math")) f0.5<-coef(svyquantile(~ing_t_p, subset(design,CL_GRUPO_OCU_08=="ISCO08_6"),quantiles=c(0.5), qrule="school")) }) all.equal(c(values[1],mean(values)), as.vector(c(f0,f0.5))) suppressWarnings({ f0<-coef(svyquantile(~ing_t_p, subset(repdesign,CL_GRUPO_OCU_08=="ISCO08_6"),quantiles=c(0.5), qrule="math")) f0.5<-coef(svyquantile(~ing_t_p, subset(repdesign,CL_GRUPO_OCU_08=="ISCO08_6"),quantiles=c(0.5), qrule="school")) }) all.equal(c(values[1],mean(values)), as.vector(c(f0,f0.5))) survey/tests/api.R0000644000176200001440000000016413517345033013652 0ustar liggesuserslibrary(survey) options(survey.replicates.mse=TRUE) example(api) options(survey.replicates.mse=FALSE) example(api) survey/tests/confintrep.R0000644000176200001440000000067214464617427015267 0ustar liggesuserslibrary(survey) data(api) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) rclus2<-as.svrepdesign(dclus2) m<-svyglm(I(comp.imp=="Yes")~1, design=dclus2, family=quasibinomial) if(anyNA(confint(m, method="likelihood"))) stop("NA in confint") mrep<-svyglm(I(comp.imp=="Yes")~1, design=rclus2, family=quasibinomial) if(anyNA(confint(mrep))) stop("NA in confint") if(anyNA(confint(mrep, method="likelihood"))) stop("NA in confint")survey/src/0000755000176200001440000000000014576431435012412 5ustar liggesuserssurvey/src/arma_multistage.cpp0000644000176200001440000003325314576422540016277 0ustar liggesusers//#include #include // [[Rcpp::depends(RcppArmadillo)]] // [[Rcpp::export(rng=FALSE)]] arma::mat arma_onestage(const arma::mat& Y, const arma::colvec& samp_unit_ids, const arma::colvec& strata_ids, const arma::colvec& strata_samp_sizes, const arma::colvec& strata_pop_sizes, const Rcpp::CharacterVector& singleton_method, const Rcpp::LogicalVector& use_singleton_method_for_domains, const int& stage) { size_t n_col_y = Y.n_cols; arma::uword number_of_data_rows = Y.n_rows; arma::mat result(n_col_y, n_col_y, arma::fill::zeros); if (number_of_data_rows == 0) { return result; } // Get distinct strata ids and their length, H arma::colvec distinct_strata_ids = unique(strata_ids); arma::uword H = distinct_strata_ids.n_elem; // Initialize vectors giving start and end rows for each stratum arma::uvec strata_ends; arma::uvec strata_starts; strata_ends.set_size(H); strata_starts.set_size(H); // Initialize checks for end of stratum bool at_end_of_stratum = true; // Prepare to iterate over list of strata arma::uword h = 0; // Get end index of each stratum arma::uvec row_indices = arma::linspace(0L, number_of_data_rows - 1L, number_of_data_rows); arma::uword next_row_index; for (arma::uvec::iterator row_index = row_indices.begin(); row_index != row_indices.end(); ++row_index) { if ((*row_index) == (number_of_data_rows-1)) { at_end_of_stratum = true; } else { next_row_index = * std::next(row_index, 1); at_end_of_stratum = strata_ids(*row_index) != strata_ids(next_row_index); } if (at_end_of_stratum) { strata_ends(h) = *(row_index); h += 1L; } } // Get start index of each stratum if (H > 1) { h = 1; while (h < H) { strata_starts(h) = strata_ends((h-1)) + 1; h += 1; } } strata_starts(0) = 0; // Initialize strata summaries arma::mat cov_h(n_col_y, n_col_y, arma::fill::zeros); double n_h; double N_h; int n_obs_h; double f_h; double scale; // Initialize checks for end of sampling unit bool at_end_of_samp_unit = true; // Initialize sampling unit total arma::rowvec Yhi; Yhi = Yhi.zeros(n_col_y); // Get count of sampling units from all strata, // and check for singleton strata double n_samp_units_all_strata = 0; arma::rowvec singleton_indicators; singleton_indicators.zeros(H); for (arma::uword h = 0; h < H; ++h) { arma::uword stratum_start = strata_starts(h); n_samp_units_all_strata += strata_samp_sizes(stratum_start); // Check if there is only one sampling unit sampled in the stratum if (strata_samp_sizes(stratum_start) < 2) { // If only one sampling unit, check that it wasn't sampled with certainty if ((strata_pop_sizes(stratum_start) > 1)) { singleton_indicators(h) = 1; if ((singleton_method[0] == "fail")) { Rcpp::String error_msg("At least one stratum contains only one PSU at stage "); error_msg += stage; Rcpp::stop(error_msg); } } continue; // Skip to next stratum } // Check for PSUs which are lonely in the domain (but not in the sample) if (use_singleton_method_for_domains[0]) { arma::uword h_row_index = strata_starts(h); // Set singleton indicator equal to 1 unless there are multiple observed sampling units in the stratum singleton_indicators(h) = 1; while ((singleton_indicators(h) == 1) && (h_row_index <= strata_ends(h))) { if (samp_unit_ids(h_row_index) != samp_unit_ids(strata_starts(h))) { singleton_indicators(h) = 0; } h_row_index += 1; } if ((static_cast(strata_ends[h] - strata_starts[h] + 1)) < 2) { singleton_indicators(h) = 1; } // Throw a warning message about the domain's singleton if (singleton_indicators(h) == 1) { Rcpp::String warning_msg("At least one stratum contains only one PSU at stage "); warning_msg += stage; Rcpp::warning(warning_msg); // If not adjusting for domain singletons, don't mark the stratum as a singleton if ((singleton_method[0] != "adjust") && (singleton_method[0] != "average")) { singleton_indicators(h) = 0; } } } } int n_singleton_strata = sum(singleton_indicators); bool any_singleton_strata = n_singleton_strata > 0; arma::rowvec Y_means; Y_means = Y_means.zeros(n_col_y); if (any_singleton_strata & (singleton_method[0] == "adjust")) { Y_means = sum(Y, 0) / n_samp_units_all_strata; } // Iterate over each stratum for (arma::uword h = 0; h < H; ++h) { if ((singleton_indicators(h) == 1) && (singleton_method[0] != "adjust")) { continue; } arma::uvec h_row_indices = arma::linspace(strata_starts[h], strata_ends[h], strata_ends[h] - strata_starts[h] + 1); // Get stratum-wide summary arma::uword stratum_start = strata_starts(h); n_h = static_cast(strata_samp_sizes(stratum_start)); N_h = static_cast(strata_pop_sizes(stratum_start)); n_obs_h = 0; if (arma::is_finite(N_h)) { f_h = static_cast(n_h) / static_cast(N_h); } else { f_h = 0.0; } arma::rowvec Ybar_h; if ((singleton_indicators(h) == 1) && (singleton_method[0] == "adjust")) { Ybar_h = Y_means; } else { Ybar_h = arma::sum(Y.rows(h_row_indices), 0) / n_h; } // Iterate over each row in the stratum and its following row arma::uword h_next_row_index; for (arma::uvec::iterator h_row_index = h_row_indices.begin(); h_row_index != h_row_indices.end(); ++h_row_index) { // Determine whether current row is last for the current sampling unit if ((*h_row_index) < strata_ends(h)) { h_next_row_index = * std::next(h_row_index, 1); at_end_of_samp_unit = samp_unit_ids(*h_row_index) != samp_unit_ids(h_next_row_index); } else { at_end_of_samp_unit = true; } // Get contribution to sampling unit\'s total Yhi += Y.row(*h_row_index); if (at_end_of_samp_unit) { n_obs_h += 1; Yhi.each_row() -= Ybar_h; cov_h += (arma::trans(Yhi)*Yhi); Yhi = Yhi.zeros(); } } // If the data were subsetted, some sampling units // may not have rows of data that appear in inputs. // Make sure these units contribute to the variance. double n_h_missing = n_h - n_obs_h; if (n_h_missing > 0) { cov_h += n_h_missing*(arma::trans(Ybar_h)*Ybar_h); } // Determine scaling factor to use for normalizing sum of squares // and handling finite population correction scale = ((1.0 - f_h) * n_h); if (n_h > 1) { scale /= (n_h - 1); } result += (scale * cov_h); cov_h = cov_h.zeros(); } // For the 'average' method for handling singleton strata, // the total variance contribution of each singleton stratum // is assumed to be equal to the variance contribution of the average nonsingleton stratum if ((singleton_method[0] == "average") && any_singleton_strata) { int n_nonsingleton_strata = H - n_singleton_strata; double scaling_factor; if (n_nonsingleton_strata > 0) { scaling_factor = static_cast(H)/static_cast(n_nonsingleton_strata); } else { scaling_factor = R_NaN; } result *= scaling_factor; } return result; } // [[Rcpp::export(rng=FALSE)]] arma::mat arma_multistage(arma::mat Y, arma::mat samp_unit_ids, arma::mat strata_ids, arma::mat strata_samp_sizes, arma::mat strata_pop_sizes, Rcpp::CharacterVector singleton_method, Rcpp::LogicalVector use_singleton_method_for_domains, Rcpp::LogicalVector use_only_first_stage, int stage) { size_t n_stages = samp_unit_ids.n_cols; if (Y.n_rows == 0) { arma::mat V(Y.n_cols, Y.n_cols, arma::fill::zeros); return V; } // First reorder inputs by first-stage sample unit IDs arma::uvec samp_unit_id_order = arma::stable_sort_index(samp_unit_ids.col(0), "ascend"); Y = Y.rows(samp_unit_id_order); samp_unit_ids = samp_unit_ids.rows(samp_unit_id_order); strata_ids = strata_ids.rows(samp_unit_id_order); strata_samp_sizes = strata_samp_sizes.rows(samp_unit_id_order); strata_pop_sizes = strata_pop_sizes.rows(samp_unit_id_order); // Next reorder inputs by first-stage strata IDs arma::uvec strata_id_order = arma::stable_sort_index(strata_ids.col(0), "ascend"); Y = Y.rows(strata_id_order); samp_unit_ids = samp_unit_ids.rows(strata_id_order); strata_ids = strata_ids.rows(strata_id_order); strata_samp_sizes = strata_samp_sizes.rows(strata_id_order); strata_pop_sizes = strata_pop_sizes.rows(strata_id_order); // Obtain first stage information arma::colvec first_stage_ids = samp_unit_ids.col(0); arma::colvec first_stage_strata = strata_ids.col(0); arma::colvec first_stage_strata_samp_sizes = strata_samp_sizes.col(0); arma::colvec first_stage_strata_pop_sizes = strata_pop_sizes.col(0); // If there are later stages of sampling, // obtain the necessary columns from inputs, // which will be used recursively arma::mat later_stage_ids; arma::mat later_stage_strata; arma::mat later_stage_strata_samp_sizes; arma::mat later_stage_strata_pop_sizes; if ((n_stages > 1) && !use_only_first_stage[0]) { later_stage_ids = samp_unit_ids.tail_cols(n_stages - 1); later_stage_strata = strata_ids.tail_cols(n_stages - 1); later_stage_strata_samp_sizes = strata_samp_sizes.tail_cols(n_stages-1); later_stage_strata_pop_sizes = strata_pop_sizes.tail_cols(n_stages-1); } // Calculate first-stage variance arma::mat V = arma_onestage(Y, first_stage_ids, first_stage_strata, first_stage_strata_samp_sizes, first_stage_strata_pop_sizes, singleton_method, use_singleton_method_for_domains, stage); // For each first-stage unit, get variance contribution from next stage if ((n_stages > 1) && !use_only_first_stage[0]) { // Get distinct first-stage strata ids and their length, H arma::colvec distinct_strata_ids = unique(first_stage_strata); arma::uword H = distinct_strata_ids.n_elem; for (arma::uword h = 0; h < H; ++h) { // Determine which rows of data correspond to the current first-stage stratum arma::uvec h_indices = arma::find(first_stage_strata==distinct_strata_ids(h)); // Get submatrices of inputs corresponding to the current first-stage stratum arma::mat Y_h = Y.rows(h_indices); arma::mat h_samp_unit_ids = later_stage_ids.rows(h_indices); arma::mat h_strata = later_stage_strata.rows(h_indices); arma::mat h_strata_samp_sizes = later_stage_strata_samp_sizes.rows(h_indices); arma::mat h_strata_pop_sizes = later_stage_strata_pop_sizes.rows(h_indices); // Get count of first-stage sampling units in first-stage stratum // and finite population correction, based on all first-stage units in sample design arma::uword n_h = min(first_stage_strata_samp_sizes.elem(h_indices)); double N_h = static_cast(min(strata_pop_sizes.elem(h_indices))); double f_h; if (arma::is_finite(N_h)) { f_h = static_cast(n_h) / N_h; } else { f_h = 0.0; continue; // Skip to next stratum, since variance contribution is 0 } // Get list of first-stage units in the current subset of data, and count them arma::colvec h_first_stage_units = first_stage_ids.elem(h_indices); arma::colvec h_unique_first_stage_units = unique(h_first_stage_units); arma::uword n_h_subset = h_unique_first_stage_units.n_elem; for (arma::uword i=0; i < n_h_subset; ++i ) { // Create subsets of inputs specific to current first-stage sampling unit arma::uvec unit_indices = arma::find(first_stage_ids.elem(h_indices) == h_unique_first_stage_units(i)); arma::mat Y_hi = Y_h.rows(unit_indices); arma::mat hi_samp_unit_ids = h_samp_unit_ids.rows(unit_indices); arma::mat hi_strata = h_strata.rows(unit_indices); arma::mat hi_strata_samp_sizes = h_strata_samp_sizes.rows(unit_indices); arma::mat hi_strata_pop_sizes = h_strata_pop_sizes.rows(unit_indices); // Estimate later-stage variance contribution arma::mat V_hi = f_h * arma_multistage(Y_hi, hi_samp_unit_ids, hi_strata, hi_strata_samp_sizes, hi_strata_pop_sizes, singleton_method, use_singleton_method_for_domains, use_only_first_stage, stage = stage + 1); V += V_hi; } } } // Deal with potential floating-point error V.diag() = arma::abs(V.diag()); for (arma::uword i=0; i < V.n_cols; ++i ) { if (std::abs(V(i,i)) < 1e-16) { V.row(i).zeros(); V.col(i).zeros(); } } // Return result return V; } survey/src/Makevars.makefile0000644000176200001440000000006014462643117015652 0ustar liggesusers PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)survey/src/Makevars.win0000644000176200001440000000006114462643117014673 0ustar liggesusers PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) survey/src/RcppExports.cpp0000644000176200001440000000770514462643117015414 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include #include using namespace Rcpp; #ifdef RCPP_USE_GLOBAL_ROSTREAM Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); #endif // arma_onestage arma::mat arma_onestage(const arma::mat& Y, const arma::colvec& samp_unit_ids, const arma::colvec& strata_ids, const arma::colvec& strata_samp_sizes, const arma::colvec& strata_pop_sizes, const Rcpp::CharacterVector& singleton_method, const Rcpp::LogicalVector& use_singleton_method_for_domains, const int& stage); RcppExport SEXP _survey_arma_onestage(SEXP YSEXP, SEXP samp_unit_idsSEXP, SEXP strata_idsSEXP, SEXP strata_samp_sizesSEXP, SEXP strata_pop_sizesSEXP, SEXP singleton_methodSEXP, SEXP use_singleton_method_for_domainsSEXP, SEXP stageSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::traits::input_parameter< const arma::mat& >::type Y(YSEXP); Rcpp::traits::input_parameter< const arma::colvec& >::type samp_unit_ids(samp_unit_idsSEXP); Rcpp::traits::input_parameter< const arma::colvec& >::type strata_ids(strata_idsSEXP); Rcpp::traits::input_parameter< const arma::colvec& >::type strata_samp_sizes(strata_samp_sizesSEXP); Rcpp::traits::input_parameter< const arma::colvec& >::type strata_pop_sizes(strata_pop_sizesSEXP); Rcpp::traits::input_parameter< const Rcpp::CharacterVector& >::type singleton_method(singleton_methodSEXP); Rcpp::traits::input_parameter< const Rcpp::LogicalVector& >::type use_singleton_method_for_domains(use_singleton_method_for_domainsSEXP); Rcpp::traits::input_parameter< const int& >::type stage(stageSEXP); rcpp_result_gen = Rcpp::wrap(arma_onestage(Y, samp_unit_ids, strata_ids, strata_samp_sizes, strata_pop_sizes, singleton_method, use_singleton_method_for_domains, stage)); return rcpp_result_gen; END_RCPP } // arma_multistage arma::mat arma_multistage(arma::mat Y, arma::mat samp_unit_ids, arma::mat strata_ids, arma::mat strata_samp_sizes, arma::mat strata_pop_sizes, Rcpp::CharacterVector singleton_method, Rcpp::LogicalVector use_singleton_method_for_domains, Rcpp::LogicalVector use_only_first_stage, int stage); RcppExport SEXP _survey_arma_multistage(SEXP YSEXP, SEXP samp_unit_idsSEXP, SEXP strata_idsSEXP, SEXP strata_samp_sizesSEXP, SEXP strata_pop_sizesSEXP, SEXP singleton_methodSEXP, SEXP use_singleton_method_for_domainsSEXP, SEXP use_only_first_stageSEXP, SEXP stageSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::traits::input_parameter< arma::mat >::type Y(YSEXP); Rcpp::traits::input_parameter< arma::mat >::type samp_unit_ids(samp_unit_idsSEXP); Rcpp::traits::input_parameter< arma::mat >::type strata_ids(strata_idsSEXP); Rcpp::traits::input_parameter< arma::mat >::type strata_samp_sizes(strata_samp_sizesSEXP); Rcpp::traits::input_parameter< arma::mat >::type strata_pop_sizes(strata_pop_sizesSEXP); Rcpp::traits::input_parameter< Rcpp::CharacterVector >::type singleton_method(singleton_methodSEXP); Rcpp::traits::input_parameter< Rcpp::LogicalVector >::type use_singleton_method_for_domains(use_singleton_method_for_domainsSEXP); Rcpp::traits::input_parameter< Rcpp::LogicalVector >::type use_only_first_stage(use_only_first_stageSEXP); Rcpp::traits::input_parameter< int >::type stage(stageSEXP); rcpp_result_gen = Rcpp::wrap(arma_multistage(Y, samp_unit_ids, strata_ids, strata_samp_sizes, strata_pop_sizes, singleton_method, use_singleton_method_for_domains, use_only_first_stage, stage)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_survey_arma_onestage", (DL_FUNC) &_survey_arma_onestage, 8}, {"_survey_arma_multistage", (DL_FUNC) &_survey_arma_multistage, 9}, {NULL, NULL, 0} }; RcppExport void R_init_survey(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } survey/vignettes/0000755000176200001440000000000014576431435013633 5ustar liggesuserssurvey/vignettes/pps.Rnw0000644000176200001440000001455113517345006015123 0ustar liggesusers\documentclass{article} \usepackage{url} \addtolength{\topmargin}{-0.5in} \addtolength{\textheight}{0.75in} \addtolength{\oddsidemargin}{-0.5in} \addtolength{\textwidth}{1in} %\VignetteIndexEntry{Analysing PPS designs} \usepackage{Sweave} \author{Thomas Lumley} \title{Describing PPS designs to R} \begin{document} \maketitle The survey package has always supported PPS (ie, arbitrary unequal probability) sampling with replacement, or using the with-replacement single-stage approximation to a multistage design. No special notation is required: just specify the correct sampling weights. Version 3.11 added an another approximation for PPS sampling without replacement, and version 3.16 added more support. There are two broad classes of estimators for PPS sampling without replacement: approximations to the Horvitz--Thompson and Yates--Grundy estimators based on approximating the pairwise sampling probabilities, and estimators of H\'ajek type that attempt to recover the extra precision of a without-replacement design by conditioning on the estimated population size. \subsection*{Direct approximations} Using the standard recursive algorithm for stratified multistage sampling when one or more stages are actually PPS gives an approximation due to Brewer. This is simple to compute, always non-negative, and appears to be fairly efficient. \subsection*{Approximating $\pi_{ij}$} Given the pairwise sampling probabilities $\pi_{ij}$ we can define the weighted covariance of sampling indicators $$\check{\Delta}_{ij} = 1-\frac{\pi_i\pi_j}{\pi_{ij}}$$ and the weighted observations $$\check{x}_i=\frac{1}{\pi_i}x_i.$$ Two unbiased estimators of the variance of the total of $x$ are the Horvitz--Thompson estimator $$\hat V_{HT}= \sum_{i,j=1}^n \check{\Delta}\check{x}_i\check{x}_j$$ and the Yates--Grundy(--Sen) estimator $$\hat V_{YG}= \frac{1}{2}\sum_{i,j=1}^n \check{\Delta}(\check{x}_i-\check{x}_j)^2$$ The Yates--Grundy estimator appears to be preferred in most comparisons. It is always non-negative (up to rounding error, at least). In principle, $\pi_{ij}$ might not be available and various approximations have been proposed. The (truncated) Hartley--Rao approximation is $$\check{\Delta}_{ij}=1-\frac{n-\pi_i-\pi_j+\sum_{k=1}^N\pi^2_k/n}{n-1}$$ which requires knowing $\pi_i$ for all units in the population. The population sum can be estimated from the sample, giving a further approximation $$\check{\Delta}_{ij}=1-\frac{n-\pi_i-\pi_j+\sum_{k=1}^n\pi_k/n}{n-1}.$$ that requires only the sample $\pi_i$. Overton's approximation is $$\check{\Delta}_{ij}=1-\frac{n-(\pi_i+\pi_j)/2}{n-1}$$ which also requires only the sample $\pi_i$. In practice, given modern computing power, $\pi_{ij}$ should be available either explicitly or by simulation, so the Hartley--Rao and Overton approximations are not particularly useful. \subsection{Using the PPS estimators} At the moment, only Brewer's approximation can be used as a component of multistage sampling, though for any sampling design it is possible to work out the joint sampling probabilities and use the other approaches. The other approaches can be used for cluster sampling or for sampling of individual units. This is likely to change in the future. To specify a PPS design, the sampling probabilities must be given in the \texttt{prob} argument of \texttt{svydesign}, or in the \texttt{fpc} argument, with \texttt{prob} and \texttt{weight} unspecified. In addition, it is necessary to specify which PPS computation should be used, with the \texttt{pps} argument. The optional \texttt{variance} argument specifies the Horvitz--Thompson (\texttt{variance="HT"}) or Yates--Grundy (\texttt{variance="YG"}) estimator, with the default being \texttt{"HT"}. Some estimators require information in addition to the sampling probabilities for units in the sample. This information is supplied to the \texttt{pps=} argument of \texttt{svydesign} using wrapper functions that create objects with appropriate classes. To specify the population sum $\sum pi_i^2/n$ needed for the Hartley--Rao approximation, use \texttt{HR()}, and to specify a matrix of pairwise sampling probabilities use \texttt{ppsmat()}. The function \texttt{HR()} without an argument will use the Hartley--Rao approximation and estimate the population sum from the sample. The data set \texttt{election} contains county-level voting data from the 2004 US presidential elections, with a PPS sample of size 40 taken using Till\'e's splitting method, from the \texttt{sampling} package. The sampling probabilities vary widely, with Los Angeles County having a probability of 0.9 and many small counties having probabilities less than 0.0005. <<>>= library(survey) data(election) summary(election$p) summary(election_pps$p) @ Some possible survey design specifications for these data are: <<>>= ## Hajek type dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") ## Horvitz-Thompson type dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) ## Yates-Grundy type dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") ## The with-replacement approximation dppswr <-svydesign(id=~1, probs=~p, data=election_pps) @ All the without-replacement design objects except for Brewer's method include a matrix $\check{\Delta}$. These can be visualized with the \texttt{image()} method. These plots use the \texttt{lattice} package and so need \texttt{show()} to display them inside a program: <>= show(image(dpps_ht)) @ <>= show(image(dpps_ov)) @ In this example there are more negative entries in $\check{\Delta}$ with the approximate methods than when the full pairwise sampling matrix is supplied. The estimated totals are the same with all the methods, but the standard errors are not. <<>>= svytotal(~Bush+Kerry+Nader, dpps_ht) svytotal(~Bush+Kerry+Nader, dpps_yg) svytotal(~Bush+Kerry+Nader, dpps_hr) svytotal(~Bush+Kerry+Nader, dpps_hryg) svytotal(~Bush+Kerry+Nader, dpps_hr1) svytotal(~Bush+Kerry+Nader, dpps_br) svytotal(~Bush+Kerry+Nader, dpps_ov) svytotal(~Bush+Kerry+Nader, dppswr) @ \end{document} survey/vignettes/epi.Rnw0000644000176200001440000002362213517345006015075 0ustar liggesusers\documentclass{article} \usepackage{url} \addtolength{\topmargin}{-0.5in} \addtolength{\textheight}{0.75in} \addtolength{\oddsidemargin}{-0.5in} \addtolength{\textwidth}{1in} %\VignetteIndexEntry{Two-phase designs in epidemiology} \usepackage{Sweave} \author{Thomas Lumley} \title{Two-phase designs in epidemiology} \begin{document} \maketitle This document explains how to analyse case--cohort and two-phase case--control studies with the ``survey'' package, using examples from \url{http://faculty.washington.edu/norm/software.html}. Some of the examples were published by Breslow \& Chatterjee (1999). The data are relapse rates from the National Wilm's Tumor Study (NWTS). Wilm's Tumour is a rare cancer of the kidney in children. Intensive treatment cures the majority of cases, but prognosis is poor when the disease is advanced at diagnosis and for some histological subtypes. The histological characterisation of the tumour is difficult, and histological group as determined by the NWTS central pathologist predicts much better than determinations by local institution pathologists. In fact, local institution histology can be regarded statistically as a pure surrogate for the central lab histology. In these examples we will pretend that the (binary) local institution histology determination (\texttt{instit}) is avavailable for all children in the study and that the central lab histology (\texttt{histol}) is obtained for a probability sample of specimens in a two-phase design. We treat the initial sampling of the study as simple random sampling from an infinite superpopulation. We also have data on disease stage, a four-level variable; on relapse; and on time to relapse. \section*{Case--control designs} Breslow \& Chatterjee (1999) use the NWTS data to illustrate two-phase case--control designs. The data are available at \url{http://faculty.washington.edu/norm/software.html} in compressed form; we first expand to one record per patient. <<>>= library(survey) load(system.file("doc","nwts.rda",package="survey")) nwtsnb<-nwts nwtsnb$case<-nwts$case-nwtsb$case nwtsnb$control<-nwts$control-nwtsb$control a<-rbind(nwtsb,nwtsnb) a$in.ccs<-rep(c(TRUE,FALSE),each=16) b<-rbind(a,a) b$rel<-rep(c(1,0),each=32) b$n<-ifelse(b$rel,b$case,b$control) index<-rep(1:64,b$n) nwt.exp<-b[index,c(1:3,6,7)] nwt.exp$id<-1:4088 @ As we actually do know \texttt{histol} for all patients we can fit the logistic regression model with full sampling to compare with the two-phase analyses <<>>= glm(rel~factor(stage)*factor(histol), family=binomial, data=nwt.exp) @ The second phase sample consists of all patients with unfavorable histology as determined by local institution pathologists, all cases, and a 20\% sample of the remainder. Phase two is thus a stratified random sample without replacement, with strata defined by the interaction of \texttt{instit} and \texttt{rel}. <<>>= dccs2<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,rel)),data=nwt.exp) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs2)) @ Disease stage at the time of surgery is also recorded. It could be used to further stratify the sampling, or, as in this example, to post-stratify. We can analyze the data either pretending that the sampling was stratified or using \texttt{calibrate} to post-stratify the design. <<>>= dccs8<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,stage,rel)),data=nwt.exp) gccs8<-calibrate(dccs2,phase=2,formula=~interaction(instit,stage,rel)) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs8)) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=gccs8)) @ \section*{Case--cohort designs} In the case--cohort design for survival analysis, a $P$\% sample of a cohort is taken at recruitment for the second phase, and all participants who experience the event (cases) are later added to the phase-two sample. Viewing the sampling design as progressing through time in this way, as originally proposed, gives a double sampling design at phase two. It is simpler to view the process \emph{sub specie aeternitatis}, and to note that cases are sampled with probability 1, and controls with probability $P/100$. The subcohort will often be determined retrospectively rather than at recruitment, giving stratified random sampling without replacement, stratified on case status. If the subcohort is determined prospectively we can use the same analysis, post-stratifying rather than stratifying. There have been many analyses proposed for the case--cohort design (Therneau \& Li, 1999). We consider only those that can be expressed as a Horvitz--Thompson estimator for the Cox model. First we load the data and the necessary packages. The version of the NWTS data that includes survival times is not identical to the data set used for case--control analyses above. <<>>= library(survey) library(survival) data(nwtco) ntwco<-subset(nwtco, !is.na(edrel)) @ Again, we fit a model that uses \texttt{histol} for all patients, to compare with the two-phase design <<>>= coxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12),data=nwtco) @ We define a two-phase survey design using simple random superpopulation sampling for the first phase, and sampling without replacement stratified on \texttt{rel} for the second phase. The \texttt{subset} argument specifies that observations are in the phase-two sample if they are in the subcohort or are cases. As before, the data structure is rectangular, but variables measured at phase two may be \texttt{NA} for participants not included at phase two. We compare the result to that given by \texttt{survival::cch} for Lin \& Ying's (1993) approach to the case--cohort design. <<>>= (dcch<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco)) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcch) subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing") @ Barlow (1994) proposes an analysis that ignores the finite population correction at the second phase. This simplifies the standard error estimation, as the design can be expressed as one-phase stratified superpopulation sampling. The standard errors will be somewhat conservative. More data preparation is needed for this analysis as the weights change over time. <<>>= nwtco$eventrec<-rep(0,nrow(nwtco)) nwtco.extra<-subset(nwtco, rel==1) nwtco.extra$eventrec<-1 nwtco.expd<-rbind(subset(nwtco,in.subcohort==1),nwtco.extra) nwtco.expd$stop<-with(nwtco.expd, ifelse(rel & !eventrec, edrel-0.001,edrel)) nwtco.expd$start<-with(nwtco.expd, ifelse(rel & eventrec, edrel-0.001, 0)) nwtco.expd$event<-with(nwtco.expd, ifelse(rel & eventrec, 1, 0)) nwtco.expd$pwts<-ifelse(nwtco.expd$event, 1, 1/with(nwtco,mean(in.subcohort | rel))) @ The analysis corresponds to a cluster-sampled design in which individuals are sampled stratified by subcohort membership and then time periods are sampled stratified by event status. Having individual as the primary sampling unit is necessary for correct standard error calculation. <<>>= (dBarlow<-svydesign(id=~seqno+eventrec, strata=~in.subcohort+rel, data=nwtco.expd, weight=~pwts)) svycoxph(Surv(start,stop,event)~factor(stage)+factor(histol)+I(age/12), design=dBarlow) @ In fact, as the finite population correction is not being used the second stage of the cluster sampling could be ignored. We can also produce the stratified bootstrap standard errors of Wacholder et al (1989), using a replicate weights analysis <<>>= (dWacholder <- as.svrepdesign(dBarlow,type="bootstrap",replicates=500)) svycoxph(Surv(start,stop,event)~factor(stage)+factor(histol)+I(age/12), design=dWacholder) @ \subsection*{Exposure-stratified designs} Borgan et al (2000) propose designs stratified or post-stratified on phase-one variables. The examples at \url{http://faculty.washington.edu/norm/software.html} use a different subcohort sample for this stratified design, so we load the new \texttt{subcohort} variable <<>>= load(system.file("doc","nwtco-subcohort.rda",package="survey")) nwtco$subcohort<-subcohort d_BorganII <- twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(instit,rel)), data=nwtco, subset=~I(rel |subcohort)) (b2<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganII)) @ We can further post-stratify the design on disease stage and age with \texttt{calibrate} <<>>= d_BorganIIps <- calibrate(d_BorganII, phase=2, formula=~age+interaction(instit,rel,stage)) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganIIps) @ \section*{References} Barlow WE (1994). Robust variance estimation for the case-cohort design. \emph{Biometrics} 50: 1064-1072 Borgan \O, Langholz B, Samuelson SO, Goldstein L and Pogoda J (2000). Exposure stratified case-cohort designs, \emph{Lifetime Data Analysis} 6:39-58 Breslow NW and Chatterjee N. (1999) Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. \emph{Applied Statistics} 48:457-68. Lin DY, and Ying Z (1993). Cox regression with incomplete covariate measurements. \emph{Journal of the American Statistical Association} 88: 1341-1349. Therneau TM and Li H., Computing the Cox model for case-cohort designs. \emph{Lifetime Data Analysis} 5:99-112, 1999 Wacholder S, Gail MH, Pee D, and Brookmeyer R (1989) Alternate variance and efficiency calculations for the case-cohort design \emph{Biometrika}, 76, 117-123 \end{document} survey/vignettes/qrule.Rnw0000644000176200001440000000774514073741722015463 0ustar liggesusers\documentclass{article} \usepackage{url} %\VignetteIndexEntry{Quantile rules} \usepackage{Sweave} \author{Thomas Lumley} \title{Estimating quantiles} \newlength{\wdth} \newcommand{\strike}[1]{\settowidth{\wdth}{#1}\rlap{\rule[.5ex]{\wdth}{.4pt}}#1} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle The $p$th quantile is defined as the value where the estimated cumulative distribution function is equal to $p$. As with quantiles in unweighted data, this definition only pins down the quantile to an interval between two observations, and a rule is needed to interpolate. As the help for the base R function \texttt{quantile} explains, even before considering sampling weights there are many possible rules. Rules in the \texttt{svyquantile()} function can be divided into three classes \begin{itemize} \item Discrete rules, following types 1 to 3 in \texttt{quantile} \item Continuous rules, following types 4 to 9 in \texttt{quantile} \item A rule proposed by Shah \& Vaish (2006) and used in some versions of \textsf{SUDAAN} \end{itemize} \subsection*{Discrete rules} These are based on the discrete empirical CDF that puts weight proportional to the weight $w_k$ on values $x_k$. $$\hat F(x) = \frac{\sum_i \{x_i\leq x\}w_i}{\sum_i w_i}$$ \paragraph{The mathematical inverse} The mathematical inverse $\hat F^{-1}(p)$ of the CDF is the smallest $x$ such that $F(x)\geq p$. This is rule \texttt{hf1} and \texttt{math} and in equally-weighted data gives the same answer as \texttt{type=1} in \texttt{quantile} \paragraph{The primary-school median} The school definition of the median for an even number of observations is the average of the middle two observations. We extend this to say that the $p$th quantile is $q_{\mathrm{low}}=\hat F^{-1}(p)$ if $\hat F(q_{\mathrm{low}})=p$ and otherwise is the the average of $\hat F^{-1}(p)$ and the next higher observation. This is \texttt{school} and \texttt{hf2} and is the same as \texttt{type=2} in \texttt{quantile}. \paragraph{Nearest even order statistic} The $p$th quantile is whichever of $\hat F^{-1}(p)$ and the next higher observation is at an even-numbered position when the distinct data values are sorted. This is \texttt{hf3} and is the same as \texttt{type=3} in \texttt{quantile}. \subsection*{Continuous rules} These construct the empirical CDF as a piecewise-linear function and read off the quantile. They differ in the choice of points to interpolate. Hyndman \& Fan describe these as interpolating the points $(p_k,x_k)$ where $p_k$ is defined in terms of $k$ and $n$. For weighted use they have been redefined in terms of the cumulative weights $C_k=\sum_{i\leq k} w_i$, the total weight $C_n=\sum w_i$, and the weight $w_k$ on the $k$th observation. \begin{tabular}{lll} {\tt qrule} & Hyndman \& Fan & Weighted\\ \hline {\tt hf4} & $p_k= k/n$ & $p_k = C_k/C_n$\\ {\tt hf5} & $p_k= (k-0.5)/n$ & $p_k = (C_k-w_k)/C_n$\\ {\tt hf6} & $p_k= k/(n+1)$ & $p_k = C_k/(C_n+w_n)$\\ {\tt hf7} & $p_k= (k-1)/(n-1)$ & $p_k = C_{k-1}/C_{n-1}$\\ {\tt hf8} & $p_k= (k-1/3)/(n+2/3)$ & $p_k = (C_k-w_k/3)/(C_n+w_n/3)$\\ {\tt hf9} & $p_k= (k-3/8)/(n+1/4)$ & $p_k = (C_k-3w_k./8)/(C_n+w_n/4)$\\ \hline \end{tabular} \subsection*{Shah \& Vaish} This rule is related to {\tt hf6}, but it is discrete and more complicated. First, define $w^*_i=w_in/C_n$, so that $w^*_i$ sum to the sample size rather than the population size, and $C^*k$ as partial sums of $w^*_k$. Now define the estimated CDF by $$\hat F(x_k) =\frac{1}{n+1}\left(C^*_k+1/2-w_k/2 \right)$$ and take $\hat F^{-1}(p)$ as the $p$th quantile. \subsection*{Other options} It would be possible to redefine all the continuous estimators in terms of $w^*$, so that type 8, for example, would use $$p_k = (C^*_k-1/3)/(C^*_n+2/3)$$ Or a compromise, eg using $w^*_k$ in the numerator and numbers in the denominator, such as $$p_k = (C^*_k-w^*_k/3)/(C^*_n+2/3).$$ Comparing these would be \strike{a worthwhile}\dots\strike{an interesting}... a research question for simulation. \end{document} survey/vignettes/survey-sae.html.asis0000644000176200001440000000011314576426776017571 0ustar liggesusers%\VignetteIndexEntry{Small area estimation} %\VignetteEngine{R.rsp::asis} survey/vignettes/survey.Rnw0000644000176200001440000000670613517345006015661 0ustar liggesusers\documentclass{article} \usepackage{url} %\VignetteIndexEntry{A survey analysis example} \usepackage{Sweave} \author{Thomas Lumley} \title{A survey analysis example} \begin{document} \maketitle This document provides a simple example analysis of a survey data set, a subsample from the California Academic Performance Index, an annual set of tests used to evaluate California schools. The API website, including the original data files are at \url{http://api.cde.ca.gov}. The subsample was generated as a teaching example by Academic Technology Services at UCLA and was obtained from \url{http://www.ats.ucla.edu/stat/stata/Library/svy_survey.htm}. We have a cluster sample in which 15 school districts were sampled and then all schools in each district. This is in the data frame \texttt{apiclus1}, loaded with \texttt{data(api)}. The two-stage sample is defined by the sampling unit (\texttt{dnum}) and the population size(\texttt{fpc}). Sampling weights are computed from the population sizes, but could be provided separately. <<>>= library(survey) data(api) dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) @ The \texttt{svydesign} function returns an object containing the survey data and metadata. <<>>= summary(dclus1) @ We can compute summary statistics to estimate the mean, median, and quartiles of the Academic Performance Index in the year 2000, the number of elementary, middle, and high schools in the state, the total number of students, and the proportion who took the test. Each function takes a formula object describing the variables and a survey design object containing the data. <<>>= svymean(~api00, dclus1) svyquantile(~api00, dclus1, quantile=c(0.25,0.5,0.75), ci=TRUE) svytotal(~stype, dclus1) svytotal(~enroll, dclus1) svyratio(~api.stu,~enroll, dclus1) @ The ordinary R subsetting functions \verb'[' and \texttt{subset} work correctly on these survey objects, carrying along the metadata needed for valid standard errors. Here we compute the proportion of high school students who took the test <<>>= svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H")) @ The warnings referred to in the output occured because several school districts have only one high school sampled, making the second stage standard error estimation unreliable. Specifying a large number of variables is made easier by the \texttt{make.formula} function <<>>= vars<-names(apiclus1)[c(12:13,16:23,27:37)] svymean(make.formula(vars),dclus1,na.rm=TRUE) @ Summary statistics for subsets can also be computed with \texttt{svyby}. Here we compute the average proportion of ``English language learners'' and of students eligible for subsidized school meals for elementary, middle, and high schools <<>>= svyby(~ell+meals, ~stype, design=dclus1, svymean) @ Regression models show that these socieconomic variables predict API score and whether the school achieved its API target <<>>= regmodel <- svyglm(api00~ell+meals,design=dclus1) logitmodel <- svyglm(I(sch.wide=="Yes")~ell+meals, design=dclus1, family=quasibinomial()) summary(regmodel) summary(logitmodel) @ We can calibrate the sampling using the statewide total for the previous year's API <<>>= gclus1 <- calibrate(dclus1, formula=~api99, population=c(6194, 3914069)) @ which improves estimation of some quantities <<>>= svymean(~api00, gclus1) svyquantile(~api00, gclus1, quantile=c(0.25,0.5,0.75), ci=TRUE) svytotal(~stype, gclus1) svytotal(~enroll, gclus1) svyratio(~api.stu,~enroll, gclus1) @ \end{document} survey/vignettes/precalibrated.Rnw0000644000176200001440000000611114232650344017112 0ustar liggesusers\documentclass{article} %\VignetteIndexEntry{Pre-calibrated weights} \usepackage{Sweave} \title{Making use of pre-calibrated weights} \begin{document} \maketitle \SweaveOpts{concordance=TRUE} Public-use data sets often come with weights that have been adjusted by post-stratification, raking, or calibration. It is standard practice to ignore this fact and treat the weights as if they were sampling weights. An alternative approach is to calibrate the weights again in R. This might seem impossible: calibration needs the population totals. But we know the population totals, because the effect of calibration is precisely that the estimated population totals match the true population totals. From version 4.2, the \texttt{svydesign} function has an option \texttt{calibrate.formula} to specify (as a model formula) the variables that the weights are already calibrated on. The weights will be recalibrated using this formula, to population totals estimated from the sample. The weights will not change --- they were already calibrated --- but information will be added to the survey design object to describe the calibration constraints and standard errors of estimates will change. Consider, for example, the \texttt{apiclus1} example. This is a cluster sample of 15 school districts in California <<>>= library(survey) data(api) dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) @ The sampling weights \texttt{pw} are already calibrated to sum to the known number of schools in the population, 6194, but R does not know this: <<>>= sum(weights(dclus1)) dim(apipop) dclus1<-update(dclus1, one=rep(1,nrow(dclus1))) svytotal(~one,dclus1) @ The standard error should be zero, because the calibration procedure ensures that the the estimated total is exactly 6194. We could use \texttt{calibrate()} on the design object <<>>= cal_dclus1<-calibrate(dclus1, formula=~1, population=sum(weights(dclus1))) svytotal(~one,cal_dclus1) @ The standard errors have changed, but the weights haven't <<>>= summary(weights(cal_dclus1)/weights(dclus1)) @ With the new option to \texttt{svydesign} we can do the same thing when the object is created <<>>= precal_dclus1<-svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc, calibrate.formula=~1) precal_dclus1<-update(precal_dclus1, one=rep(1,nrow(dclus1))) svytotal(~one,precal_dclus1) @ Calibrating to the population size simplifies the standard error relationship between mean and total <<>>= (enroll_t<-svytotal(~enroll, dclus1)) (enroll_m<-svymean(~enroll, dclus1)) SE(enroll_m) SE(enroll_t)/6194 (cenroll_t<-svytotal(~enroll, precal_dclus1)) (cenroll_m<-svymean(~enroll, precal_dclus1)) SE(cenroll_m) SE(cenroll_t)/6194 @ Because calibration in this way changes the standard errors but not the point estimates, it's critical that you {\em only use it when the weights are in fact already calibrated}. If not, the standard errors will be wrong. In particular, it is not valid to take a subset of a data set with calibrated weights and then pretend the subset was also calibrated on the same variables. \end{document} survey/vignettes/domain.Rnw0000644000176200001440000000771613517345006015575 0ustar liggesusers\documentclass{article} \usepackage{url} %\VignetteIndexEntry{Estimates in subpopulations} \usepackage{Sweave} \author{Thomas Lumley} \title{Estimates in subpopulations.} \begin{document} \maketitle Estimating a mean or total in a subpopulation (domain) from a survey, eg the mean blood pressure in women, is not done simply by taking the subset of data in that subpopulation and pretending it is a new survey. This approach would give correct point estimates but incorrect standard errors. The standard way to derive domain means is as ratio estimators. I think it is easier to derive them as regression coefficients. These derivations are not important for R users, since subset operations on survey design objects automatically do the necessary adjustments, but they may be of interest. The various ways of constructing domain mean estimators are useful in quality control for the survey package, and some of the examples here are taken from \texttt{survey/tests/domain.R}. Suppose that in the artificial \texttt{fpc} data set we want to estimate the mean of \texttt{x} when \texttt{x>4}. <<>>= library(survey) data(fpc) dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) dsub<-subset(dfpc,x>4) svymean(~x,design=dsub) @ The \texttt{subset} function constructs a survey design object with information about this subpopulation and \texttt{svymean} computes the mean. The same operation can be done for a set of subpopulations with \texttt{svyby}. <<>>= svyby(~x,~I(x>4),design=dfpc, svymean) @ In a regression model with a binary covariate $Z$ and no intercept, there are two coefficients that estimate the mean of the outcome variable in the subpopulations with $Z=0$ and $Z=1$, so we can construct the domain mean estimator by regression. <<>>= summary(svyglm(x~I(x>4)+0,design=dfpc)) @ Finally, the classical derivation of the domain mean estimator is as a ratio where the numerator is $X$ for observations in the domain and 0 otherwise and the denominator is 1 for observations in the domain and 0 otherwise <<>>= svyratio(~I(x*(x>4)),~as.numeric(x>4), dfpc) @ The estimator is implemented by setting the sampling weight to zero for observations not in the domain. For most survey design objects this allows a reduction in memory use, since only the number of zero weights in each sampling unit needs to be kept. For more complicated survey designs, such as post-stratified designs, all the data are kept and there is no reduction in memory use. \subsection*{More complex examples} Verifying that \texttt{svymean} agrees with the ratio and regression derivations is particularly useful for more complicated designs where published examples are less readily available. This example shows calibration (GREG) estimators of domain means for the California Academic Performance Index (API). <<>>= data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) gclus1 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069)) svymean(~api00, subset(gclus1, comp.imp=="Yes")) svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), gclus1) summary(svyglm(api00~comp.imp-1, gclus1)) @ Two-stage samples with full finite-population corrections <<>>= data(mu284) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) svymean(~y1, subset(dmu284,y1>40)) svyratio(~I(y1*(y1>40)),~as.numeric(y1>40),dmu284) summary(svyglm(y1~I(y1>40)+0,dmu284)) @ Stratified two-phase sampling of children with Wilm's Tumor, estimating relapse probability for those older than 3 years (36 months) at diagnosis <<>>= library("survival") data(nwtco) nwtco$incc2<-as.logical(with(nwtco, ifelse(rel | instit==2,1,rbinom(nrow(nwtco),1,.1)))) dccs8<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2) svymean(~rel, subset(dccs8,age>36)) svyratio(~I(rel*as.numeric(age>36)), ~as.numeric(age>36), dccs8) summary(svyglm(rel~I(age>36)+0, dccs8)) @ \end{document} survey/R/0000755000176200001440000000000014571730437012023 5ustar liggesuserssurvey/R/olr.R0000644000176200001440000003147014526736255012753 0ustar liggesuserssvyolr<-function(formula, design,...) UseMethod("svyolr",design) ## ## Much of this is taken from MASS polr, thus the GPL-2 license. ## svyolr.svyrep.design<-function(formula,design,subset=NULL,...,return.replicates=FALSE, multicore=getOption("survey.multicore")){ environment(formula)<-environment() subset<-substitute(subset) subset<-eval(subset, model.frame(design), parent.frame()) if (!is.null(subset)){ if (any(is.na(subset))) stop("subset must not contain NA values") design<-design[subset,] } df<-model.frame(design) pwt<-weights(design,"sampling") if (multicore && !requireNamespace("parallel", quietly=TRUE)) multicore <- FALSE rval<-suppressWarnings(MASS::polr(formula,data=df,...,Hess=TRUE,model=FALSE, weights=pwt)) start<-c(rval$coefficients,rval$zeta) rw<-weights(design,"analysis") if (multicore){ betas<-do.call(cbind,parallel::mclapply(1:ncol(rw), function(i){ w<-rw[,i] environment(formula)<-environment() m<-MASS::polr(formula,data=df,Hess=FALSE, start=start, model=FALSE, weights=w) c(m$coefficients, m$zeta) })) } else { betas<-apply(rw,2,function(w) { environment(formula)<-environment() m<-MASS::polr(formula,data=df,Hess=FALSE, start=start, model=FALSE, weights=w) c(m$coefficients, m$zeta) }) } rval$var<-svrVar(t(betas),design$scale,design$rscales,mse=design$mse, coef=start) rval$df.residual<-degf(design)-length(rval$coefficients) rval$deviance<-rval$deviance/mean(pwt) rval$survey.design<-design class(rval)<-"svyolr" rval$call<-sys.call() rval$call[[1]]<-as.name(.Generic) if (return.replicates) rval$replicates<-t(betas) rval } pgumbel<- function (q, loc = 0, scale = 1, lower.tail = TRUE) { q <- (q - loc)/scale p <- exp(-exp(-q)) if (!lower.tail) 1 - p else p } dgumbel<-function (x, loc = 0, scale = 1, log = FALSE) { x <- (x - loc)/scale d <- log(1/scale) - x - exp(-x) if (!log) exp(d) else d } svyolr.survey.design2<-function (formula, design, start, subset=NULL,..., na.action=na.omit, method = c("logistic", "probit", "cloglog", "cauchit")) { logit <- function(p) log(p/(1 - p)) fmin <- function(beta) { theta <- beta[pc + 1:q] gamm <- c(-100, cumsum(c(theta[1], exp(theta[-1]))), 100) eta <- offset if (pc > 0) eta <- eta + drop(x %*% beta[1:pc]) pr <- pfun(gamm[y + 1] - eta) - pfun(gamm[y] - eta) if (all(pr > 0)) -sum(wt * log(pr)) else Inf } jacobian <- function(theta) { k <- length(theta) etheta <- exp(theta) mat <- matrix(0, k, k) mat[, 1] <- rep(1, k) for (i in 2:k) mat[i:k, i] <- etheta[i] mat } gmini <- function(beta,logdiff=FALSE) { theta <- beta[pc + 1:q] gamm <- c(-100, cumsum(c(theta[1], exp(theta[-1]))), 100) eta <- offset if (pc > 0) eta <- eta + drop(x %*% beta[1:pc]) pr <- pfun(gamm[y + 1] - eta) - pfun(gamm[y] - eta) p1 <- dfun(gamm[y + 1] - eta) p2 <- dfun(gamm[y] - eta) g1 <- if (pc > 0) x * (wt * (p1 - p2)/pr) else numeric(0) xx <- .polrY1 * p1 - .polrY2 * p2 g2 <- - xx * (wt/pr) if (logdiff) g2 <- g2 %*% jacobian(theta) if (all(pr > 0)) cbind(g1, g2) else NA+cbind(g1,g2) } gmin<-function(beta){ colSums(gmini(beta,logdiff=TRUE)) } m <- match.call(expand.dots = FALSE) method <- match.arg(method) pfun <- switch(method, logistic = plogis, probit = pnorm, cloglog = pgumbel, cauchit = pcauchy) dfun <- switch(method, logistic = dlogis, probit = dnorm, cloglog = dgumbel, cauchit = dcauchy) subset<-substitute(subset) subset<-eval(subset, model.frame(design), parent.frame()) if (!is.null(subset)){ if (any(is.na(subset))) stop("subset must not contain NA values") design<-design[subset,] } m<-model.frame(formula,model.frame(design),na.action=na.pass) Terms <- attr(m, "terms") m<-na.action(m) nao<-attr(m,"na.action") if(length(nao)) { design<-design[-nao,] } x <- model.matrix(Terms, m) xint <- match("(Intercept)", colnames(x), nomatch = 0) n <- nrow(x) pc <- ncol(x) cons <- attr(x, "contrasts") if (xint > 0) { x <- x[, -xint, drop = FALSE] pc <- pc - 1 } else warning("an intercept is needed and assumed") keep<-weights(design)!=0 wt <- weights(design)[keep] offset <- model.offset(m) if (length(offset) <= 1) offset <- rep(0, n) y <- model.response(m) if (!is.factor(y)) stop("response must be a factor") lev <- levels(y) if (length(lev) <= 2) stop("response must have 3 or more levels") y <- unclass(y) q <- length(lev) - 1 Y <- matrix(0, n, q) .polrY1 <- col(Y) == y .polrY2 <- col(Y) == y - 1 if (missing(start)) { q1 <- length(lev)%/%2 y1 <- (y > q1) X <- cbind(Intercept = rep(1, n), x) fit <- switch(method, logistic = glm.fit(X, y1, wt/mean(wt), family = quasibinomial(), offset = offset), probit = glm.fit(X, y1, wt/mean(wt), family = quasibinomial("probit"), offset = offset), cloglog = glm.fit(X, y1, wt/mean(wt), family = quasibinomial("probit"), offset = offset), cauchit = glm.fit(X, y1, wt/mean(wt), family = quasibinomial("cauchit"), offset = offset)) if (!fit$converged) stop("attempt to find suitable starting values failed") coefs <- fit$coefficients if (any(is.na(coefs))) { warning("design appears to be rank-deficient, so dropping some coefs") betakeep <- names(coefs)[!is.na(coefs)] coefs <- coefs[betakeep] x <- x[, betakeep[-1], drop = FALSE] pc <- ncol(x) } spacing <- logit((1:q)/(q + 1)) if (method != "logit") spacing <- spacing/1.7 gammas <- -coefs[1] + spacing - spacing[q1] thetas <- c(gammas[1], log(diff(gammas))) start <- c(coefs[-1], thetas) } else if (length(start) != pc + q) stop("'start' is not of the correct length") res <- optim(start, fmin, gmin, method = "BFGS", hessian = TRUE, ...) beta <- res$par[seq_len(pc)] theta <- res$par[pc + 1:q] zeta <- cumsum(c(theta[1], exp(theta[-1]))) deviance <- 2 * res$value/mean(wt) niter <- c(f.evals = res$counts[1], g.evals = res$counts[2]) names(zeta) <- paste(lev[-length(lev)], lev[-1], sep = "|") if (pc > 0) { names(beta) <- colnames(x) eta <- drop(x %*% beta) } else { eta <- rep(0, n) } cumpr <- matrix(pfun(matrix(zeta, n, q, byrow = TRUE) - eta), , q) fitted <- t(apply(cumpr, 1, function(x) diff(c(0, x, 1)))) dimnames(fitted) <- list(row.names(m), lev) fit <- list(coefficients = beta, zeta = zeta, deviance = deviance, fitted.values = fitted, lev = lev, terms = Terms, df.residual = sum(wt) - pc - q, edf = pc + q, n = sum(wt), nobs = sum(wt), method = method, convergence = res$convergence, niter = niter) dn <- c(names(beta), names(zeta)) ## MASS::polr reparametrises the intercepts other than the first ## using logs of differences (to ensure the ordering). Need to undo it. vc <- MASS::ginv(res$hessian) pc <- length(beta) gamma <- fit$zeta z.ind <- pc + seq_along(gamma) theta <- c(gamma[1L], log(diff(gamma))) J <- jacobian(theta) A <- diag(pc + length(gamma)) A[z.ind, z.ind] <- J Vm <- A %*% vc %*% t(A) dimnames(Vm) <- list(dn, dn) fit$Hessian<-H <- solve(Vm) ## fit$call<-sys.call() fit$call[[1]]<-as.name(.Generic) inffun<- gmini(res$par, logdiff=FALSE)%*%solve(H) if(any(!keep)){ ## subsets of raked designs inffun1<-matrix(0,ncol=NCOL(inffun), nrow=length(keep)) inffun1[keep,]<-inffun inffun<-inffun1 } fit$var<-svyrecvar(inffun, design$cluster, design$strata, design$fpc, postStrata = design$postStrata) fit$df.residual<-degf(design)-length(beta) fit$survey.design<-design fit$na.action <- attr(m, "na.action") fit$contrasts <- cons fit$xlevels <- .getXlevels(Terms, m) class(fit) <- "svyolr" fit } vcov.svyolr<-function(object,...) object$var print.svyolr<-function (x, ...) { if (!is.null(cl <- x$call)) { cat("Call:\n") dput(cl, control = NULL) } if (length(coef(x))) { cat("\nCoefficients:\n") print(coef(x,intercept=FALSE), ...) } else { cat("\nNo coefficients\n") } cat("\nIntercepts:\n") print(x$zeta, ...) invisible(x) } coef.svyolr<-function(object,intercepts=TRUE,...) { if(intercepts) c(object$coefficients, object$zeta) else object$coefficients } summary.svyolr<-function (object, digits = max(3, .Options$digits - 3), correlation = FALSE, ...) { cc <- coef(object) pc <- length(coef(object, FALSE)) q <- length(object$zeta) coef <- matrix(0, pc + q, 3, dimnames = list(names(cc), c("Value", "Std. Error", "t value"))) coef[, 1] <- cc vc <- vcov(object) z.ind <- (pc + 1):(pc + q) gamma <- object$zeta theta <- c(gamma[1], log(diff(gamma))) ## used to have jacobian here but it's now in svyolr coef[, 2] <- sd <- sqrt(diag(vc)) coef[, 3] <- coef[, 1]/coef[, 2] object$coefficients <- coef object$pc <- pc object$digits <- digits if (correlation) object$correlation <- (vc/sd)/rep(sd, rep(pc + q, pc + q)) class(object) <- "summary.svyolr" object } print.summary.svyolr<-function (x, digits = x$digits, ...) { if (!is.null(cl <- x$call)) { cat("Call:\n") dput(cl, control = NULL) } coef <- format(round(x$coefficients, digits = digits)) pc <- x$pc if (pc > 0) { cat("\nCoefficients:\n") print(x$coefficients[seq_len(pc), , drop = FALSE], quote = FALSE, ...) } else { cat("\nNo coefficients\n") } cat("\nIntercepts:\n") print(coef[(pc + 1):nrow(coef), , drop = FALSE], quote = FALSE, ...) if (nzchar(mess <- naprint(x$na.action))) cat("(", mess, ")\n", sep = "") if (!is.null(correl <- x$correlation)) { cat("\nCorrelation of Coefficients:\n") ll <- lower.tri(correl) correl[ll] <- format(round(correl[ll], digits)) correl[!ll] <- "" print(correl[-1, -ncol(correl)], quote = FALSE, ...) } invisible(x) } model.frame.svyolr<-function(formula, ...){ mcall <- match.call(svyolr, formula$call) e<-environment(formula(formula)) formula<-eval(mcall$formula, envir=e) design<- eval(mcall$design, envir=e) mf<-model.frame(formula,model.frame(design)) w<-weights(design, type="sampling") if (is.null(naa<-attr(mf,"na.action"))) mf[["(weights)"]]<-w else mf[["(weights)"]]<-w[-naa] mf } ## taken from MASS::predict.polr predict.svyolr<-function (object, newdata, type = c("class", "probs"), ...) { type <- match.arg(type) if (missing(newdata)) Y <- object$fitted else { newdata <- as.data.frame(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = function(x) x, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) X <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(X), nomatch = 0L) if (xint > 0L) X <- X[, -xint, drop = FALSE] n <- nrow(X) q <- length(object$zeta) eta <- drop(X %*% object$coefficients) pfun <- switch(object$method, logistic = plogis, probit = pnorm, loglog = pgumbel, cloglog = pGumbel, cauchit = pcauchy) cumpr <- matrix(pfun(matrix(object$zeta, n, q, byrow = TRUE) - eta), , q) Y <- t(apply(cumpr, 1L, function(x) diff(c(0, x, 1)))) dimnames(Y) <- list(rownames(X), object$lev) } if (missing(newdata) && !is.null(object$na.action)) Y <- napredict(object$na.action, Y) if (type == "class") factor(max.col(Y), levels = seq_along(object$lev), labels = object$lev) else drop(Y) } ## taken from MASS::pGumbel pGumbel<-function (q, loc = 0, scale = 1, lower.tail = TRUE) { q <- (q - loc)/scale p <- exp(-exp(q)) if (lower.tail) 1 - p else p } survey/R/naa.R0000644000176200001440000000434414066261522012703 0ustar liggesusersnaa_longer<-function(naa, object,...) UseMethod("naa_longer",naa) naa_shorter<-function(naa, object,...) UseMethod("naa_shorter",naa) naa_longer.NULL<-function(naa, object,...) object naa_shorter.NULL<-function(naa, object,...) object naa_longer.default<-function(naa, object,...) stop("no default method (not psychic)") naa_shorter.default<-function(naa, object,...) stop("no default method (not psychic)") naa_longer.fail<-function(naa, object,...) stop("can't happen (na.fail)") naa_shorter.fail<-function(naa, object,...) stop("can't happen (na.fail)") naa_shorter.omit<-function(naa, object,...) object naa_longer.omit<-function(naa,object,...){ ##from naresid.exclude if (length(naa) == 0 || !is.numeric(naa)) stop("invalid argument 'naa'") if (is.null(object)) return(object) n <- NROW(object) keep <- rep.int(NA, n + length(naa)) keep[-naa] <- 1L:n if (is.matrix(object)) { object <- object[keep, , drop = FALSE] temp <- rownames(object) if (length(temp)) { temp[naa] <- names(naa) rownames(object) <- temp } } else if (is.array(object) && length(d <- dim(object)) > 2L) { object <- object[keep, , , drop = FALSE] temp <- (dn <- dimnames(object))[[1L]] if (!is.null(temp)) { temp[naa] <- names(naa) dimnames(object)[[1L]] <- temp } } else { object <- object[keep] temp <- names(object) if (length(temp)) { temp[naa] <- names(naa) names(object) <- temp } } object } naa_longer.exclude<-function(naa,object,...) object naa_shorter.exclude<-function(naa,object,...) { if (length(naa) == 0 || !is.numeric(naa)) stop("invalid argument 'naa'") if (is.null(object)) return(object) n <- NROW(object) keep <- (1:n)[-naa] if (is.matrix(object)) { object <- object[keep, , drop = FALSE] temp <- rownames(object) } else if (is.array(object) && length(d <- dim(object)) > 2L) { object <- object[keep, , , drop = FALSE] temp <- (dn <- dimnames(object))[[1L]] } else { object <- object[keep] temp <- names(object) } object } survey/R/count.R0000644000176200001440000000172413517345031013271 0ustar liggesusersunwtd.count<-function(x, design,...){ if (inherits(x, "formula")) { mf <- model.frame(x, model.frame(design), na.action = na.pass) xx <- lapply(attr(terms(x), "variables")[-1], function(tt) model.matrix(eval(bquote(~0 + .(tt))), mf) ) cols <- sapply(xx, NCOL) x <- matrix(nrow = NROW(xx[[1]]), ncol = sum(cols)) scols <- c(0, cumsum(cols)) for (i in 1:length(xx)) { x[, scols[i] + 1:cols[i]] <- xx[[i]] } colnames(x) <- do.call("c", lapply(xx, colnames)) } else if (typeof(x) %in% c("expression", "symbol")) x <- eval(x, model.frame(design)) x <- as.matrix(x) out<- weights(design,"sampling")==0 nas <- rowSums(is.na(x)) x <- x[(nas+out) == 0, , drop = FALSE] rval<-NROW(x) names(rval)<-"counts" attr(rval,"var")<-matrix(0,1,1) attr(rval,"statistic")<-"counts" if (inherits(design,"svyrep.design")) class(rval)<-"svrepstat" else class(rval)<-"svystat" rval } survey/R/greg.R0000644000176200001440000002140414074217675013075 0ustar liggesusers regcalibrate<-function(design, ...) UseMethod("regcalibrate") is.calibrated<-function(design){ !is.null(design$postStrata)} regcalibrate.pps<-function(design,formula, population, stage=NULL, lambda=NULL, aggregate.stage=NULL,...){ if (!is.null(stage) && !(stage==0)) stop("'stage' not supported for pps designs") if (!is.null(aggregate.stage)) stop("'aggregate.stage' not supported for pps designs") regcalibrate.survey.design2(design,formula, population, stage=NULL, lambda=NULL, aggregate.stage=NULL,...) } ## ## unbounded linear calibration using qr decomposition: less sensitive to ## collinearity than Deville & Sarndal's Newton algorithm. ## regcalibrate.survey.design2<-function(design, formula, population, stage=NULL, lambda=NULL, aggregate.stage=NULL, sparse=FALSE,...){ if (is.null(stage)) stage<-if (is.list(population)) 1 else 0 if (!is.null(aggregate.stage)){ aggindex<-design$cluster[[aggregate.stage]] } if(stage==0){ ## calibration to population totals if(sparse){ mm<-sparse.model.matrix(formula, model.frame(formula, model.frame(design))) }else{ mm<-model.matrix(formula, model.frame(formula, model.frame(design))) } ww<-weights(design) if (is.null(lambda)){ sigma2<-rep(1,nrow(mm)) }else if(length(lambda) == nrow(mm)){ # for the heteroskedasticity parameter sigma2<-drop(lambda) }else{ sigma2<-drop(mm%*%lambda) # to keep the same functionality when variance = 1 } if (!is.null(aggregate.stage)){ mm<-apply(mm,2,function(mx) ave(mx,aggindex)) ww<-ave(ww,aggindex) sigma2<-ave(sigma2,aggindex) } whalf<-sqrt(ww) sample.total<-colSums(mm*ww) if(any(sample.total==0)){ ## drop columsn where all sample and population are zero zz<-(population==0) & (apply(mm,2,function(x) all(x==0))) mm<-mm[,!zz] population<-population[!zz] sample.total<-sample.total[!zz] } if (!is.null(names(population))){ if (!all(names(sample.total) %in% names(population))){ warning("Sampling and population totals have different names.") cat("Sample: "); print(names(sample.total)) cat("Popltn: "); print(names(population)) } else if (!all(names(sample.total) == names(population))){ warning("Sample and population totals reordered to make names agree: check results.") population <- population[match(names(sample.total), names(population))] } } tqr<-qr(mm*whalf/sqrt(sigma2)) ## not needed ##if (is.null(lambda) && !all(abs(qr.resid(tqr,whalf*sigma2)/sigma2) <1e-5)) ## warning("Calibration models with constant variance must have an intercept") g<-rep(1,NROW(mm)) Tmat<-crossprod(mm*whalf/sqrt(sigma2)) tT<-solve(Tmat,population-sample.total) g<-drop(1+mm%*%tT/sigma2) design$prob<-design$prob/g caldata<- list(qr=tqr, w=g*whalf*sqrt(sigma2), stage=0, index=NULL) } else { ## Calibration within clusters (Sarndal's Case C) if (stage>NCOL(design$cluster)) stop("This design does not have stage",stage) if (!is.null(aggregate.stage)){ stop("aggregate= not implemented for calibration within clusters") } if (!all(length(population[[1]])==sapply(population,length))) stop("Population totals are not all the same length") clusters<-unique(design$cluster[,stage]) nc<-length(clusters) caldata<-list(qr=vector("list",nc), w=vector("list",nc), stage=stage,index=as.character(clusters)) if(sparse){ mm<-sparse.model.matrix(formula, model.frame(formula, model.frame(design))) }else{ mm<-model.matrix(formula, model.frame(formula, model.frame(design))) } if (is.null(lambda)){ sigma2<-rep(1,nrow(mm)) }else if(length(lambda) == nrow(mm)){ # for the heteroskedasticity parameter sigma2<-drop(lambda) }else{ sigma2<-drop(mm%*%lambda) # to keep the same functionality when variance = 1 } if(NCOL(mm)!=length(population[[1]])){ stop("Population and sample totals are not the same length.") } if (any(colnames(mm)!=names(population[[1]]))){ warning("Sampling and population totals have different names.") cat("Sample: "); print(colnames(mm)) cat("Popltn: "); print(names(population[[1]])) } stageweights<-1/apply(design$allprob[,1:stage,drop=FALSE],1,prod) if (any(duplicated(design$cluster[!duplicated(stageweights),stage]))) stop("Weights at stage", stage, "vary within sampling units") cwhalf<-sqrt(weights(design)/stageweights) dwhalf<-sqrt(weights(design)) tqr<-qr(mm) ## not needed ## if (is.null(lambda) && !all(abs(qr.resid(tqr,sigma2)) <1e-3)) ## stop("Calibration models with constant variance must have an intercept") for (i in 1:length(clusters)){ cluster<-clusters[[i]] these<-which(cluster == as.character(design$cluster[,stage])) mmi<-mm[these,,drop=FALSE] sample.total<-colSums(mmi*cwhalf[these]*cwhalf[these]) if(any(sample.total==0)){ ## drop columsn where all sample and population are zero zz<-(population[[i]]==0) & (apply(mmi,2,function(x) all(x==0))) mmi<-mmi[,!zz,drop=FALSE] population[[i]]<-population[[i]][!zz] sample.total<-sample.total[!zz] } tqr<-qr(mmi*cwhalf[these]/sqrt(sigma2[these])) Tmat<-crossprod(mmi*cwhalf[these]/sqrt(sigma2[these])) tT<-solve(Tmat,population[[i]]-sample.total) g<-drop(1+mmi%*%tT/sigma2[these]) design$prob[these]<-design$prob[these]/g caldata$qr[[i]]<-tqr caldata$w[[i]]<-g*stageweights[these]*sqrt(sigma2[these])*cwhalf[these]^2 } } class(caldata)<-"greg_calibration" design$postStrata<-c(design$postStrata, list(caldata)) design$call<-sys.call(-1) design } regcalibrate.svyrep.design<-function(design, formula, population,compress=NA,lambda=NULL, aggregate.index=NULL,sparse=FALSE,...){ mf<-model.frame(formula, design$variables) if(sparse){ mm<-sparse.model.matrix(formula, mf) }else{ mm<-model.matrix(formula, mf) } ww<-design$pweights if (is.null(lambda)){ sigma2<-rep(1,nrow(mm)) }else if(length(lambda) == nrow(mm)){ # for the heteroskedasticity parameter sigma2<-drop(lambda) }else{ sigma2<-drop(mm%*%lambda) # to keep the same functionality when variance = 1 } repwt<-as.matrix(design$repweights) if (!design$combined.weights) repwt<-repwt*design$pweights if (inherits(aggregate.index,"formula")){ if (length(aggregate.index)!=2) stop("aggregate.index must be a one-sided formula") aggregate.index<-model.frame(aggregate.index, design$variables) if (NCOL(aggregate.index)>1) stop("aggregate.index must specify a single variable") aggregate.index<-aggregate.index[[1]] } if (!is.null(aggregate.index)){ if (sqrt(max(ave(ww,aggregate.index,FUN=var),na.rm=TRUE))>1e-2*mean(ww)) warning("Sampling weights are not constant within clusters defined by aggregate.index") mm<-apply(mm,2,function(mx) ave(mx,aggregate.index)) ww<-ave(ww,aggregate.index) sigma2<-ave(sigma2,aggregate.index) repwt<-apply(repwt,2,function(wx) ave(wx, aggregate.index)) } whalf<-sqrt(ww) sample.total<-colSums(mm*ww) if(any(sample.total==0)){ ## drop columsn where all sample and population are zero zz<-(population==0) & (apply(mm,2,function(x) all(x==0))) mm<-mm[,!zz] population<-population[!zz] sample.total<-sample.total[!zz] } if(length(sample.total)!=length(population)){ stop("Population and sample totals are not the same length.") } if (!is.null(names(population)) && any(names(sample.total)!=names(population))){ warning("Sampling and population totals have different names.") cat("Sample: "); print(colnames(mm)) cat("Popltn: "); print(names(population[[1]])) } Tmat<-crossprod(mm*whalf/sqrt(sigma2)) tT<-solve(Tmat,population-sample.total) gtotal<-drop(1+mm%*%tT/sigma2) design$pweights<-design$pweights*gtotal for(i in 1:NCOL(repwt)){ whalf<-sqrt(repwt[,i]) Tmat<-crossprod(mm*whalf/sqrt(sigma2)) sample.total<-colSums(mm*whalf*whalf) g<-drop(1+mm%*%solve(Tmat,population-sample.total)/sigma2) repwt[,i]<-as.vector(design$repweights[,i])*g } if (!design$combined.weights) repwt<-repwt/gtotal if (compress || (is.na(compress && inherits(design$repweights,"repweights_compressed")))){ repwt<-compressWeights(repwt) } design$repweights<-repwt design$call<-sys.call(-1) design$degf<-NULL design$degf<-degf(design) design } survey/R/svyttest.R0000644000176200001440000001103414526736254014055 0ustar liggesuserssvyttest<-function(formula, design,...) UseMethod("svyttest",design) svyttest.default<-function(formula, design, ...){ if (formula[[3]]==1 || formula[[3]]==0){ ## one-sample tt <- eval(bquote(svymean(~as.numeric(.(formula[[2]])),design,...))) rval<-list(statistic=coef(tt)[1]/SE(tt)[1], parameter=degf(design)-1, estimate=coef(tt)[1], null.value=0, alternative="two.sided", method="Design-based one-sample t-test", data.name=deparse(formula)) rval$p.value<-2*pt(-abs(rval$statistic),df=rval$parameter) ci <- confint(tt,level=0.95,df=rval$parameter) attr(ci,"conf.level")<-0.95 rval$conf.int<-ci names(rval$statistic)<-"t" names(rval$parameter)<-"df" names(rval$estimate)<-"mean" names(rval$null.value)<-"mean" class(rval)<-c("svyttest","htest") } else { ## two-sample m <- eval(bquote(svyglm(formula,design, family=gaussian()))) mm<-model.matrix(m) if( (ncol(mm)!=2) || (any(!(mm[,2] %in% c(0,1))) && any(!(mm[,2] %in% c(1,2))))) stop("group must be binary") rval<-list(statistic=coef(m)[2]/SE(m)[2], parameter=m$df.resid, estimate=coef(m)[2], null.value=0, alternative="two.sided", method="Design-based t-test", data.name=deparse(formula)) rval$p.value<-2*pt(-abs(rval$statistic),df=rval$parameter) ci <- confint(m,parm=2) attr(ci,"conf.level")<-0.95 rval$conf.int<-ci names(rval$statistic)<-"t" names(rval$parameter)<-"df" names(rval$estimate)<-"difference in mean" names(rval$null.value)<-"difference in mean" class(rval)<-c("svyttest","htest") } return(rval) } confint.svyttest<-function(object, parm, level=0.95,...){ if(level==0.95) return(object$conf.int) halfw<-diff(as.vector(object$conf.int))/2 q95<-qt(0.975,df=object$parameter) qthis<-qt(1-(1-level)/2, df=object$parameter) thishalfw<-halfw*qthis/q95 rval<-object$estimate+c(-thishalfw,thishalfw) attr(rval,"conf.level")<-level rval } expit<-function(eta) exp(eta)/(1+exp(eta)) svyciprop<-function(formula, design, method=c("logit","likelihood","asin","beta","mean","xlogit"), level=0.95,df=degf(design),...) { method<-match.arg(method) if (method=="mean"){ m<-eval(bquote(svymean(~as.numeric(.(formula[[2]])),design,...))) ci<-as.vector(confint(m,1,level=level,df=df,...)) rval<-coef(m)[1] attr(rval,"var")<-vcov(m) } else if (method=="asin"){ m<-eval(bquote(svymean(~as.numeric(.(formula[[2]])),design,...))) names(m)<-1 xform<-svycontrast(m,quote(asin(sqrt(`1`)))) ci<-sin(as.vector(confint(xform,1,level=level,df=df,...)))^2 rval<-coef(m)[1] attr(rval,"var")<-vcov(m) } else if (method=="xlogit"){ m<-eval(bquote(svymean(~as.numeric(.(formula[[2]])),design,...))) names(m)<-1 xform<-svycontrast(m,quote(log(`1`/(1-`1`)))) ci<-expit(as.vector(confint(xform,1,level=level,df=df,...))) rval<-coef(m)[1] attr(rval,"var")<-vcov(m) } else if (method=="beta"){ m<-eval(bquote(svymean(~as.numeric(.(formula[[2]])),design,...))) n.eff <- coef(m)*(1-coef(m))/vcov(m) rval<-coef(m)[1] attr(rval,"var")<-vcov(m) alpha<-1-level n.eff<-n.eff*( qt(alpha/2, nrow(design)-1)/qt(alpha/2, degf(design)) )^2 ci<-c(qbeta(alpha/2, n.eff*rval,n.eff*(1-rval)+1), qbeta(1-alpha/2, n.eff*rval+1, n.eff*(1-rval))) } else { m<-eval(bquote(svyglm(.(formula[[2]])~1,design, family=quasibinomial))) cimethod<-switch(method, logit="Wald",likelihood="likelihood") ci<-suppressMessages(as.numeric(expit(confint(m,1,level=level,method=cimethod,ddf=df)))) rval<-expit(coef(m))[1] attr(rval,"var")<-vcov(eval(bquote(svymean(~as.numeric(.(formula[[2]])),design,...)))) } halfalpha<-(1-level)/2 names(ci)<-paste(round(c(halfalpha,(1-halfalpha))*100,1),"%",sep="") names(rval)<-paste(deparse(formula[[2]]), collapse="") attr(rval,"ci")<-ci class(rval)<-"svyciprop" rval } confint.svyciprop<-function(object,parm,level=NULL,...){ if (!is.null(level)) stop("need to re-run svyciprop to specify level") rval<-t(as.matrix(attr(object,"ci"))) rownames(rval)<-names(object) rval } coef.svyciprop<-function(object,...) object vcov.svyciprop<-function(object,...) attr(object,"var") print.svyciprop<-function(x,digits=max(3,getOption("digits")-4),...){ m <- cbind(coef(x), confint(x)) printCoefmat(m,digits=digits, cs.ind=1:3,tst.ind=NULL) invisible(x) } survey/R/svycralpha.R0000644000176200001440000000057313517345031014316 0ustar liggesusers svycralpha<-function(formula, design, na.rm=FALSE){ scoredef<-formula scoredef[[1]]<-quote(I) design<-eval(bquote(update(design, `*alpha*`= .(scoredef)))) vtotal<-coef(svyvar(~`*alpha*`,design,na.rm=na.rm)) vitems <-diag(coef(svyvar(formula, design,na.rm=na.rm))) K<-length(attr(terms(formula),"term.labels")) (K/(K-1))*(1-sum(vitems)/vtotal) } survey/R/logrank.R0000644000176200001440000001647714424406073013613 0ustar liggesuserssvylogrank<-function(formula, design,rho=0,gamma=0,method=c("small","large","score"),...){ UseMethod("svylogrank",design) } print.svylogrank<-function(x,...){ m<-t(x) rownames(m)="" printCoefmat(m,has.Pvalue=TRUE,P.values=TRUE) invisible(NULL) } .logrank<-function(formula, design,rho=0,gamma=0){ nullformula<-update(formula,.~1) S<-svykm(nullformula,design,se=FALSE) epsilon<-min(diff(sort(unique(S$time))))/10 w<-approxfun(S$time+epsilon,S$surv^rho*(1-S$surv)^gamma,method="constant",rule=2) environment(formula)<-environment() coxmodel<-coxph(formula,data=model.frame(design), weights=weights(design,"sampling"),iter.max=0) x<-model.matrix(coxmodel) detail<-coxph.detail(coxmodel,riskmat=TRUE) dLambda<-detail$hazard E<-as.matrix(detail$means) N<-coxmodel$y[,"status"] times<-coxmodel$y[,"time"] Y<-t(outer(times, detail$time,">=")) # was t(detail$riskmat) but that doesn't do what is documented U<-matrix(nrow=nrow(x),ncol=ncol(x)) index<-match(times[N==1],detail$time) ZmEdN<- matrix(0,nrow=nrow(x),ncol=ncol(x)) ZmEdN[N==1,]<-x[N==1,,drop=FALSE]-E[index,] for(p in 1:ncol(x)){ ZmE <- outer(x[,p],E[,p], "-") ##people are rows, times are columns U[,p]<- ZmEdN[,p]*w(times)- rowSums(ZmE *t(w(detail$time)*dLambda*Y)) } means <- svytotal(U,design) zstat<-coef(means)/SE(means) chisqstat<-coef(means)%*%solve(vcov(means),coef(means)) rval<-list(cbind(score=coef(means),se=SE(means),z=coef(means)/SE(means),p= 2*pnorm(-abs(coef(means)/SE(means)))), c(Chisq=chisqstat,p=pchisq(chisqstat,df=ncol(x),lower.tail=FALSE))) class(rval)<-"svylogrank" rval } .biglogrank<-function(formula, design,rho=0,gamma=0){ nullformula<-update(formula,.~1) S<-svykm(nullformula,design,se=FALSE) epsilon<-min(diff(sort(unique(S$time))))/10 w<-approxfun(S$time+epsilon,S$surv^rho*(1-S$surv)^gamma,method="constant",rule=2) environment(formula)<-environment() coxmodel<-coxph(formula,data=model.frame(design), weights=weights(design,"sampling"),iter.max=0) x<-model.matrix(coxmodel) detail<-coxph.detail(coxmodel) dLambda<-detail$hazard E<-as.matrix(detail$means) N<-coxmodel$y[,"status"] times<-coxmodel$y[,"time"] U<-matrix(nrow=nrow(x),ncol=ncol(x)) index<-match(times[N==1],detail$time) ZmEdN<- matrix(0,nrow=nrow(x),ncol=ncol(x)) ZmEdN[N==1,]<-x[N==1,,drop=FALSE]-E[index,] for(p in 1:ncol(x)){ U[,p]<- ZmEdN[,p]*w(times) for (j in seq_along(detail$time)){ thistime<-detail$time[j] ZmE <- x[,p]-E[j,p] U[,p] <- U[,p] - w(thistime)*ZmE*dLambda[j]*(times>=thistime) } } means <- svytotal(U,design) zstat<-coef(means)/SE(means) chisqstat<-coef(means)%*%solve(vcov(means),coef(means)) rval<-list(data.frame(score=coef(means),se=SE(means),z=coef(means)/SE(means),p= 2*pnorm(-abs(coef(means)/SE(means)))), c(Chisq=chisqstat,p=pchisq(chisqstat,df=ncol(x),lower.tail=FALSE))) class(rval)<-"svylogrank" rval } svylogrank.survey.design2<-function(formula, design,rho=0,gamma=0, method=c("small","large","score"), ...){ method<-match.arg(method) if (method=="small") return(.logrank(formula,design, rho,gamma,...)) else if (method=="large") return(.biglogrank(formula,design,rho,gamma,...)) if (rho!=0 || gamma!=0){ return(expandlogrank(formula,design,rho,gamma,...)) } tms<-delete.response(terms(formula,specials="strata")) findstrat<-untangle.specials(tms,"strata") if(length(findstrat$terms)) tms<-tms[-findstrat$terms] mf<-model.frame(tms,model.frame(design)) if(length(mf)>1) stop("Only one grouping variable allowed") if(!is.factor(mf[[1]]) && length(unique(mf[[1]]))>2) stop("Grouping variable with more than 2 levels must be a factor") b<-coef(svycoxph(formula,design,iter=1)) v<-vcov(svycoxph(formula,design,iter=0)) x2<-sum(b*solve(v,b)) rval<-c(z=b/sqrt(diag(v)), Chisq=x2, p=pchisq(x2,length(b),lower.tail=FALSE)) class(rval)<-"svylogrank" rval } svylogrank.twophase<-svylogrank.survey.design2 svylogrank.twophase2<-svylogrank.survey.design2 svylogrank.DBIsvydesign<-function (formula, design, ...) { design$variables <- dropFactor(getvars(formula, design$db$connection, design$db$tablename, updates = design$updates, subset = design$subset), weights(design)) NextMethod("svylogrank", design) } svylogrank.svyrep.design<-function(formula, design,rho=0,gamma=0,method=c("small","large","score"), ...){ method<-match.arg(method) if (method=="small") return(.logrank(formula,design, rho,gamma,...)) else if (method=="large") return(.biglogrank(formula,design,rho,gamma,...)) if (rho!=0 || gamma!=0){ return(expandlogrank(formula,design,rho,gamma,...)) } tms<-delete.response(terms(formula,specials="strata")) findstrat<-untangle.specials(tms,"strata") if(length(findstrat$terms)) tms<-tms[-findstrat$terms] mf<-model.frame(tms,model.frame(design)) if(length(mf)>1) stop("Only one grouping variable allowed") if(!is.factor(mf[[1]]) && length(unique(mf[[1]]))>2) stop("Grouping variable with more than 2 levels must be a factor") rr<-withReplicates(design, function(w,df){ environment(formula)<-environment() coef(coxph(formula,data=df,weights=w+1e-8,iter=1)) }) b<-unclass(rr) attr(b,"var")<-NULL v<-attr(rr,"var") x2<-sum(b*solve(v,b)) rval<- c(z=b/sqrt(diag(as.matrix(v))), Chisq=x2, p=pchisq(x2,length(b),lower.tail=FALSE)) class(rval)<-"svylogrank" rval } expandlogrank<-function(formula, design, rho=0, gamma=0){ nullformula<-update(formula,.~1) S<-svykm(nullformula,design,se=FALSE) epsilon<-min(diff(sort(unique(S$time))))/10 w<-approxfun(S$time+epsilon,S$surv^rho*(1-S$surv)^gamma,method="constant",rule=2) environment(formula)<-environment() coxmodel<-coxph(formula,data=model.frame(design), weights=weights(design,"sampling"),iter.max=0) mf<-model.frame(design) detail<-coxph.detail(coxmodel) if(attr(coxmodel$y,"type")=="right"){ mf$.time<-coxmodel$y[,"time"] mf$.status<-coxmodel$y[,"status"] mfsplit <- survSplit(mf, cut=detail$time, end=".time", event=".status", start=".start", id=".id", episode=".episode") } else { mf$.start<-coxmodel$y[,"start"] mf$.time<-coxmodel$y[,"stop"] mf$.status<-coxmodel$y[,"status"] mfsplit <- survSplit(mf, cut=detail$time, end=".time", event=".status", start=".start", id=".id", episode=".episode") } formula[[2]]<-quote(Surv(.start,.time,.status)) mfsplit$.weights<-weights(design,"sampling")[match(mfsplit$.id, rownames(mf))]*w(mfsplit$.time) expdesign<-svydesign(ids=eval(design$call$id), strata=eval(design$call$strata), data=mfsplit, weights=~.weights) #svylogrank(formula,expdesign) tms<-delete.response(terms(formula,specials="strata")) findstrat<-untangle.specials(tms,"strata") if(length(findstrat$terms)) tms<-tms[-findstrat$terms] mf<-model.frame(tms,model.frame(expdesign)) if(length(mf)>1) stop("Only one grouping variable allowed") if(!is.factor(mf[[1]]) && length(unique(mf[[1]]))>2) stop("Grouping variable with more than 2 levels must be a factor") b<-coef(svycoxph(formula,expdesign,iter=1)) v<-vcov(svycoxph(formula,expdesign,iter=0)) x2<-sum(b*solve(v,b)) rval<-c(z=b/sqrt(diag(v)), Chisq=x2, p=pchisq(x2,length(b),lower.tail=FALSE)) class(rval)<-"svylogrank" rval } survey/R/DBI.R0000644000176200001440000003133514556563037012554 0ustar liggesusers svydesign.character<-function (ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL,pps=FALSE, calibrate.formula=NULL, dbtype="SQLite", dbname, ...) { if (dbtype == "ODBC"){ message("RODBC support has been removed. Use the 'odbc' package") } else { db<-DBI::dbDriver(dbtype) dbconn<- DBI::dbConnect(db, dbname,...) } design.vars<-c(all.vars(ids), all.vars(probs), all.vars(strata), all.vars(fpc), all.vars(weights)) ## Need at least one variable to find out the data size if (length(design.vars)==0) design.vars<-"1 as _ugly_workaround_" design.query<-paste("select", paste(design.vars,collapse=","), "from", data) design.data<-DBI::dbGetQuery(dbconn, design.query) rval<-svydesign(ids=ids, probs=probs, strata=strata, data=design.data, fpc=fpc, variables=variables, nest=nest,check.strata=check.strata, weights=weights, calibrate.formula=calibrate.formula) rval$db<-list(dbname=dbname, tablename=data, connection=dbconn, dbtype=dbtype) rval$variables<-NULL rval$call<-sys.call(-1) class(rval)<-c("DBIsvydesign",class(rval)) rval } print.DBIsvydesign<-function(x,...){ cat("DB-backed ") NextMethod() if (!checkConnection(x$db$connection, error=FALSE)) cat("\n") invisible(x) } summary.DBIsvydesign<-function(object,...){ class(object)<-c("summary.DBIsvydesign",class(object)) object } print.summary.DBIsvydesign<-function(x,...){ print.survey.design2(x,varnames=TRUE,design.summaries=TRUE,...) invisible(x) } close.DBIsvydesign<-function(con,...){ DBI::dbDisconnect(con$db$connection,...) invisible(con) } open.DBIsvydesign<-function(con,...){ db<-DBI::dbDriver(con$db$dbtype) con$db$connection<-DBI::dbConnect(db, dbname=con$db$dbname,...) con } svymean.DBIsvydesign<-function(x, design,...){ design$variables<-getvars(x, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svymean",design) } svytotal.DBIsvydesign<-function(x, design,na.rm=FALSE,...){ design$variables<-getvars(x, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svytotal",design) } svyquantile.DBIsvydesign<-function(x, design,quantiles,...){ design$variables<-getvars(x, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svyquantile",design) } oldsvyquantile.DBIsvydesign<-function(x, design,quantiles,...){ design$variables<-getvars(x, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svyquantile",design) } dropFactor<-function(mf, w){ if(!any(w==0)) return(mf) dropped<-w==0 for(i in 1:ncol(mf)) { if (is.factor(mf[[i]])){ fi<-mf[[i]] if (all(dropped[fi==levels(fi)[1]])){ tt<-table(fi[!dropped]) l<-min(which(tt>0)) levs<-levels(fi) mf[[i]]<-relevel(mf[[i]],ref=levs[l]) } } } mf } svyglm.DBIsvydesign<-function(formula, design,...){ design$variables<-dropFactor(getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset), weights(design)) NextMethod("svyglm",design) } svyplot.DBIsvydesign<-function(formula,design,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) design$variables[weights(design)==0,]<-NA NextMethod("svyplot",design) } svycoplot.DBIsvydesign<-function(formula,design, style=c("hexbin","transparent"), basecol="black",alpha=c(0,0.8),hexscale=c("relative","absolute"),...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename, updates=design$updates, subset=design$subset) design$variables[weights(design)==0,]<-NA NextMethod("svycoplot",design) } svyboxplot.DBIsvydesign<-function(formula,design, ...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) design$variables[weights(design)==0,]<-NA class(design)<-setdiff(class(design),"DBIsvydesign") svyboxplot(formula,design,...) } svycdf.DBIsvydesign<-function(formula,design, na.rm=TRUE, ...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svycdf",design) } svyolr.DBIsvydesign<-function(formula,design,...){ design$variables<-dropFactor(getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset), weights(design)) NextMethod("svyolr",design) } svycoxph.DBIsvydesign<-function(formula,design,...){ design$variables<-dropFactor(getvars(formula, design$db$connection, design$db$tablename,updates=design$updates), weights(design)) NextMethod("svycoxph",design) } svyvar.DBIsvydesign<-function(x,design,na.rm=FALSE,...){ design$variables<-getvars(x, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svyvar",design) } svykm.DBIsvydesign<-function(formula,design,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svykm",design) } svykappa.DBIsvydesign<-function(formula,design,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svykappa",design) } svysmooth.DBIsvydesign<-function(formula,design,method=c("locpoly","quantreg"),bandwidth,quantile,df,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svysmooth",design) } svychisq.DBIsvydesign<-function(formula,design,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svychisq",design) } svyranktest.DBIsvydesign<-function(formula,design,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svyranktest",design) } svyratio.DBIsvydesign<-function(numerator, denominator, design,...){ design$variables<-cbind(getvars(numerator,design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset), getvars(denominator,design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset)) NextMethod("svyratio",design) } svyby.DBIsvydesign<-function(formula, by, design,...){ design$variables<-cbind(getvars(formula,design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset), getvars(by,design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset)) class(design)<-setdiff(class(design),"DBIsvydesign") svyby(formula,by,design,...) } svytable.DBIsvydesign<-function(formula,design,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("svytable",design) } calibrate.DBIsvydesign<-function(design,formula,...){ design$variables<-getvars(formula, design$db$connection, design$db$tablename,updates=design$updates, subset=design$subset) NextMethod("calibrate",design) } postStratify.DBIsvydesign<-function(design, strata, population, partial = FALSE, ...) .NotYetImplemented() subset.DBIsvydesign<-function (x, subset, ...) { e <- substitute(subset) x$variables<-getvars(make.formula(all.vars(e)), x$db$connection, x$db$tablename,updates=x$updates, subset=x$subset) r <- eval(e, x$variables, parent.frame()) r <- r & !is.na(r) x <- x[r, ] x$call <- sys.call(-1) x } dim.DBIsvydesign<-function(x){ w<-weights(x) nrow <- length(w) #need dim to report honest matrix size, so not: nrow<-sum(w!=0) coln<-names(DBI::dbGetQuery(x$db$conn, paste("select * from", x$db$tablename, "limit 1"))) if (!is.null(x$updates)){ update.names<-do.call(c, lapply(x$updates, names)) ncol<-length(unique(c(coln,update.names))) } else ncol<-length(coln) c(nrow,ncol) } dim.DBIrepdesign<-function(x){ if (is.null(x$subset)) nrow <-nrow(x$repweights) else nrow<-length(x$subset) coln<-names(DBI::dbGetQuery(x$db$conn, paste("select * from", x$db$tablename, "limit 1"))) if (!is.null(x$updates)){ update.names<-do.call(c, lapply(x$updates, names)) ncol<-length(unique(c(coln,update.names))) } else ncol<-length(coln) c(nrow,ncol) } dimnames.DBIsvydesign<-function(x){ rown<-rownames(x$cluster)[weights(x)!=0] coln<-names(DBI::dbGetQuery(x$db$conn, paste("select * from", x$db$tablename, "limit 1"))) if (!is.null(x$updates)){ update.names<-do.call(c, lapply(x$updates, names)) coln<-unique(c(coln,update.names)) } list(rown,coln) } dimnames.DBIrepdesign<-function(x){ if (is.null(x$subset)) rown<-rownames(x$cluster) else rown<-rownames(x$cluster)[x$subset] coln<-names(DBI::dbGetQuery(x$db$conn, paste("select * from", x$db$tablename, "limit 1"))) if (!is.null(x$updates)){ update.names<-do.call(c, lapply(x$updates, names)) coln<-unique(c(coln,update.names)) } list(rown,coln) } "[.DBIsvydesign"<-function (x, i, ..., drop = TRUE) { if (!missing(i)) { if (is.logical(i)) x$prob[!i] <- Inf else if (is.numeric(i) && length(i)) x$prob[-i] <- Inf else { tmp <- x$prob[i, ] x$prob <- rep(Inf, length(x$prob)) x$prob[i, ] <- tmp } index <- is.finite(x$prob) psu <- !duplicated(x$cluster[index, 1]) tt <- table(x$strata[index, 1][psu]) if (any(tt == 1)) { warning(sum(tt == 1), " strata have only one PSU in this subset.") } } else { if (!is.null(x$variables)) x$variables <- x$variables[, ..1, drop = FALSE] } x } "[.DBIrepdesign"<-function (x, i, j, drop = FALSE) { if (!missing(i)) { pwt <- x$pweights if (is.data.frame(pwt)) pwt <- pwt[[1]] x$pweights <- pwt[i] x$repweights <- x$repweights[i, , drop = FALSE] if (!is.null(x$selfrep)) x$selfrep <- x$selfrep[i] if (is.null(x$subset)) x$subset<-(1:nrow(x$variables))[i] else x$subset<-x$subset[i] if (!missing(j)) x$variables <- x$variables[i, j, drop = FALSE] else x$variables <- x$variables[i, , drop = FALSE] x$degf <- NULL x$degf <- degf(x) } else { x$variables <- x$variables[, j, drop = FALSE] } x } svrepdesign.character<-function (variables=NULL,repweights=NULL, weights=NULL, data=NULL, degf=NULL,type=c("BRR","Fay","JK1", "JKn","bootstrap","ACS","successive-difference","JK2","other"), combined.weights=TRUE, rho=NULL, bootstrap.average=NULL, scale=NULL,rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"), mse=getOption("survey.replicates.mse"),dbtype="SQLite", dbname, ...) { if (dbtype == "ODBC"){ stop("'RODBC' no longer supported. Use the odbc package") } else { db<-DBI::dbDriver(dbtype) dbconn<- DBI::dbConnect(db, dbname,...) } if (is.character(repweights)){ allvars<-names(DBI::dbGetQuery(dbconn, paste("select * from",data,"limit 1"))) design.vars<-c(all.vars(weights),grep(repweights,allvars,value=TRUE)) } else { design.vars<-c(all.vars(weights),all.vars(repweights)) } design.query<-paste("select", paste(design.vars,collapse=","), "from", data) design.data<-DBI::dbGetQuery(dbconn, design.query) rval<-svrepdesign(variables=variables,repweights=repweights, weights=weights, degf=NULL, type=type, data=design.data, combined.weights=combined.weights, rho=rho, bootstrap.average=NULL, scale=scale,rscales=rscales,fpc=fpc, fpctype=c("fraction","correction"), mse=mse) rval$db<-list(dbname=dbname, tablename=data, connection=dbconn, dbtype=dbtype) rval$variables<-NULL rval$call<-sys.call(-1) class(rval)<-c("DBIrepdesign","DBIsvydesign",class(rval)) rval } print.DBIrepdesign<-function(x,...){ cat("DB-backed replicate weight design\n") print.svyrep.design(x,...) if (!checkConnection(x$db$connection, error=FALSE)) cat("\n") invisible(x) } summary.DBIrepdesign<-function(object,...){ summary.svyrep.design(object,...) } survey/R/svysurvreg.R0000644000176200001440000000521713517345031014401 0ustar liggesuserssvysurvreg<-function (formula, design, weights=NULL, subset = NULL, ...) { UseMethod("svysurvreg", design) } residuals.svysurvreg<-function(object, type = c("response", "deviance", "dfbeta", "dfbetas", "working", "ldcase", "ldresp", "ldshape", "matrix"), rsigma = TRUE, collapse = FALSE, weighted = TRUE, ...) { NextMethod() } svysurvreg.survey.design<- function (formula, design, weights=NULL, subset=NULL, ...) { subset <- substitute(subset) subset <- eval(subset, model.frame(design), parent.frame()) if (!is.null(subset)) design <- design[subset, ] if (any(weights(design) < 0)) stop("weights must be non-negative") data <- model.frame(design) g <- match.call() g$formula <- eval.parent(g$formula) g$design <- NULL g$var <- NULL if (is.null(g$weights)) g$weights <- quote(.survey.prob.weights) else g$weights <- bquote(.survey.prob.weights * .(g$weights)) g[[1]] <- quote(survreg) g$data <- quote(data) g$subset <- quote(.survey.prob.weights > 0) g$model <- TRUE data$.survey.prob.weights <- (1/design$prob)/mean(1/design$prob) if (!all(all.vars(formula) %in% names(data))) stop("all variables must be in design= argument") g <- with(list(data = data), eval(g)) g$call <- match.call() g$call[[1]] <- as.name(.Generic) g$printcall <- sys.call(-1) g$printcall[[1]] <- as.name(.Generic) class(g) <- c("svysurvreg", class(g)) g$survey.design <- design nas <- g$na.action if (length(nas)) design <- design[-nas, ] dbeta.subset <- resid(g, "dfbeta", weighted = TRUE) if (nrow(design) == NROW(dbeta.subset)) { dbeta <- as.matrix(dbeta.subset) } else { dbeta <- matrix(0, ncol = NCOL(dbeta.subset), nrow = nrow(design)) dbeta[is.finite(design$prob), ] <- dbeta.subset } g$inv.info <- g$var if (inherits(design, "survey.design2")) g$var <- svyrecvar(dbeta, design$cluster, design$strata, design$fpc, postStrata = design$postStrata) else if (inherits(design, "twophase")) g$var <- twophasevar(dbeta, design) else if (inherits(design, "twophase2")) g$var <- twophase2var(dbeta, design) else if (inherits(design, "pps")) g$var <- ppsvar(dbeta, design) else g$var <- svyCprod(dbeta, design$strata, design$cluster[[1]], design$fpc, design$nPSU, design$certainty, design$postStrata) g$ll <- g$loglik g$loglik <- NA g$degf.resid <- degf(design) - length(coef(g)[!is.na(coef(g))]) + 1 g } survey/R/anova.svyglm.R0000644000176200001440000003076114207317361014572 0ustar liggesusers oneanova.svyglm<-function(object,test,method){ tt<-terms(object) tlbls<-attr(tt,"term.labels") nt<-length(tlbls) if (nt<2) return(NULL) seqtests<-vector("list",nt) if(test=="F") ddf<-NULL else ddf<-Inf thismodel<-object if (!("formula") %in% names(thismodel$call)) names(thismodel$call)[[2]] <- "formula" for(i in nt:1){ thisterm<-tlbls[i] seqtests[[i]]<-regTermTest(thismodel,tlbls[i],method=method,df=ddf) thisformula<-make.formula(thisterm)[[2]] thismodel <- eval(bquote(update(thismodel, . ~ . - (.(thisformula))))) } class(seqtests)<-"seqanova.svyglm" attr(seqtests,"method")<-method attr(seqtests,"test")<-test seqtests } oneanova.svycoxph<-function(object,test,method){ tt<-terms(object) tlbls<-attr(tt,"term.labels") nt<-length(tlbls) if (nt<2) return(NULL) seqtests<-vector("list",nt) if(test=="F") ddf<-NULL else ddf<-Inf thismodel<-object if (!("formula") %in% names(thismodel$call)) names(thismodel$call)[[2]] <- "formula" for(i in nt:1){ thisterm<-tlbls[i] seqtests[[i]]<-regTermTest(thismodel,tlbls[i],method=method,df=ddf) thisformula<-make.formula(thisterm)[[2]] thismodel <- eval(bquote(update(thismodel, . ~ . - (.(thisformula))))) } class(seqtests)<-"seqanova.svycoxph" attr(seqtests,"method")<-method attr(seqtests,"test")<-test seqtests } print.seqanova.svyglm<-function(x,...){ isWald<-attr(x,"method")=="Wald" isF<-attr(x,"test")=="F" cat("Anova table: ") if (isWald) cat("(Wald tests)\n") else cat(" (Rao-Scott LRT)\n") print(x[[1]]$mcall) terms<-sapply(x,"[[","test.terms") stats<-if(isF && isWald) sapply(x,"[[","Ftest") else sapply(x,"[[","chisq") if(!isWald) stats<-cbind(stats,DEff=sapply(x,function(xi) mean(xi$lambda))) df<-sapply(x,"[[","df") p<-sapply(x,"[[","p") if (!isF){ rval<-cbind(stats,df,p) } else { ddf<-sapply(x,"[[","ddf") rval<-cbind(stats,df,ddf,p) } rownames(rval)<-terms printCoefmat(rval,tst.ind=1,zap.ind=2:3,has.Pvalue=TRUE) invisible(x) } print.seqanova.svycoxph<-function(x,...){ isWald<-attr(x,"method")=="Wald" isF<-attr(x,"test")=="F" cat("Anova table: ") if (isWald) cat("(Wald tests)\n") else cat(" (Rao-Scott LRT)\n") print(x[[1]]$mcall) terms<-sapply(x,"[[","test.terms") stats<-if(isF && isWald) sapply(x,"[[","Ftest") else sapply(x,"[[","chisq") if(!isWald) stats<-cbind(stats,DEff=sapply(x,function(xi) mean(xi$lambda))) df<-sapply(x,"[[","df") p<-sapply(x,"[[","p") if (!isF){ rval<-cbind(stats,df,p) } else { ddf<-sapply(x,"[[","ddf") rval<-cbind(stats,df,ddf,p) } rownames(rval)<-terms printCoefmat(rval,tst.ind=1,zap.ind=2:3,has.Pvalue=TRUE) invisible(x) } SD<-function(x) if (NCOL(x)>1) apply(x,2,sd) else sd(x) anova.svyglm<-function(object, object2=NULL,test=c("F","Chisq"),method=c("LRT","Wald"),tolerance=1e-5,...,force=FALSE){ test<-match.arg(test) method<-match.arg(method) if(is.null(object2)) ## sequential tests return(oneanova.svyglm(object,test,method)) t1<-attr(terms(object),"term.labels") t2<-attr(terms(object2),"term.labels") if ((all(t1 %in% t2) || all(t2 %in% t1)) && !force){ ## symbolically nested, call regTermTest biggerobject<-if(all(t1 %in% t2)) object2 else object termdiff<-make.formula(if(all(t1 %in% t2)) setdiff(t2,t1) else setdiff(t1,t2)) if(test=="F") ddf<-NULL else ddf<-Inf return(regTermTest(biggerobject,termdiff,df=ddf,method=method)) } ## not symbolically nested, need to project explicitly X<-model.matrix(object) Z<-model.matrix(object2) if (nrow(X)!=nrow(Z)) stop("models have different numbers of observations") if (ncol(X)>ncol(Z)) { tmp<-X X<-Z Z<-tmp bigger<-1 } else bigger<-2 if (any(sapply(suppressWarnings(summary(lm(X~Z))), "[[","sigma")/(tolerance+SD(X))>tolerance)) stop("models not nested") XX<-matrix(nrow=nrow(Z),ncol=ncol(Z)) xform<-lm(Z[,1]~X+0) XX[,1]<-resid(xform) for(i in 2:ncol(Z)){ XX[,i]<-resid(xform<-lm(Z[,i]~X+Z[,1:(i-1)]+0)) } colkeep<-colMeans(abs(XX))/(tolerance+colMeans(abs(Z))) > tolerance XX<-XX[,colkeep,drop=FALSE] index<-ncol(X)+(1:ncol(XX)) ## and now need to refit the model ## ugly, but svyglm demands that all variables are in the design argument. ## We do know the fitted values at convergence, so one iteration suffices. mu<-if(bigger==1) fitted(object) else fitted(object2) eta<-if(bigger==1) object$linear.predictors else object2$linear.predictors offset<-if(bigger==1) object$offset else object2$offset if (is.null(offset)) offset<-0 pweights<-weights(object$survey.design,"sampling") y<-object$y if (length(pweights)!=length(y)){ pweights<-pweights[pweights>0] if (length(pweights)!=length(y)) stop("number of observations does not match design") } pweights<-pweights/mean(pweights) ywork<-eta-offset+(y-mu)/object$family$mu.eta(eta) wwork<-((pweights * object$family$mu.eta(eta)^2)/object$family$variance(mu)) wlm<-lm.wfit(cbind(X,XX),ywork,wwork) p1<-1:wlm$rank Ainv<-chol2inv(wlm$qr$qr[p1,p1,drop=FALSE]) estfun<-cbind(X,XX)*wwork*((y-mu)/object$family$mu.eta(eta)) design<-object$survey.design if (inherits(design, "survey.design2")) V<-svyrecvar(estfun %*% Ainv, design$cluster, design$strata, design$fpc, postStrata = design$postStrata) else if (inherits(design, "twophase")) V<-twophasevar(estfun %*% Ainv, design) else if (inherits(design, "twophase2")) V<-twophase2var(estfun %*% Ainv, design) else if (inherits(design, "pps")) V<-ppsvar(estfun %*% Ainv, design) else if (inherits(design, "svyrep.design")){ V<-vcov(svymean(estfun%*% Ainv/pweights, design))*length(pweights)^2 } else stop("not implemented for this type of design") V<-V[index,index] df<-min(object$df.residual, object2$df.residual) if(method=="LRT"){ V0<-Ainv[index,index] chisq <- if(bigger==1) deviance(object2) - deviance(object) else deviance(object)-deviance(object2) misspec <- eigen(solve(V0) %*% V, only.values = TRUE)$values if (test=="Chisq") p <- pchisqsum(chisq, rep(1, length(misspec)), misspec, method = "sad", lower.tail = FALSE) else p <- pFsum(chisq, rep(1, length(misspec)), misspec, ddf = df, method = "sad", lower.tail = FALSE) rval <- list(call = sys.call(), chisq = chisq, df = length(index), p = p, lambda = misspec, ddf = df, mcall=if(bigger==1) object$call else object2$call, test.terms=if(bigger==1) c(setdiff(t1,t2),"-",setdiff(t2,t1)) else c(setdiff(t2,t1),"-",setdiff(t1,t2)) ) class(rval)<-"regTermTestLRT" } else { ## method=Wald beta<- wlm$coefficients[index] chisq<-crossprod(beta,solve(V,beta)) if (test=="Chisq"){ p<-pchisq(chisq,df=length(index),lower.tail=FALSE) } else { p<-pf(chisq/length(index),df1=length(index),df2=df,lower.tail=FALSE) } rval <- list(call = sys.call(), Ftest = chisq/length(index), df = length(index), p = p, ddf = df, mcall=if(bigger==1) object$call else object2$call, test.terms=if(bigger==1) c(setdiff(t1,t2),"-",setdiff(t2,t1)) else c(setdiff(t2,t1),"-",setdiff(t1,t2)) ) class(rval)<-"regTermTest" } rval } get_cox_strata<-function(object){ t<-untangle.specials(terms(object),"strata", order=1) if (length(t$vars)==0) return(NULL) m<-model.frame(object) strata(m[t$vars], shortlabel = TRUE) } anova.svycoxph<-function(object, object2=NULL,test=c("F","Chisq"),method=c("LRT","Wald"),tolerance=1e-5,...,force=FALSE){ test<-match.arg(test) method<-match.arg(method) if(is.null(object2)) ## sequential tests return(oneanova.svycoxph(object,test,method)) t1<-attr(terms(object),"term.labels") t2<-attr(terms(object2),"term.labels") if ((all(t1 %in% t2) || all(t2 %in% t1)) && !force){ ## symbolically nested, call regTermTest biggerobject<-if(all(t1 %in% t2)) object2 else object termdiff<-make.formula(if(all(t1 %in% t2)) setdiff(t2,t1) else setdiff(t1,t2)) if(test=="F") ddf<-NULL else ddf<-Inf return(regTermTest(biggerobject,termdiff,df=ddf,method=method)) } ## not symbolically nested, need to project explicitly X<-model.matrix(object) Z<-model.matrix(object2) if (nrow(X)!=nrow(Z)) stop("models have different numbers of observations") if (ncol(X)>ncol(Z)) { tmp<-X X<-Z Z<-tmp bigger<-1 } else bigger<-2 if (any(sapply(suppressWarnings(summary(lm(X~Z))), "[[","sigma")/(tolerance+SD(X))>tolerance)) stop("models not nested") XX<-matrix(nrow=nrow(Z),ncol=ncol(Z)) xform<-lm(Z[,1]~X) XX[,1]<-resid(xform) for(i in 2:ncol(Z)){ XX[,i]<-resid(xform<-lm(Z[,i]~X+Z[,1:(i-1)])) } colnames(XX)<-paste0("_X_",colnames(Z)) colkeep<-colMeans(abs(XX))/(tolerance+colMeans(abs(Z))) > tolerance XX<-XX[,colkeep,drop=FALSE] index<-ncol(X)+(1:ncol(XX)) ## and now need to refit the model ## ugly, but svycoxph demands that all variables are in the design argument. ### FIXME pweights<-weights(object$survey.design,"sampling") y<-object$y if (length(pweights)!=length(y)){ pweights<-pweights[pweights>0] if (length(pweights)!=length(y)) stop("number of observations does not match design") } pweights<-pweights/mean(pweights) beta<-if(bigger==1) coef(object2) else coef(object) coxstrat<-get_cox_strata(object) coxstrat2<-get_cox_strata(object2) offs<-if(bigger==1) object$offset else object2$offset if (length(offs)==0) offs<-NULL else stop("models with offset not yet implemented") if(!all(coxstrat==coxstrat2)) stop("Cox model strata must match") refit1<-coxph.fit(cbind(X,XX),y,coxstrat, offs, init=c(beta,rep(0,NCOL(XX))), control=coxph.control(),weights=pweights,method=object$method, rownames=names(resid(object)),resid=TRUE) refit<-if(bigger==1) object else object2 refit[names(refit1)]<-refit1 design<-object$survey.design Ainv<- if(is.null(refit$naive.var)) refit$var else refit$naive.var dbeta.subset <- resid(refit, "dfbeta", weighted = TRUE) if (nrow(design) == NROW(dbeta.subset)) { estfun <- as.matrix(dbeta.subset) } else { estfun <- matrix(0, ncol = NCOL(dbeta.subset), nrow = nrow(design)) estfun[is.finite(design$prob), ] <- dbeta.subset } if (inherits(design, "survey.design2")) V<-svyrecvar(estfun, design$cluster, design$strata, design$fpc, postStrata = design$postStrata) else if (inherits(design, "twophase")) V<-twophasevar(estfun, design) else if (inherits(design, "twophase2")) V<-twophase2var(estfun , design) else if (inherits(design, "pps")) V<-ppsvar(estfun , design) else if (inherits(design, "svyrep.design")){ V<-vcov(svymean(estfun/pweights, design))*length(pweights)^2 } else stop("not implemented for this type of design") V<-V[index,index] df<-min(object$degf.resid, object2$degf.resid) deviance.coxph<-function(object,...) -2*object$loglik[2] if(method=="LRT"){ V0<-Ainv[index,index] chisq <- if(bigger==1) deviance(object2) - deviance(object) else deviance(object)-deviance(object2) misspec <- eigen(solve(V0) %*% V, only.values = TRUE)$values if (test=="Chisq") p <- pchisqsum(chisq, rep(1, length(misspec)), misspec, method = "sad", lower.tail = FALSE) else p <- pFsum(chisq, rep(1, length(misspec)), misspec, ddf = df, method = "sad", lower.tail = FALSE) rval <- list(call = sys.call(), chisq = chisq, df = length(index), p = p, lambda = misspec, ddf = df, mcall=if(bigger==1) object$call else object2$call, test.terms=if(bigger==1) c(setdiff(t1,t2),"-",setdiff(t2,t1)) else c(setdiff(t2,t1),"-",setdiff(t1,t2)) ) class(rval)<-"regTermTestLRT" } else { ## method=Wald beta<- refit$coefficients[index] chisq<-crossprod(beta,solve(V,beta)) if (test=="Chisq"){ p<-pchisq(chisq,df=length(index),lower.tail=FALSE) } else { p<-pf(chisq/length(index),df1=length(index),df2=df,lower.tail=FALSE) } rval <- list(call = sys.call(), Ftest = chisq/length(index), df = length(index), p = p, ddf = df, mcall=if(bigger==1) object$call else object2$call, test.terms=if(bigger==1) c(setdiff(t1,t2),"-",setdiff(t2,t1)) else c(setdiff(t2,t1),"-",setdiff(t1,t2)) ) class(rval)<-"regTermTest" } rval } survey/R/sysdata.rda0000644000176200001440000000073614576431436014173 0ustar liggesusersBZh91AY&SY 3XP@6f@ lIFdEC@4AM& @ In(8R&iy"Hc fhdNWdbIu }!ÏBH&c*Bee*DC ieɐZCm=jq޳_oL;;R׾>o .+ŲJpZ! #+cwsDY^.Qˎ s2VAmncs,-hեq51cm[Q k0Z -᮲iUJ+hւpB, eCme54wrĥh ar1^)X-bVTR¥Vb*, -I`RlRŋ ,,C( V `V+'}d)NO˟*X=48G[Ww$S  ]survey/R/surveychisq.R0000755000176200001440000004137714556563036014554 0ustar liggesusers## ## Tests for contingency tables ## svychisq<-function(formula, design,...) UseMethod("svychisq",design) svychisq.survey.design<-function(formula, design, statistic=c("F","Chisq","Wald","adjWald","lincom","saddlepoint","wls-score"), na.rm=TRUE,...){ if (ncol(attr(terms(formula),"factors"))!=2) stop("Only 2-way tables at the moment") statistic<-match.arg(statistic) if (statistic=="wls-score") return(svychisqzero(formula,design,na.rm=na.rm)) rows<-formula[[2]][[2]] cols<-formula[[2]][[3]] rowvar<-unique(design$variables[,as.character(rows)]) colvar<-unique(design$variables[,as.character(cols)]) returnNA<-FALSE if ((any(is.na(rowvar),is.na(colvar)))){ rowvar<-na.omit(rowvar) colvar<-na.omit(colvar) returnNA<-!na.rm } nr<-length(rowvar) nc<-length(colvar) fsat<-eval(bquote(~interaction(factor(.(rows)),factor(.(cols)))-1)) mm<-model.matrix(fsat,model.frame(fsat, design$variables,na.action=na.pass)) N<-nrow(mm) nu <- length(unique(design$cluster[,1]))-length(unique(design$strata[,1])) pearson<- suppressWarnings(chisq.test(svytable(formula,design,Ntotal=N), correct=FALSE)) mf1<-expand.grid(rows=1:nr,cols=1:nc) X1<-model.matrix(~factor(rows)+factor(cols),mf1) X12<-model.matrix(~factor(rows)*factor(cols),mf1) if(statistic %in% c("Wald", "adjWald")){ frow<-eval(bquote(~factor(.(rows))-1)) fcol<-eval(bquote(~factor(.(cols))-1)) mr<-model.matrix(frow, model.frame(frow,design$variables, na.action=na.pass)) mc<-model.matrix(fcol, model.frame(fcol,design$variables, na.action=na.pass)) one<-rep(1,NROW(mc)) cells<-svytotal(~mm+mr+mc+one,design,na.rm=TRUE) Jcb <- cbind(diag(nr*nc), -outer(mf1$rows,1:nr,"==")*rep(cells[(nr*nc)+nr+1:nc]/cells[(nr*nc)+nr+nc+1],each=nr), -outer(mf1$cols,1:nc,"==")*cells[(nr*nc)+1:nr]/cells[(nr*nc)+nr+nc+1], as.vector(outer(cells[(nr*nc)+1:nr],cells[(nr*nc+nr)+1:nc])/cells[(nr*nc)+nr+nc+1]^2)) Y<-cells[1:(nc*nr)]-as.vector(outer(cells[(nr*nc)+1:nr],cells[(nr*nc+nr)+1:nc]))/cells[(nr*nc)+nr+nc+1] V<-Jcb%*%attr(cells,"var")%*%t(Jcb) use<-as.vector(matrix(1:(nr*nc),nrow=nr,ncol=nc)[-1,-1]) waldstat<-Y[use]%*%solve(V[use,use],Y[use]) if (statistic=="Wald"){ waldstat<-waldstat/((nc-1)*(nr-1)) numdf<-(nc-1)*(nr-1) denomdf<-nu } else { numdf<-(nr-1)*(nc-1) denomdf<-(nu-numdf+1) waldstat <- waldstat*denomdf/(numdf*nu) } if (returnNA){ pearson$statistic<-NA pearson$parameter<-c(ndf=numdf,ddf=denomdf) pearson$p.value<-NA attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based Wald test of association" } else { pearson$statistic<-waldstat pearson$parameter<-c(ndf=numdf,ddf=denomdf) pearson$p.value<-pf(pearson$statistic, numdf, denomdf, lower.tail=FALSE) attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based Wald test of association" } return(pearson) } mean2<-svymean(mm,design,na.rm=TRUE) Cmat<-qr.resid(qr(X1),X12[,-(1:(nr+nc-1)),drop=FALSE]) Dmat <- diag(mean2) iDmat<- diag(ifelse(mean2==0,0,1/mean2)) Vsrs <- (Dmat - outer(mean2,mean2))/N V <- attr(mean2,"var") denom<- t(Cmat) %*% (iDmat/N) %*% Cmat numr<-t(Cmat)%*% iDmat %*% V %*% iDmat %*% Cmat Delta<-solve(denom,numr) d0<- sum(diag(Delta))^2/(sum(diag(Delta%*%Delta))) warn<-options(warn=-1) ## turn off the small-cell count warning. pearson<- chisq.test(svytable(formula,design,Ntotal=N), correct=FALSE) options(warn) if (match.arg(statistic)=="F"){ pearson$statistic<-pearson$statistic/sum(diag(Delta)) pearson$p.value<-pf(pearson$statistic, d0, d0*nu, lower.tail=FALSE) attr(pearson$statistic,"names")<-"F" pearson$parameter<-c(ndf=d0,ddf=d0*nu) pearson$method<-"Pearson's X^2: Rao & Scott adjustment" } else if (match.arg(statistic)=="lincom") { pearson$p.value<-pFsum(pearson$statistic, rep(1,ncol(Delta)), eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="integration",ddf=d0*nu) pearson$parameter<-NULL pearson$method<-"Pearson's X^2: asymptotic exact distribution" } else if (match.arg(statistic)=="saddlepoint") { pearson$p.value<-pFsum(pearson$statistic, rep(1,ncol(Delta)), eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="saddlepoint",ddf=d0*nu) pearson$parameter<-NULL pearson$method<-"Pearson's X^2: saddlepoint approximation" } else{ pearson$p.value<-pchisq(pearson$statistic/mean(diag(Delta)), df=NCOL(Delta),lower.tail=FALSE) pearson$parameter<-c(df=NCOL(Delta)) pearson$method<-"Pearson's X^2: Rao & Scott adjustment" } if (returnNA){ pearson$statistic<-NA pearson$p.value<-NA } pearson$data.name<-deparse(sys.call(-1)) pearson } svychisq.twophase<-function(formula, design, statistic=c("F","Chisq","Wald","adjWald","lincom","saddlepoint"), na.rm=TRUE,...){ if (ncol(attr(terms(formula),"factors"))!=2) stop("Only 2-way tables at the moment") statistic<-match.arg(statistic) ##if(!is.null(design$postStrata)) ## warning("Post-stratification not implemented") rows<-formula[[2]][[2]] cols<-formula[[2]][[3]] dat<-design$phase1$sample$variables rowvar<-unique(dat[,as.character(rows)]) colvar<-unique(dat[,as.character(cols)]) returnNA<-FALSE if ((any(is.na(rowvar),is.na(colvar)))){ rowvar<-na.omit(rowvar) colvar<-na.omit(colvar) returnNA<-!na.rm } nr<-length(rowvar) nc<-length(colvar) fsat<-eval(bquote(~interaction(factor(.(rows)),factor(.(cols)))-1)) mm<-model.matrix(fsat,model.frame(fsat, dat,na.action=na.pass)) N<-nrow(mm) nu <- length(unique(design$phase2$cluster[,1]))-length(unique(design$phase2$strata[,1])) pearson<- suppressWarnings(chisq.test(svytable(formula,design,Ntotal=N), correct=FALSE)) mf1<-expand.grid(rows=1:nr,cols=1:nc) X1<-model.matrix(~factor(rows)+factor(cols),mf1) X12<-model.matrix(~factor(rows)*factor(cols),mf1) if(statistic %in% c("Wald", "adjWald")){ frow<-eval(bquote(~factor(.(rows))-1)) fcol<-eval(bquote(~factor(.(cols))-1)) mr<-model.matrix(frow, model.frame(frow,dat, na.action=na.pass)) mc<-model.matrix(fcol, model.frame(fcol,dat, na.action=na.pass)) one<-rep(1,NROW(mc)) cells<-svytotal(~mm+mr+mc+one,design,na.rm=TRUE) Jcb <- cbind(diag(nr*nc), -outer(mf1$rows,1:nr,"==")*rep(cells[(nr*nc)+nr+1:nc]/cells[(nr*nc)+nr+nc+1],each=nr), -outer(mf1$cols,1:nc,"==")*cells[(nr*nc)+1:nr]/cells[(nr*nc)+nr+nc+1], as.vector(outer(cells[(nr*nc)+1:nr],cells[(nr*nc+nr)+1:nc])/cells[(nr*nc)+nr+nc+1]^2)) Y<-cells[1:(nc*nr)]-as.vector(outer(cells[(nr*nc)+1:nr],cells[(nr*nc+nr)+1:nc]))/cells[(nr*nc)+nr+nc+1] V<-Jcb%*%attr(cells,"var")%*%t(Jcb) use<-as.vector(matrix(1:(nr*nc),nrow=nr,ncol=nc)[-1,-1]) waldstat<-Y[use]%*%solve(V[use,use],Y[use]) if (statistic=="Wald"){ waldstat<-waldstat/((nc-1)*(nr-1)) numdf<-(nc-1)*(nr-1) denomdf<-nu } else { numdf<-(nr-1)*(nc-1) denomdf<-(nu-numdf+1) waldstat <- waldstat*denomdf/(numdf*nu) } if (returnNA){ pearson$statistic<-NA pearson$parameter<-c(ndf=numdf,ddf=denomdf) pearson$p.value<-NA attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based Wald test of association" } else { pearson$statistic<-waldstat pearson$parameter<-c(ndf=numdf,ddf=denomdf) pearson$p.value<-pf(pearson$statistic, numdf, denomdf, lower.tail=FALSE) attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based Wald test of association" } return(pearson) } mean2<-svymean(mm,design,na.rm=TRUE) Cmat<-qr.resid(qr(X1),X12[,-(1:(nr+nc-1)),drop=FALSE]) Dmat <- diag(mean2) iDmat<- diag(ifelse(mean2==0,0,1/mean2)) Vsrs <- (Dmat - outer(mean2,mean2))/N V <- attr(mean2,"var") denom<- t(Cmat) %*% (iDmat/N) %*% Cmat numr<-t(Cmat)%*% iDmat %*% V %*% iDmat %*% Cmat Delta<-solve(denom,numr) d0<- sum(diag(Delta))^2/(sum(diag(Delta%*%Delta))) warn<-options(warn=-1) ## turn off the small-cell count warning. pearson<- chisq.test(svytable(formula,design,Ntotal=N), correct=FALSE) options(warn) if (match.arg(statistic)=="F"){ pearson$statistic<-pearson$statistic/sum(diag(Delta)) pearson$p.value<-pf(pearson$statistic, d0, d0*nu, lower.tail=FALSE) attr(pearson$statistic,"names")<-"F" pearson$parameter<-c(ndf=d0,ddf=d0*nu) pearson$method<-"Pearson's X^2: Rao & Scott adjustment" } else if (match.arg(statistic)=="lincom") { pearson$p.value<-pFsum(pearson$statistic, rep(1,ncol(Delta)), eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="integration",ddf=d0*nu) pearson$parameter<-NULL pearson$method<-"Pearson's X^2: asymptotic exact distribution" } else if (match.arg(statistic)=="saddlepoint") { pearson$p.value<-pFsum(pearson$statistic, rep(1,ncol(Delta)), eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="saddlepoint",ddf=d0*nu) pearson$parameter<-NULL pearson$method<-"Pearson's X^2: saddlepoint approximation" } else{ pearson$p.value<-pchisq(pearson$statistic/mean(diag(Delta)), df=NCOL(Delta),lower.tail=FALSE) pearson$parameter<-c(df=NCOL(Delta)) pearson$method<-"Pearson's X^2: Rao & Scott adjustment" } if (returnNA){ pearson$statistic<-NA pearson$p.value<-NA } pearson$data.name<-deparse(sys.call(-1)) pearson } svychisq.svyrep.design<-function(formula, design, statistic=c("F","Chisq","Wald","adjWald","lincom","saddlepoint","wls-score"), na.rm=TRUE,...){ if (ncol(attr(terms(formula),"factors"))!=2) stop("Only 2-way tables at the moment") statistic<-match.arg(statistic) if (statistic=="wls-score") return(svychisqzero(formula,design,na.rm=na.rm)) rows<-formula[[2]][[2]] cols<-formula[[2]][[3]] rowvar<-unique(design$variables[,as.character(rows)]) colvar<-unique(design$variables[,as.character(cols)]) returnNA<-FALSE if ((any(is.na(rowvar),is.na(colvar)))){ rowvar<-na.omit(rowvar) colvar<-na.omit(colvar) returnNA<-!na.rm } nr<-length(rowvar) nc<-length(colvar) fsat<-eval(bquote(~interaction(factor(.(rows)),factor(.(cols)))-1)) mm<-model.matrix(fsat,model.frame(fsat, design$variables,na.action=na.pass)) N<-nrow(mm) nu <- degf(design) pearson<- suppressWarnings(chisq.test(svytable(formula,design,Ntotal=N), correct=FALSE)) mf1<-expand.grid(rows=1:nr,cols=1:nc) X1<-model.matrix(~factor(rows)+factor(cols),mf1) X12<-model.matrix(~factor(rows)*factor(cols),mf1) if(statistic %in% c("Wald", "adjWald")){ frow<-eval(bquote(~factor(.(rows))-1)) fcol<-eval(bquote(~factor(.(cols))-1)) mr<-model.matrix(frow, model.frame(frow,design$variables, na.action=na.pass)) mc<-model.matrix(fcol, model.frame(fcol,design$variables, na.action=na.pass)) one<-rep(1,NROW(mc)) cells<-svytotal(~mm+mr+mc+one,design,na.rm=TRUE) Jcb <- cbind(diag(nr*nc), -outer(mf1$rows,1:nr,"==")*rep(cells[(nr*nc)+nr+1:nc]/cells[(nr*nc)+nr+nc+1],each=nr), -outer(mf1$cols,1:nc,"==")*cells[(nr*nc)+1:nr]/cells[(nr*nc)+nr+nc+1], as.vector(outer(cells[(nr*nc)+1:nr],cells[(nr*nc+nr)+1:nc])/cells[(nr*nc)+nr+nc+1]^2)) Y<-cells[1:(nc*nr)]-as.vector(outer(cells[(nr*nc)+1:nr],cells[(nr*nc+nr)+1:nc]))/cells[(nr*nc)+nr+nc+1] V<-Jcb%*%attr(cells,"var")%*%t(Jcb) use<-as.vector(matrix(1:(nr*nc),nrow=nr,ncol=nc)[-1,-1]) waldstat<-Y[use]%*%solve(V[use,use],Y[use]) if (statistic=="Wald"){ waldstat<-waldstat/((nc-1)*(nr-1)) numdf<-(nc-1)*(nr-1) denomdf<-nu } else { numdf<-(nr-1)*(nc-1) denomdf<-(nu-numdf+1) waldstat <- waldstat*denomdf/(numdf*nu) } if (returnNA){ pearson$statistic<-NA pearson$parameter<-c(ndf=numdf,ddf=denomdf) pearson$p.value<-NA attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based Wald test of association" } else { pearson$statistic<-waldstat pearson$parameter<-c(ndf=numdf,ddf=denomdf) pearson$p.value<-pf(pearson$statistic, numdf, denomdf, lower.tail=FALSE) attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based Wald test of association" } return(pearson) } mean2<-svymean(mm,design,na.rm=TRUE) Cmat<-qr.resid(qr(X1),X12[,-(1:(nr+nc-1)),drop=FALSE]) Dmat <- diag(mean2) iDmat<- diag(ifelse(mean2==0,0,1/mean2)) Vsrs <- (Dmat - outer(mean2,mean2))/N V <- attr(mean2,"var") denom<- t(Cmat) %*% (iDmat/N) %*% Cmat numr<-t(Cmat)%*% iDmat %*% V %*% iDmat %*% Cmat Delta<-solve(denom,numr) d0<- sum(diag(Delta))^2/(sum(diag(Delta%*%Delta))) warn<-options(warn=-1) ## turn off the small-cell count warning. pearson<- chisq.test(svytable(formula,design,Ntotal=N), correct=FALSE) options(warn) if (match.arg(statistic)=="F"){ pearson$statistic<-pearson$statistic/sum(diag(Delta)) pearson$p.value<-pf(pearson$statistic, d0, d0*nu, lower.tail=FALSE) attr(pearson$statistic,"names")<-"F" pearson$parameter<-c(ndf=d0,ddf=d0*nu) } else if (match.arg(statistic)=="lincom") { pearson$p.value<-pchisqsum(pearson$statistic, rep(1,ncol(Delta)), eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="integration") pearson$parameter<-NULL pearson$method<-"Pearson's X^2: asymptotic exact distribution" }else if (match.arg(statistic)=="saddlepoint") { pearson$p.value<-pchisqsum(pearson$statistic, rep(1,ncol(Delta)), eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="saddlepoint") pearson$parameter<-NULL pearson$method<-"Pearson's X^2: saddlepoint approximation" } else { pearson$p.value<-pchisq(pearson$statistic/mean(diag(Delta)), df=NCOL(Delta),lower.tail=FALSE) pearson$parameter<-c(df=NCOL(Delta)) } if (returnNA){ pearson$statistic<-NA pearson$p.value<-NA } pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Pearson's X^2: Rao & Scott adjustment" pearson } summary.svreptable<-function(object,...){ object } summary.svytable<-function(object, statistic=c("F","Chisq","Wald","adjWald","lincom","saddlepoint"),...){ statistic<-match.arg(statistic) call<-attr(object, "call") ff<-call$formula if (is.null(environment(ff))) env<-parent.frame() else env<-environment(ff) ff<-delete.response(ff) test<-eval(bquote(svychisq(.(ff), design=.(call$design), statistic=.(statistic))), env) rval<-list(table=object,statistic=test) class(rval)<-"summary.svytable" rval } print.summary.svytable<-function(x,digits=0,...){ print(round(x$table,digits)) print(x$statistic,...) } svychisqzero<-function(formula,design,na.rm){ if (ncol(attr(terms(formula), "factors")) > 2) stop("Only 2-way tables") rows <- formula[[2]][[2]] cols <- formula[[2]][[3]] rowvar <- unique(design$variables[, as.character(rows)]) nr <- length(rowvar) des<-design[rep(1:nrow(design), nr), ] yind<-as.vector(eval(bquote( model.matrix(~factor(.(rows))+0, model.frame(design))))) k<-rep(1:nr,each=nrow(design)) des<-update(des,yind=yind,k=k) ff0<-eval(bquote(yind~factor(k)+factor(.(cols)))) ff1<-eval(bquote(~factor(k):factor(.(cols)))) m0<-svyglm(ff0, des) test<-svyscoretest(m0, add.terms=ff1,method="pseudo") ## rescaling: the weights matter because score test uses svytotal test["X2"]<-test["X2"]/nr/nr if (is.finite(test["ddf"])) { p<-pf(test["X2"], test["df"],test["ddf"],lower.tail=FALSE) } else{ p<- pchisq(test["X2"], test["df"],lower.tail=FALSE) } test["p"]<-p ## formatting warn<-options(warn=-1) ## turn off the small-cell count warning. pearson<- chisq.test(svytable(formula,design,Ntotal=nrow(design)), correct=FALSE) options(warn) pearson$statistic<-test["X2"] pearson$parameter<-c(ndf=test["df"],ddf=test["ddf"]) pearson$p.value<-test["p"] attr(pearson$statistic,"names")<-"F" pearson$data.name<-deparse(sys.call(-1)) pearson$method<-"Design-based WLS score test of association" pearson } survey/R/twophase.R0000644000176200001440000006123014565003231013766 0ustar liggesusers## ## twophase<-function(id,strata=NULL, probs=NULL, weights=NULL, fpc=NULL, subset, data, method=c("full","approx","simple"), pps=NULL){ data<-detibble(data) method<-match.arg(method) if(method=="full") { if (!is.null(weights)) stop("weights not accepted by method='full'") return(twophase2(id=id, strata=strata, probs=probs, fpc=fpc,subset=subset,data=data,pps=pps)) } d1<-svydesign(ids=id[[1]],strata=strata[[1]],weights=weights[[1]], probs=probs[[1]],fpc=fpc[[1]],data=data) if(inherits(subset,"formula")) subset<-eval.parent(model.frame(subset,data=data,na.action=na.pass))[[1]] if(!is.logical(subset) && sort(unique(subset))==c(0,1)) subset<-as.logical(subset) if (any(is.na(subset))) stop("missing values in 'subset'") d1s<-svydesign(ids=id[[1]],strata=strata[[1]],weights=weights[[1]], probs=probs[[1]],fpc=fpc[[1]],data=data[subset,]) d1s$prob<-d1$prob[subset] d1s$allprob<-d1$allprob[subset,,drop=FALSE] ##if (NCOL(d1s$allprob)>1) ## stop("Can't handle multistage sampling at phase 1 (yet)") ## work out phase-two fpc if (is.null(fpc[[2]])){ complete.vars<-names(data)[apply(data, 2, function(v) all(!is.na(v)))] if (all(c(all.vars(id[[2]]), all.vars(strata[[2]])) %in% complete.vars)){ dfpc<-svydesign(ids=id[[2]], strata=strata[[2]], data=data, probs=NULL) popsize<-mapply(function(s,i) ave(!duplicated(i),s,FUN=sum), dfpc$strata, dfpc$cluster) rm(dfpc) } else { warning("Second-stage fpc not specified and not computable") popsize<-NULL } } else popsize<-NULL d2<-svydesign(ids=id[[2]], strata=strata[[2]], probs=probs[[2]], weights=weights[[2]], fpc=fpc[[2]], data=data[subset,]) ## ugly hack to get nicer labels if(!is.null(fpc[[2]])){ d2call<-bquote(svydesign(ids=.(id[[2]]),strata=.(strata[[2]]), probs=.(probs[[2]]), weights=.(weights[[2]]), fpc=.(fpc[[2]]))) } else{ d2call<-bquote(svydesign(ids=.(id[[2]]),strata=.(strata[[2]]), probs=.(probs[[2]]), weights=.(weights[[2]]), fpc=`*phase1*`)) } for(i in names(d2call)[-1]) d2call[[i]]<-d2call[[i]] d2$call<-d2call d1call<-bquote(svydesign(ids=.(id[[1]]), strata=.(strata[[1]]), probs=.(probs[[1]]), weights=.(weights[[1]]), fpc=.(fpc[[1]]))) for(i in names(d1call)[-1]) d1call[[i]]<-d1call[[i]] d1$call<-d1call ## Add phase 2 fpc and probs if they were computed rather than specified. if (!is.null(popsize)) d2$fpc<-as.fpc(popsize[subset,,drop=FALSE],d2$strata,d2$cluster) if(is.null(probs[[2]]) && is.null(weights[[2]]) && !is.null(d2$fpc$popsize)){ d2$allprob<-1/weights(d2$fpc,final=FALSE) d2$prob<-apply(as.data.frame(d2$allprob),1,prod) } d2$variables<-NULL rval<-list(phase1=list(full=d1,sample=d1s), phase2=d2, subset=subset) rval$prob<-rval$phase1$sample$prob ## Are phase 2 PSUs the same as Phase 1 USUs, or smaller? rval$samescale<- !any(duplicated(d1s$cluster[,NCOL(d1s$cluster)][!duplicated(d2$cluster[,1])])) ## For each phase 1 sampling unit, need probability of being represented ## at phase 2. nunique<-function(x) sum(!duplicated(x)) m<-NCOL(rval$phase1$sample$cluster) if(d2$has.strata){ if (inherits(strata[[2]],"formula")) sa<-eval(attr(terms(strata[[2]]),"variables")[[2]],d1$variables) else sa<-d1$strata[,1] cm<-rval$phase1$full$cluster[,m] if (nunique(sa)!=nunique(sa[subset])) stop("Some phase-2 strata have zero sampling fraction") rval$usu<-ave(as.numeric(cm[subset]),sa[subset],FUN=nunique)/ave(as.numeric(cm),sa,FUN=nunique)[subset] } else { rval$usu<-drop(with(rval$phase1$sample,ave(as.numeric(cluster[,m]), strata[,m], FUN=nunique))/rval$phase1$full$fpc$sampsize[rval$subset,m]) } ## if (any(rval$usu<1) && any(duplicated(d1$cluster[,1]))) ## stop("Phase 1 design must either be element sampling or have all phase 1 sampling units in phase 2") if (length(rval$phase1$sample$prob)==length(d2$prob)) rval$prob<-rval$phase1$sample$prob*d2$prob else{ rval$prob<-rep(Inf,length(rval$phase1$sample$prob)) rval$prob[subset]<-rval$prob[subset]*d2$prob } rval$call<-sys.call() class(rval) <- c("twophase","survey.design") rval } print.twophase<-function(x,...){ cat("Two-phase design: ") print(x$call) cat("Phase 1:\n") print(x$phase1$full) cat("Phase 2:\n") print(x$phase2) invisible(x) } summary.twophase<-function(object,...){ class(object)<-"summary.twophase" object } print.summary.twophase<-function(x,...,varnames=TRUE){ cat("Two-phase design: ") print(x$call) cat("Phase 1:\n") print(x$phase1$full,design.summaries=TRUE,varnames=FALSE) cat("Phase 2:\n") print(x$phase2,design.summaries=TRUE, varnames=FALSE) if (varnames){ cat("Data variables:\n") print(names(x$phase1$full$variables)) } invisible(x) } twophasevar<-function(x,design){ d1 <- design$phase1$sample if (NROW(x)==length(design$usu)){ ph2pr<-design$usu if (any(design$prob==Inf)) x[is.na(x)]<-0 }else{ x[is.na(x)]<-0 ph2pr<-rep(1,NROW(x)) ph2pr[design$subset]<-design$usu } ## compute phase 1 variance vphase1 <- svyrecvar.phase1(x,d1$cluster, d1$strata, d1$fpc, postStrata=d1$postStrata, ph2prob=ph2pr, nPSUfull=design$phase1$full$fpc$sampsize[design$subset,,drop=FALSE]) ## is phase 2 sampling whole phase 1 units or subsampling within units? if (design$samescale) u2<-x else u2<-x*sqrt(d1$prob) u2[is.na(u2)]<-0 ## compute phase 2 variance vphase2 <- with(design, svyrecvar(u2, phase2$cluster, phase2$strata, phase2$fpc, postStrata=phase2$postStrata)) rval <- vphase1+vphase2 attr(rval, "phases")<-list(phase1=vphase1, phase2=vphase2) rval } svyrecvar.phase1<-function(x, clusters, stratas, fpcs, postStrata=NULL, lonely.psu=getOption("survey.lonely.psu"), one.stage=getOption("survey.ultimate.cluster"), ph2prob, nPSUfull){ x<-as.matrix(x) cal<-NULL ## FIXME: calibration of phase 1 not yet implemented. ## Remove post-stratum means, which may cut across clusters ## Also center the data using any "g-calibration" models if(!is.null(postStrata)){ stop("calibration of phase 1 not yet implemented") for (psvar in postStrata){ if (inherits(psvar, "greg_calibration")) { if (psvar$stage==0){ ## G-calibration at population level x<-qr.resid(psvar$qr,x/psvar$w)*psvar$w } else { ## G-calibration within clusters cal<-c(cal, list(psvar)) } } else { ## ordinary post-stratification psw<-attr(psvar, "weights") postStrata<-as.factor(psvar) psmeans<-rowsum(x/psw,psvar,reorder=TRUE)/as.vector(table(factor(psvar))) x<- x-psmeans[match(psvar,sort(unique(psvar))),]*psw } } } multistage.phase1(x, clusters,stratas,fpcs$sampsize, fpcs$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage,stage=1,cal=cal,ph2prob=ph2prob, nPSUfull=nPSUfull) } multistage.phase1<-function(x, clusters, stratas, nPSUs, fpcs, lonely.psu=getOption("survey.lonely.psu"), one.stage=FALSE,stage,cal,ph2prob, nPSUfull){ n<-NROW(x) v <- onestage.phase1(x,stratas[,1], clusters[,1], nPSUs[,1], fpcs[,1], lonely.psu=lonely.psu,stage=stage,cal=cal, ph2prob=ph2prob, nPSUfull=nPSUfull[,1]) if (one.stage!=TRUE && !is.null(fpcs) && NCOL(clusters)>1) { v.sub<-by(1:n, list(as.numeric(clusters[,1])), function(index){ ## residuals for G-calibration using population information ## only on clusters at this stage. for(cali in cal){ if (cali$stage != stage) next j<-match(clusters[index,1],cali$index) if (length(unique(j))!=1) stop("Internal problem in g-calibration data: stage",stage, ", cluster", j) j<-j[[1]] x[index,]<-qr.resid(cali$qr[[j]], x[index,,drop=FALSE]/cali$w[[j]])*cali$w[[j]] } multistage.phase1(x[index,,drop=FALSE], clusters[index,-1,drop=FALSE], stratas[index,-1,drop=FALSE], nPSUs[index,-1,drop=FALSE], fpcs[index,-1,drop=FALSE], lonely.psu=lonely.psu,one.stage=one.stage-1, stage=stage+1,cal=cal,ph2prob=ph2prob[index], nPSUfull=nPSUfull[index,-1,drop=FALSE])*nPSUfull[index[1],1]/fpcs[index[1],1] }) for(i in 1:length(v.sub)) v<-v+v.sub[[i]] } v } onestrat.phase1<-function(x,cluster,nPSU,fpc, lonely.psu,stratum=NULL, stage=1,cal,ph2prob, nPSUfull){ x<-rowsum(x, cluster) ph2prob<-ph2prob[!duplicated(cluster)] nsubset<-nrow(x) if (nsubset1 || (nPSU>1 && !getOption("survey.adjust.domain.lonely"))) x<-sweep(x, 2, xcenter, "-") if (nPSU>1) scale<-f*nPSUfull/(nPSUfull-1) else scale<-f if (nsubset==1 && nPSU>1){ warning("Stratum (",stratum,") has only one PSU at stage ",stage) if (lonely.psu=="average" && getOption("survey.adjust.domain.lonely")) scale<-NA } if (nPSU>1){ return(crossprod(x/sqrt(ph2prob))*scale) } else if (f<0.0000001) ## certainty PSU return(0*crossprod(x/sqrt(ph2prob))) else { rval<-switch(lonely.psu, certainty=scale*crossprod(x/sqrt(ph2prob)), remove=scale*crossprod(x/sqrt(ph2prob)), adjust=scale*crossprod(x/sqrt(ph2prob)), average=NA*crossprod(x/sqrt(ph2prob)), fail= stop("Stratum (",stratum,") has only one PSU at stage ",stage), stop("Can't handle lonely.psu=",lonely.psu) ) rval } } onestage.phase1<-function(x, strata, clusters, nPSU, fpc, lonely.psu=getOption("survey.lonely.psu"),stage=0, cal,ph2prob, nPSUfull){ stratvars<-tapply(1:NROW(x), list(factor(strata)), function(index){ onestrat.phase1(x[index,,drop=FALSE], clusters[index], nPSU[index][1], fpc[index][1], lonely.psu=lonely.psu,stratum=strata[index][1], stage=stage,cal=cal, ph2prob=ph2prob[index], nPSUfull=nPSUfull[index][1]) }) p<-NCOL(x) nstrat<-length(unique(strata)) nokstrat<-sum(sapply(stratvars,function(m) !any(is.na(m)))) apply(array(unlist(stratvars),c(p,p,length(stratvars))),1:2,sum,na.rm=TRUE)*nstrat/nokstrat } svytotal.twophase<-function(x,design, na.rm=FALSE, deff=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$phase1$sample$variables, na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if(length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } N<-sum(1/design$prob) total <- colSums(x/as.vector(design$prob),na.rm=na.rm) class(total)<-"svystat" attr(total, "var")<-v<-twophasevar(x/design$prob,design) attr(total,"statistic")<-"total" if (is.character(deff) || deff){ nobs<-NROW(design$cluster) if (deff=="replace") vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design)^2)*(N-nobs)/N else vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design)^2) attr(total, "deff")<-v/vsrs } return(total) } svymean.twophase<-function(x,design, na.rm=FALSE,deff=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$phase1$sample$variables ,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if(length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } pweights<-1/design$prob psum<-sum(pweights) average<-colSums(x*pweights/psum) x<-sweep(x,2,average) v<-twophasevar(x*pweights/psum,design) attr(average,"var")<-v attr(average,"statistic")<-"mean" class(average)<-"svystat" if (is.character(deff) || deff){ nobs<-NROW(design$cluster) if(deff=="replace"){ vsrs<-svyvar(x,design,na.rm=na.rm)/(nobs) } else { if(psum1){ N<-ncol(ids) for(i in 2:(N)){ ids[,i]<-do.call("interaction", ids[,1:i,drop=TRUE]) } } ## force clusters nested in strata if (nest && has.strata && NCOL(ids)){ N<-NCOL(ids) for(i in 1:N) ids[,i]<-do.call("interaction", list(strata, ids[,i])) } ## check if clusters nested in strata if (check.strata && nest) warning("No point in check.strata=TRUE if nest=TRUE") if(check.strata && !is.null(strata) && NCOL(ids)){ sc<-rowSums(table(ids[,1],strata)>0) if(any(sc>1)) stop("Clusters not nested in strata") } ## Put degrees of freedom (# of PSUs in each stratum) in object, to ## allow subpopulations if (NCOL(ids)){ nPSU<-table(strata[!duplicated(ids[,1])]) } if (!is.null(fpc)){ if (NCOL(ids)>1){ if (all(fpc<1)) warning("FPC is not currently supported for multi-stage sampling") else stop("Can't compute FPC from population size for multi-stage sampling") } ## Finite population correction: specified per observation if (is.numeric(fpc) && length(fpc)==NROW(variables)){ tbl<-by(fpc,list(strata),unique) if (any(sapply(tbl,length)!=1)) stop("fpc not constant within strata") fpc<-data.frame(strata=factor(rownames(tbl),levels=levels(strata)), N=as.vector(tbl)) } ## Now reduced to fpc per stratum nstr<-table(strata[!duplicated(ids[[1]])]) if (all(fpc[,2]<=1)){ fpc[,2]<- nstr[match(as.character(fpc[,1]), names(nstr))]/fpc[,2] } else if (any(fpc[,2] 0.99])) names(certainty)<-as.character(unique(strata)) } else { warning("Some strata have only one PSU and I can't tell if they are certainty PSUs") } } if (is.numeric(probs) && length(probs)==1) probs<-rep(probs, NROW(variables)) if (length(probs)==0) probs<-rep(1,NROW(variables)) if (NCOL(probs)==1) probs<-data.frame(probs) rval<-list(cluster=ids) rval$strata<-strata rval$has.strata<-has.strata rval$prob<- apply(probs,1,prod) rval$allprob<-probs rval$call<-match.call() rval$variables<-variables rval$fpc<-fpc rval$certainty<-certainty rval$call<-sys.call() rval$nPSU<-nPSU class(rval)<-"survey.design" rval } print.survey.design<-function(x,varnames=FALSE,design.summaries=FALSE,...){ .svycheck(x) n<-NROW(x$cluster) if (x$has.strata) cat("Stratified ") un<-length(unique(x$cluster[,1])) if(n==un){ cat("Independent Sampling design\n") is.independent<-TRUE } else { cat(NCOL(x$cluster),"- level Cluster Sampling design\n") nn<-lapply(x$cluster,function(i) length(unique(i))) cat(paste("With (",paste(unlist(nn),collapse=", "),") clusters.\n",sep="")) is.independent<-FALSE } print(x$call) if (design.summaries){ cat("Probabilities:\n") print(summary(x$prob)) if(x$has.strata){ cat("Stratum sizes: \n") a<-rbind(obs=table(x$strata), design.PSU=x$nPSU, actual.PSU=if(!is.independent || !is.null(x$fpc)) table(x$strata[!duplicated(x$cluster[,1])])) print(a) } if (!is.null(x$fpc)){ if (x$has.strata) { cat("Population stratum sizes (PSUs): \n") print(x$fpc) } else { cat("Population size (PSUs):",x$fpc[,2],"\n") } } } if (varnames){ cat("Data variables:\n") print(names(x$variables)) } invisible(x) } "[.survey.design"<-function (x,i, ...){ if (!missing(i)){ if (is.calibrated(x)){ tmp<-x$prob[i,] x$prob<-rep(Inf, length(x$prob)) x$prob[i,]<-tmp } else { x$variables<-"[.data.frame"(x$variables,i,...,drop=FALSE) x$cluster<-x$cluster[i,,drop=FALSE] x$prob<-x$prob[i] x$allprob<-x$allprob[i,,drop=FALSE] x$strata<-x$strata[i] } } else { x$variables<-x$variables[,...,drop=FALSE] } x } "[<-.survey.design"<-function(x, ...,value){ if (inherits(value, "survey.design")) value<-value$variables x$variables[...]<-value x } dim.survey.design<-function(x){ dim(x$variables) } na.fail.survey.design<-function(object,...){ tmp<-na.fail(object$variables,...) object } na.omit.survey.design<-function(object,...){ tmp<-na.omit(object$variables,...) omit<-attr(tmp,"na.action") if (length(omit)){ object<-object[-omit,] object$variables<-tmp attr(object,"na.action")<-omit } object } na.exclude.survey.design<-function(object,...){ tmp<-na.exclude(object$variables,...) exclude<-attr(tmp,"na.action") if (length(exclude)){ object<-object[-exclude,] object$variables<-tmp attr(object,"na.action")<-exclude } object } update.survey.design<-function(object,...){ dots<-substitute(list(...))[-1] newnames<-names(dots) for(j in seq(along=dots)){ object$variables[,newnames[j]]<-eval(dots[[j]],object$variables, parent.frame()) } object$call<-sys.call(-1) object } subset.survey.design<-function(x,subset,...){ e <- substitute(subset) r <- eval(e, x$variables, parent.frame()) r <- r & !is.na(r) x<-x[r,] x$call<-sys.call(-1) x } summary.survey.design<-function(object,...){ class(object)<-"summary.survey.design" object } print.summary.survey.design<-function(x,...){ y<-x class(y)<-"survey.design" print(y,varnames=TRUE,design.summaries=TRUE,...) } postStratify.survey.design<-function(design, strata, population, partial=FALSE,...){ if(inherits(strata,"formula")){ mf<-substitute(model.frame(strata, data=design$variables,na.action=na.fail)) strata<-eval.parent(mf) } strata<-as.data.frame(strata) sampletable<-xtabs(I(1/design$prob)~.,data=strata) sampletable<-as.data.frame(sampletable) if (inherits(population,"table")) population<-as.data.frame(population) else if (is.data.frame(population)) population$Freq <- as.vector(population$Freq) ##allows Freq computed by tapply() else stop("population must be a table or dataframe") if (!all(names(strata) %in% names(population))) stop("Stratifying variables don't match") nn<- names(population) %in% names(strata) if (sum(!nn)!=1) stop("stratifying variables don't match") names(population)[which(!nn)]<-"Pop.Freq" both<-merge(sampletable, population, by=names(strata), all=TRUE) samplezero <- both$Freq %in% c(0, NA) popzero <- both$Pop.Freq %in% c(0, NA) both<-both[!(samplezero & popzero),] if (any(onlysample<- popzero & !samplezero)){ print(both[onlysample,]) stop("Strata in sample absent from population. This Can't Happen") } if (any(onlypop <- samplezero & !popzero)){ if (partial){ both<-both[!onlypop,] warning("Some strata absent from sample: ignored") } else { print(both[onlypop,]) stop("Some strata absent from sample: use partial=TRUE to ignore them.") } } reweight<-both$Pop.Freq/both$Freq both$label <- do.call("interaction", list(both[,names(strata)])) designlabel <- do.call("interaction", strata) index<-match(designlabel, both$label) attr(index,"oldweights")<-1/design$prob design$prob<-design$prob/reweight[index] attr(index,"weights")<-1/design$prob design$postStrata<-c(design$postStrata,list(index)) ## Do we need to iterate here a la raking to get design strata ## and post-strata both balanced? design$call<-sys.call(-1) design } svyCprod<-function(x, strata, psu, fpc, nPSU, certainty=NULL, postStrata=NULL, lonely.psu=getOption("survey.lonely.psu")){ x<-as.matrix(x) n<-NROW(x) ## Remove post-stratum means, which may cut across PSUs if(!is.null(postStrata)){ for (psvar in postStrata){ if (inherits(psvar, "greg_calibration") || inherits(psvar, "raking")) stop("rake() and calibrate() not supported for old-style design objects") psw<-attr(psvar,"weights") psmeans<-rowsum(x/psw,psvar,reorder=TRUE)/as.vector(table(factor(psvar))) x<- x-psmeans[match(psvar,sort(unique(psvar))),]*psw } } ##First collapse over PSUs if (is.null(strata)){ strata<-rep("1",n) if (!is.null(nPSU)) names(nPSU)<-"1" } else strata<-as.character(strata) ##can't use factors as indices in for()' if (is.null(certainty)){ certainty<-rep(FALSE,length(strata)) names(certainty)<-strata } if (!is.null(psu)){ x<-rowsum(x, psu, reorder=FALSE) strata<-strata[!duplicated(psu)] n<-NROW(x) } if (!is.null(nPSU)){ obsn<-table(strata) dropped<-nPSU[match(names(obsn),names(nPSU))]-obsn if(sum(dropped)){ xtra<-matrix(0,ncol=NCOL(x),nrow=sum(dropped)) strata<-c(strata,rep(names(dropped),dropped)) if(is.matrix(x)) x<-rbind(x,xtra) else x<-c(x,xtra) n<-NROW(x) } } else obsn<-table(strata) if(is.null(strata)){ x<-t(t(x)-colMeans(x)) } else { strata.means<-drop(rowsum(x,strata, reorder=FALSE))/drop(rowsum(rep(1,n),strata, reorder=FALSE)) if (!is.matrix(strata.means)) strata.means<-matrix(strata.means, ncol=NCOL(x)) x<- x- strata.means[ match(strata, unique(strata)),,drop=FALSE] } p<-NCOL(x) v<-matrix(0,p,p) ss<-unique(strata) for(s in ss){ this.stratum <- strata %in% s ## original number of PSUs in this stratum ## before missing data/subsetting this.n <-nPSU[match(s,names(nPSU))] this.df <- this.n/(this.n-1) if (is.null(fpc)) this.fpc <- 1 else{ this.fpc <- fpc[,2][ fpc[,1]==as.character(s)] this.fpc <- (this.fpc - this.n)/this.fpc } xs<-x[this.stratum,,drop=FALSE] this.certain<-certainty[names(certainty) %in% s] ## stratum with only 1 design cluster leads to undefined variance lonely.psu<-match.arg(lonely.psu, c("remove","adjust","fail", "certainty","average")) if (this.n==1 && !this.certain){ this.df<-1 if (lonely.psu=="fail") stop("Stratum ",s, " has only one sampling unit.") else if (lonely.psu!="certainty") warning("Stratum ",s, " has only one sampling unit.") if (lonely.psu=="adjust") xs<-strata.means[match(s,ss),,drop=FALSE] } else if (obsn[match(s,names(obsn))]==1 && !this.certain){ ## stratum with only 1 cluster left after subsetting warning("Stratum ",s," has only one PSU in this subset.") if (lonely.psu=="adjust") xs<-strata.means[match(s,ss),,drop=FALSE] } ## add it up if (!this.certain) v<-v+crossprod(xs)*this.df*this.fpc } if (lonely.psu=="average"){ v<- v/(1-mean(obsn==1 & !certainty)) } v } svymean<-function(x, design,na.rm=FALSE,...){ .svycheck(design) UseMethod("svymean",design) } svymean.survey.design<-function(x,design, na.rm=FALSE,deff=FALSE, influence=FALSE,...){ if (!inherits(design,"survey.design")) stop("design is not a survey design") if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$variables,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] x<-x[nas==0,,drop=FALSE] } pweights<-1/design$prob psum<-sum(pweights) average<-colSums(x*pweights/psum) x<-sweep(x,2,average) v<-svyCprod(x*pweights/psum,design$strata,design$cluster[[1]], design$fpc, design$nPSU,design$certainty, design$postStrata) attr(average,"var")<-v attr(average,"statistic")<-"mean" if(influence) attr(average, "influence")<-x*pweights/psum class(average)<-"svystat" if (is.character(deff) || deff){ nobs<-NROW(design$cluster) vsrs<-svyvar(x,design,na.rm=na.rm)/nobs vsrs<-vsrs*(psum-nobs)/psum attr(average, "deff")<-v/vsrs } return(average) } print.svystat<-function(x,...){ vv<-attr(x,"var") if (is.matrix(vv)) m<-cbind(x,sqrt(diag(vv))) else m<-cbind(x,sqrt(vv)) hasdeff<-!is.null(attr(x,"deff")) if (hasdeff) { m<-cbind(m,deff(x)) colnames(m)<-c(attr(x,"statistic"),"SE","DEff") } else { colnames(m)<-c(attr(x,"statistic"),"SE") } printCoefmat(m) } as.data.frame.svystat<-function(x,...){ rval<-data.frame(statistic=coef(x),SE=SE(x)) names(rval)[1]<-attr(x,"statistic") if (!is.null(attr(x,"deff"))) rval<-cbind(rval,deff=deff(x)) rval } coef.svystat<-function(object,...){ attr(object,"statistic")<-NULL attr(object,"deff")<-NULL attr(object,"var")<-NULL unclass(object) } vcov.svystat<-function(object,...){ as.matrix(attr(object,"var")) } influence.svystat<-function(model,...){ attr(model,"influence") } SE.svystat<-function(object,...){ v<-vcov(object) if (!is.matrix(v) || NCOL(v)==1) sqrt(v) else sqrt(diag(v)) } deff <- function(object,quietly=FALSE,...) UseMethod("deff") deff.default <- function(object, quietly=FALSE,...){ rval<-attr(object,"deff") if (is.null(rval)) { if(!quietly) warning("object has no design effect information") } else rval<-diag(as.matrix(rval)) rval } cv<-function(object,...) UseMethod("cv") cv.default<-function(object, warn=TRUE, ...){ rval<-SE(object)/coef(object) if (warn && any(coef(object)<0,na.rm=TRUE)) warning("CV may not be useful for negative statistics") rval } svytotal<-function(x,design,na.rm=FALSE,...){ .svycheck(design) UseMethod("svytotal",design) } svytotal.survey.design<-function(x,design, na.rm=FALSE, deff=FALSE,influence=FALSE,...){ if (!inherits(design,"survey.design")) stop("design is not a survey design") if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$variables,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] x<-x[nas==0,,drop=FALSE] } N<-sum(1/design$prob) m <- svymean(x, design, na.rm=na.rm) total<-m*N attr(total, "var")<-v<-svyCprod(x/design$prob,design$strata, design$cluster[[1]], design$fpc, design$nPSU,design$certainty,design$postStrata) attr(total,"statistic")<-"total" if (influence){ attr(total,"influence")<-x/design$prob } if (is.character(deff) || deff){ vsrs<-svyvar(x,design)*sum(weights(design)^2) vsrs<-vsrs*(N-NROW(design$cluster))/N attr(total,"deff")<-v/vsrs } return(total) } svyvar<-function(x, design, na.rm=FALSE,...){ .svycheck(design) UseMethod("svyvar",design) } svyvar.survey.design<-function(x, design, na.rm=FALSE,...){ if (inherits(x,"formula")) x<-model.frame(x,model.frame(design),na.action=na.pass) else if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) n<-sum((weights(design,"sampling")!=0) & (rowSums(is.na(as.matrix(x)))==0)) xbar<-svymean(x,design, na.rm=na.rm) if(NCOL(x)==1) { if(n==1){ v<-NA attr(v,"statistic")<-NA attr(v,"var")<-NA class(v)<-"svystat" return(v) } x<-x-xbar v<-svymean(x*x*n/(n-1),design, na.rm=na.rm) attr(v,"statistic")<-"variance" return(v) } x<-t(t(x)-xbar) p<-NCOL(x) a<-matrix(rep(x,p),ncol=p*p) b<-x[,rep(1:p,each=p)] ## Kish uses the n-1 divisor, so it affects design effects v<-svymean(a*b*n/(n-1),design, na.rm=na.rm) vv<-matrix(v,ncol=p) dimnames(vv)<-list(names(xbar),names(xbar)) attr(vv,"var")<-attr(v,"var") attr(vv,"statistic")<-"variance" class(vv)<-c("svyvar","svystat") vv } print.svyvar<-function (x, covariance=FALSE, ...) { if(!is.matrix(x)) NextMethod() vv <- attr(x, "var") if (covariance){ nms<-outer(rownames(x),colnames(x),paste,sep=":") m<-cbind(as.vector(x), sqrt(diag(vv))) rownames(m)<-nms } else{ ii <- which(diag(sqrt(length(x)))>0) m <- cbind(x[ii], sqrt(diag(vv))[ii]) } colnames(m) <- c(attr(x, "statistic"), "SE") printCoefmat(m) } as.matrix.svyvar<-function(x,...) unclass(x) coef.svyratio<-function(object,...,drop=TRUE){ if (!drop) return(object$ratio) cf<-as.vector(object$ratio) nms<-as.vector(outer(rownames(object$ratio),colnames(object$ratio),paste,sep="/")) names(cf)<-nms cf } SE.svyratio<-function(object,...,drop=TRUE){ if(!drop) return(sqrt(object$var)) se<-as.vector(sqrt(object$var)) nms<-as.vector(outer(rownames(object$ratio),colnames(object$ratio),paste,sep="/")) names(se)<-nms se } svyratio<-function(numerator,denominator, design,...){ .svycheck(design) UseMethod("svyratio",design) } svyratio.survey.design<-function(numerator, denominator, design,...){ if (inherits(numerator,"formula")) numerator<-model.frame(numerator,design$variables) else if(typeof(numerator) %in% c("expression","symbol")) numerator<-eval(numerator, design$variables) if (inherits(denominator,"formula")) denominator<-model.frame(denominator,design$variables) else if(typeof(denominator) %in% c("expression","symbol")) denominator<-eval(denominator, design$variables) nn<-NCOL(numerator) nd<-NCOL(denominator) all<-cbind(numerator,denominator) allstats<-svytotal(all,design) rval<-list(ratio=outer(allstats[1:nn],allstats[nn+1:nd],"/")) vars<-matrix(ncol=nd,nrow=nn) for(i in 1:nn){ for(j in 1:nd){ r<-(numerator[,i]-rval$ratio[i,j]*denominator[,j])/sum(denominator[,j]/design$prob) vars[i,j]<-svyCprod(r*1/design$prob, design$strata, design$cluster[[1]], design$fpc, design$nPSU, design$certainty,design$postStrata) } } colnames(vars)<-names(denominator) rownames(vars)<-names(numerator) rval$var<-vars rval$call<-sys.call() class(rval)<-"svyratio" rval } print.svyratio_separate<-function(x,...){ cat("Stratified ratio estimate: ") if (!is.null(x$call)) print(x$call) else if (!is.null(attr(x,"call"))) print(attr(x$call)) for(r in x$ratios) { print(r) } invisible(x) } print.svyratio<-function(x,...){ cat("Ratio estimator: ") if (!is.null(x$call)) print(x$call) else if(!is.null(attr(x,"call"))) print(attr(x,"call")) cat("Ratios=\n") print(x$ratio) cat("SEs=\n") print(sqrt(x$var)) invisible(NULL) } predict.svyratio<-function(object, total, se=TRUE,...){ if (se) return(list(total=object$ratio*total,se=sqrt(object$var)*total)) else return(object$ratio*total) } predict.svyratio_separate<-function(object, total, se=TRUE,...){ if (length(total)!=length(object$ratios)) stop("Number of strata differ in ratio object and totals.") if (!is.null(names(total)) && !is.null(levels(object$strata))){ if (!setequal(names(total), levels(object$strata))) warning("Names of strata differ in ratio object and totals") else if (!all(names(total)==levels(object$strata))){ warning("Reordering supplied totals to make their names match the ratio object") total<-total[match(names(total),levels(object$strata))] } } totals<-mapply(predict, object=object$ratios, total=total,se=se,...,SIMPLIFY=FALSE) if(se){ rval<-totals[[1]]$total v<-totals[[1]]$se^2 for(ti in totals[-1]) { rval<-rval+ti$total v<-v+ti$se^2 } list(total=rval,se=sqrt(v)) } else { rval<-totals[[1]] for (ti in totals[-1]) rval<-rval+ti rval } } cv.svyratio<-function(object,...){ sqrt(object$var)/object$ratio } svytable<-function(formula, design, ...){ UseMethod("svytable",design) } svytable.survey.design<-function(formula, design, Ntotal=NULL, round=FALSE,...){ if (!inherits(design,"survey.design")) stop("design must be a survey design") weights<-1/design$prob ## unstratified or unadjusted if (length(Ntotal)<=1 || !design$has.strata){ if (length(formula)==3) tblcall<-bquote(xtabs(I(weights*.(formula[[2]]))~.(formula[[3]]), data=model.frame(design),...)) else tblcall<-bquote(xtabs(weights~.(formula[[2]]), data=model.frame(design),...)) tbl<-eval(tblcall) if (!is.null(Ntotal)) { if(length(formula)==3) tbl<-tbl/sum(Ntotal) else tbl<-tbl*sum(Ntotal)/sum(tbl) } if (round) tbl<-round(tbl) attr(tbl,"call")<-match.call() class(tbl)<-c("svytable",class(tbl)) return(tbl) } ## adjusted and stratified if (length(formula)==3) tblcall<-bquote(xtabs(I(weights*.(formula[[2]]))~design$strata[,1]+.(formula[[3]]), data=model.frame(design),...)) else tblcall<-bquote(xtabs(weights~design$strata[,1]+.(formula[[2]]), data=model.frame(design),...)) tbl<-eval(tblcall) ss<-match(sort(unique(design$strata[,1])), Ntotal[,1]) dm<-dim(tbl) layer<-prod(dm[-1]) tbl<-sweep(tbl,1,Ntotal[ss, 2]/apply(tbl,1,sum),"*") tbl<-apply(tbl, 2:length(dm), sum) if (round) tbl<-round(tbl) class(tbl)<-c("svytable","xtabs", "table") attr(tbl, "call")<-match.call() tbl } svycoxph<-function(formula,design,subset=NULL,rescale=TRUE,...){ .svycheck(design) UseMethod("svycoxph",design) } svycoxph.survey.design<-function(formula,design, subset=NULL, rescale=TRUE, ...){ subset<-substitute(subset) subset<-eval(subset, model.frame(design),parent.frame()) if (!is.null(subset)) design<-design[subset,] if(any(weights(design)<0)) stop("weights must be non-negative") data<-model.frame(design) g<-match.call() g$formula<-eval.parent(g$formula) g$design<-NULL g$var<-NULL g$rescale <- NULL if (is.null(g$weights)) g$weights<-quote(.survey.prob.weights) else g$weights<-bquote(.survey.prob.weights*.(g$weights)) g[[1]]<-quote(coxph) g$data<-quote(data) g$subset<-quote(.survey.prob.weights>0) g$model <- TRUE ##need to rescale weights for stability ## unless the user doesn't want to if (rescale) data$.survey.prob.weights<-(1/design$prob)/mean(1/design$prob) else data$.survey.prob.weights<-1/design$prob if (!all(all.vars(formula) %in% names(data))) stop("all variables must be in design= argument") g<-with(list(data=data), eval(g)) if (inherits(g, "coxph.penal")) warning("svycoxph does not support penalised terms") g$call<-match.call() g$call[[1]]<-as.name(.Generic) g$printcall<-sys.call(-1) g$printcall[[1]]<-as.name(.Generic) class(g)<-c("svycoxph", class(g)) g$survey.design<-design nas<-g$na.action if (length(nas)) design<-design[-nas,] ## if there are betas... if (length(coef(g))>0){ dbeta.subset<-resid(g,"dfbeta",weighted=TRUE) if (nrow(design)==NROW(dbeta.subset)){ dbeta<-as.matrix(dbeta.subset) } else { dbeta<-matrix(0,ncol=NCOL(dbeta.subset),nrow=nrow(design)) dbeta[is.finite(design$prob),]<-dbeta.subset } if (!is.null(g$naive.var)) ## newer versions of survival switch to robust=TRUE with non-integer weights g$inv.info<-g$naive.var else g$inv.info<-g$var if (inherits(design,"survey.design2")) g$var<-svyrecvar(dbeta, design$cluster, design$strata, design$fpc, postStrata=design$postStrata) else if (inherits(design, "twophase")) g$var<-twophasevar(dbeta, design) else if(inherits(design, "twophase2")) g$var<-twophase2var(dbeta, design) else if(inherits(design, "pps")) g$var<-ppsvar(dbeta,design) else g$var<-svyCprod(dbeta, design$strata, design$cluster[[1]], design$fpc,design$nPSU, design$certainty,design$postStrata) g$wald.test<-coef(g)%*%solve(g$var,coef(g)) } else g$var<-matrix(ncol=0,nrow=0) g$ll<-g$loglik g$loglik<-NULL g$rscore<-NULL g$score<-NA g$degf.resid<-degf(design)-length(coef(g)[!is.na(coef(g))])+1 g } model.frame.svycoxph<-function(formula,...){ f<-formula$call env <- environment(formula(formula)) if (is.null(env)) env <- parent.frame() f[[1]]<-as.name("model.frame") f$data<-quote(data) f$design<-NULL f$method<-f$control<-f$singular.ok<-f$model<-f$x<-f$y<-f$iter<-NULL f$formula<-formula(formula) if (is.null(f$weights)) f$weights<-quote(.survey.prob.weights) else f$weights<-bquote(.survey.prob.weights*.(f$weights)) design<-formula$survey.design data<-model.frame(design) data$.survey.prob.weights<-(1/design$prob)/sum(1/design$prob) with(list(data=data), eval(f)) } ## model.matrix.svycoxph<-function (object, data = NULL, contrast.arg = object$contrasts, ## ...) ## { ## if (!is.null(object[["x"]])) ## object[["x"]] ## else { ## if (is.null(data)) ## data <- model.frame(object, ...) ## else data <- model.frame(object, data = data, ...) ## Terms <- object$terms ## attr(Terms, "intercept") <- 1 ## strats <- attr(Terms, "specials")$strata ## cluster <- attr(Terms, "specials")$cluster ## dropx <- NULL ## if (length(cluster)) { ## tempc <- untangle.specials(Terms, "cluster", 1:10) ## ord <- attr(Terms, "order")[tempc$terms] ## if (any(ord > 1)) ## stop("Cluster can not be used in an interaction") ## dropx <- tempc$terms ## } ## if (length(strats)) { ## temp <- untangle.specials(Terms, "strata", 1) ## dropx <- c(dropx, temp$terms) ## } ## if (length(dropx)) { ## newTerms <- Terms[-dropx] ## X <- model.matrix(newTerms, data, contrasts = contrast.arg) ## } ## else { ## newTerms <- Terms ## X <- model.matrix(Terms, data, contrasts = contrast.arg) ## } ## X ## } ## } print.svycoxph<-function(x,...){ print(x$survey.design, varnames=FALSE, design.summaries=FALSE,...) ## x$call<-x$printcall NextMethod() } summary.svycoxph<-function(object,...){ print(object$survey.design,varnames=FALSE, design.summaries=FALSE,...) ## object$call<-object$printcall NextMethod() } survfit.svycoxph<-function(formula,...){ stop("No survfit method for survey models") } extractAIC.svycoxph<-function(fit,...){ stop("No AIC for survey models") } svyglm<-function(formula, design,subset=NULL,family=stats::gaussian(),start=NULL, ...){ .svycheck(design) UseMethod("svyglm",design) } svyglm.survey.design<-function(formula,design,subset=NULL, family=stats::gaussian(),start=NULL, rescale=TRUE,..., deff=FALSE, influence=FALSE){ subset<-substitute(subset) subset<-eval(subset, model.frame(design), parent.frame()) if (!is.null(subset)){ if (any(is.na(subset))) stop("subset must not contain NA values") design<-design[subset,] } data<-model.frame(design) g<-match.call() g$formula<-eval.parent(g$formula) g$influence<-NULL g$design<-NULL g$var <- NULL g$rescale <- NULL g$deff<-NULL g$subset <- NULL ## done it already g$family<-family if (is.null(g$weights)) g$weights<-quote(.survey.prob.weights) else g$weights<-bquote(.survey.prob.weights*.(g$weights)) g$data<-quote(data) g[[1]]<-quote(glm) ##need to rescale weights for stability in binomial ## (unless the user doesn't want to) if (rescale) data$.survey.prob.weights<-(1/design$prob)/mean(1/design$prob) else data$.survey.prob.weights<-(1/design$prob) if(any(is.na(data$.survey.prob.weights))) stop("weights must not contain NA values") if (!all(all.vars(formula) %in% names(data))) stop("all variables must be in design= argument") g<-with(list(data=data), eval(g)) summ<-summary(g) g$naive.cov<-summ$cov.unscaled nas<-g$na.action if (length(nas)) design<-design[-nas,] g$cov.unscaled<-svy.varcoef(g,design) g$df.residual <- degf(design)+1-length(coef(g)[!is.na(coef(g))]) class(g)<-c("svyglm",class(g)) g$call<-match.call() g$call[[1]]<-as.name(.Generic) if(!("formula" %in% names(g$call))) { if (is.null(names(g$call))) i<-1 else i<-min(which(names(g$call)[-1]=="")) names(g$call)[i+1]<-"formula" } if(deff){ vsrs<-summ$cov.scaled*mean(data$.survey.prob.weights) attr(g,"deff")<-g$cov.unscaled/vsrs } if (influence){ estfun<- model.matrix(g)*naa_shorter(nas, resid(g,"working"))*g$weights if (g$rankNROW(estfun))){ estfun1<-matrix(0,ncol=ncol(estfun),nrow=nrow(data)) estfun1[-nas,]<-estfun estfun<-estfun1 } attr(g, "influence")<-estfun%*%g$naive.cov } g$survey.design<-design g } print.svyglm<-function(x,...){ print(x$survey.design, varnames=FALSE, design.summaries=FALSE,...) NextMethod() } coef.svyglm<-function(object,complete=FALSE,...,na.rm=!complete) { beta<-object$coefficients if (!na.rm || length(beta)==object$rank) beta else beta[object$qr$pivot[1:object$rank]] } vcov.svyglm<-function(object,...) { v<-object$cov.unscaled dimnames(v)<-list(names(coef(object)),names(coef(object))) v } svy.varcoef<-function(glm.object,design){ Ainv<-summary(glm.object)$cov.unscaled nas<-glm.object$na.action estfun<-model.matrix(glm.object)*naa_shorter(nas, resid(glm.object,"working"))*glm.object$weights if (glm.object$rank|z|)")) } else if (df.r > 0) { pvalue <- 2 * pt(-abs(tvalue), df.r) coef.table <- cbind(coef.p, s.err, tvalue, pvalue) dimnames(coef.table) <- list(names(coef.p), c(dn, "t value", "Pr(>|t|)")) } else { coef.table <- cbind(coef.p, s.err, tvalue, NaN) dimnames(coef.table) <- list(names(coef.p), c(dn, "t value", "Pr(>|t|)")) } ans <- c(object[c("call", "terms", "family", "deviance", "aic", "contrasts", "df.residual", "null.deviance", "df.null", "iter")], list(deviance.resid = residuals(object, type = "deviance"), aic = object$aic, coefficients = coef.table, dispersion = dispersion, df = c(object$rank, df.r,NCOL(Qr$qr)), cov.unscaled = covmat, cov.scaled = covmat)) if (correlation) { dd <- sqrt(diag(covmat)) ans$correlation <- covmat/outer(dd, dd) } ans$aliased<-is.na(coef(object,na.rm=FALSE)) ans$survey.design<-list(call=object$survey.design$call) class(ans) <- c("summary.svyglm","summary.glm") return(ans) } confint.svyglm<-function(object,parm,level=0.95,method=c("Wald","likelihood"),ddf=NULL,...){ method<-match.arg(method) if(method=="Wald"){ if (is.null(ddf)) ddf <- object$df.residual if (ddf<=0) { ci<-confint.default(object,parm=parm,level=.95,...)*NaN } else { tlevel <- 1 - 2*pnorm(qt((1 - level)/2, df = ddf)) ci<-confint.default(object,parm=parm,level=tlevel,...) } a <- (1 - level)/2 a <- c(a, 1 - a) pct <- format.perc(a, 3) colnames(ci)<-pct return(ci) } pnames <- names(coef(object)) if (missing(parm)) parm <- seq_along(pnames) else if (is.character(parm)) parm <- match(parm, pnames, nomatch = 0) lambda<-diag(object$cov.unscaled[parm,parm,drop=FALSE])/diag(object$naive.cov[parm,parm,drop=FALSE]) if(is.null(ddf)) ddf<-object$df.residual if (ddf==Inf){ alpha<-pnorm(qnorm((1-level)/2)*sqrt(lambda))/2 } else if (ddf<=0) { stop("zero or negative denomintor df") } else { alpha<-pnorm(qt((1-level)/2,df=ddf)*sqrt(lambda))/2 } rval<-vector("list",length(parm)) for(i in 1:length(parm)){ temp<-MASSprofile_glm(fitted=object,which=parm[i],alpha=alpha[i],...) rval[[i]]<-confint_profile(temp,parm=parm[i],level=level, unscaled_level=2*alpha[i],...) } names(rval)<-pnames[parm] if (length(rval)==1) rval<-rval[[1]] else rval<-do.call(rbind,rval) attr(rval,"levels")<-level rval } ## ## based on MASS:::confint.profile.glm ## which is GPL and (c) Bill Venables and Brian D Ripley. ## confint_profile <- function (object, parm = seq_along(pnames), level = 0.95, unscaled_level, ...) { of <- attr(object, "original.fit") pnames <- names(coef(of)) if (is.character(parm)) parm <- match(parm, pnames, nomatch = 0L) a <- (1-level)/2 a <- c(a, 1 - a) pct <- paste(round(100 * a, 1), "%") ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(pnames[parm], pct)) cutoff <- c(qnorm(unscaled_level),qnorm(unscaled_level, lower.tail=FALSE)) for (pm in parm) { pro <- object[[pnames[pm]]] if (is.null(pro)) next if (length(pnames) > 1L) sp <- spline(x = pro[, "par.vals"][, pm], y = pro[, 1]) else sp <- spline(x = pro[, "par.vals"], y = pro[, 1]) ci[pnames[pm], ] <- approx(sp$y, sp$x, xout = cutoff)$y } drop(ci) } ## ## MASS:::profile.glm with very slight changes to avoid rounding error in 1-alpha ## original is GPL and (c) Bill Venables and Brian D Ripley. ## MASSprofile_glm<-function (fitted, which = 1:p, alpha = 0.01, maxsteps = 10, del = zmax/5, trace = FALSE, ...) { Pnames <- names(B0 <- coef(fitted)) nonA <- !is.na(B0) pv0 <- t(as.matrix(B0)) p <- length(Pnames) if (is.character(which)) which <- match(which, Pnames) summ <- summary(fitted) std.err <- summ$coefficients[, "Std. Error", drop = FALSE] mf <- model.frame(fitted) Y <- model.response(mf) n <- NROW(Y) O <- model.offset(mf) if (!length(O)) O <- rep(0, n) W <- model.weights(mf) if (length(W) == 0L) W <- rep(1, n) OriginalDeviance <- deviance(fitted) DispersionParameter <- summ$dispersion X <- model.matrix(fitted) fam <- family(fitted) switch(fam$family, binomial = , poisson = , `Negative Binomial` = { zmax <- sqrt(qchisq(alpha, 1,lower.tail=FALSE)) profName <- "z" }, gaussian = , quasi = , inverse.gaussian = , quasibinomial = , quasipoisson = , { zmax <- sqrt(qf(alpha, 1, n - p,lower.tail=FALSE)) profName <- "tau" }) prof <- vector("list", length = length(which)) names(prof) <- Pnames[which] for (i in which) { if (!nonA[i]) next zi <- 0 pvi <- pv0 a <- nonA a[i] <- FALSE Xi <- X[, a, drop = FALSE] pi <- Pnames[i] for (sgn in c(-1, 1)) { if (trace) message("\nParameter: ", pi, " ", c("down", "up")[(sgn + 1)/2 + 1]) step <- 0 z <- 0 LP <- X[, nonA, drop = FALSE] %*% B0[nonA] + O while ((step <- step + 1) < maxsteps && abs(z) < zmax) { bi <- B0[i] + sgn * step * del * std.err[Pnames[i], 1] o <- O + X[, i] * bi fm <- glm.fit(x = Xi, y = Y, weights = W, etastart = LP, offset = o, family = fam, control = fitted$control) LP <- Xi %*% fm$coefficients + o ri <- pv0 ri[, names(coef(fm))] <- coef(fm) ri[, pi] <- bi pvi <- rbind(pvi, ri) zz <- (fm$deviance - OriginalDeviance)/DispersionParameter if (zz > -0.001) zz <- max(zz, 0) else stop("profiling has found a better solution, so original fit had not converged") z <- sgn * sqrt(zz) zi <- c(zi, z) } } si <- order(zi) prof[[pi]] <- structure(data.frame(zi[si]), names = profName) prof[[pi]]$par.vals <- pvi[si, , drop = FALSE] } val <- structure(prof, original.fit = fitted, summary = summ) class(val) <- c("profile.glm", "profile") val } ### svymle<-function(loglike, gradient=NULL, design, formulas, start=NULL, control=list(), na.action="na.fail", method=NULL,lower=NULL,upper=NULL, influence=FALSE,...){ if(is.null(method)) method<-if(is.null(gradient)) "Nelder-Mead" else "newuoa" if (!inherits(design,"survey.design")) stop("design is not a survey.design") weights<-weights(design) wtotal<-sum(weights) if (!(method %in% c("bobyqa","newuoa")) && is.null(control$fnscale)) control$fnscale <- -wtotal/length(weights) if (inherits(design, "twophase")) { data<-design$phase1$sample$variables } else { data<-design$variables } ## Get the response variable nms<-names(formulas) if (nms[1]==""){ if (inherits(formulas[[1]],"formula")) { y<-eval.parent(model.frame(formulas[[1]],data=data,na.action=na.pass)) } else { y<-eval(y,data,parent.frame()) } formulas[1]<-NULL if (FALSE && NCOL(y)>1) stop("Y has more than one column") } else { ## one formula must have response has.response<-sapply(formulas,length)==3 if (sum(has.response)!=1) stop("Need a response variable") ff<-formulas[[which(has.response)]] ff[[3]]<-1 y<-eval.parent(model.frame(ff,data=data,na.action=na.pass)) formulas[[which(has.response)]]<-delete.response(terms(formulas[[which(has.response)]])) nms<-c("",nms) } if(length(which(nms==""))>1) stop("Formulas must have names") mf<-vector("list",length(formulas)) vnms <- unique(do.call(c, lapply(formulas, all.vars))) uformula <- make.formula(vnms) mf <- model.frame(uformula, data=data,na.action=na.pass) mf <- cbind(`(Response)`=y, mf) mf<-mf[,!duplicated(colnames(mf)),drop=FALSE] mf<-get(na.action)(mf) nas<-attr(mf,"na.action") if (length(nas)) design<-design[-nas,] weights<-1/design$prob wtotal<-sum(weights) Y<-mf[,1] # mm <- lapply(formulas,model.matrix, data=mf) mmFrame <- lapply(formulas,model.frame, data=mf) mm = mapply(model.matrix, object = formulas, data=mmFrame, SIMPLIFY=FALSE) mmOffset <- lapply(mmFrame, model.offset) # add a vector of zeros if there is no offset provided noOffset = which(unlist(lapply(mmOffset, length))==0) for(D in noOffset) { mmOffset[[D]] = rep(0, NROW(mm[[1]])) } ## parameter names parnms<-lapply(mm,colnames) for(i in 1:length(parnms)) parnms[[i]]<-paste(nms[i+1],parnms[[i]],sep=".") parnms<-unlist(parnms) # maps position in theta to model matrices np<-c(0,cumsum(sapply(mm,NCOL))) objectivefn<-function(theta,...){ args<-vector("list",length(nms)) args[[1]]<-Y for(i in 2:length(nms)) args[[i]]<-mm[[i-1]]%*%theta[(np[i-1]+1):np[i]] + mmOffset[[i-1]] names(args)<-nms args<-c(args, ...) sum(do.call("loglike",args)*weights) } if (is.null(gradient)) { grad<-NULL } else { fnargs<-names(formals(loglike))[-1] grargs<-names(formals(gradient))[-1] if(!identical(fnargs,grargs)) stop("loglike and gradient have different arguments.") reorder<-na.omit(match(grargs,nms[-1])) grad<-function(theta,...){ args<-vector("list",length(nms)) args[[1]]<-Y for(i in 2:length(nms)) args[[i]]<-drop( mm[[i-1]]%*%theta[(np[i-1]+1):np[i]] + mmOffset[[i-1]]) names(args)<-nms args<-c(args,...) rval<-NULL tmp<-do.call("gradient",args) for(i in reorder){ rval<-c(rval, colSums(as.matrix(tmp[,i]*weights*mm[[i]]))) } drop(rval) } } theta0<-numeric(np[length(np)]) if (is.list(start)) st<-do.call("c",start) else st<-start if (length(st)==length(theta0)) { theta0<-st } else { stop("starting values wrong length") } if (method=="nlm"){ ff<-function(theta){ rval<- -objectivefn(theta) if (is.na(rval)) rval<- -Inf attr(rval,"gradient")<- -grad(theta) rval } rval<-nlm(ff, theta0,hessian=TRUE) if (rval$code>3) warning("nlm did not converge") rval$par<-rval$estimate } else if (method == "newuoa"){ rval<-minqa::uobyqa(theta0, function(par,...) -objectivefn(par,...), control=control, ...) if(rval$ier>0) warning(rval$msg) rval$hessian <- numDeriv::hessian(objectivefn, rval$par) } else if (method == "bobyqa"){ if (is.list(lower)) lower<-do.call(c, lower) if (is.list(upper)) upper<-do.call(c,upper) rval<-minqa::bobyqa(theta0, function(par,...) -objectivefn(par,...), control=control,lower=lower,upper=upper, ...) if(rval$ier>0) warning(rval$msg) rval$hessian <- numDeriv::hessian(objectivefn,rval$par) }else { rval<-optim(theta0, objectivefn, grad,control=control, hessian=TRUE,method=method,...) if (rval$conv!=0) warning("optim did not converge") } names(rval$par)<-parnms dimnames(rval$hessian)<-list(parnms,parnms) if (is.null(gradient)) { rval$invinf<-solve(-rval$hessian) rval$scores<-NULL rval$sandwich<-NULL } else { theta<-rval$par args<-vector("list",length(nms)) args[[1]]<-Y for(i in 2:length(nms)) args[[i]]<-drop(mm[[i-1]]%*%theta[(np[i-1]+1):np[i]]+ mmOffset[[i-1]]) names(args)<-nms args<-c(args,...) deta<-do.call("gradient",args) rval$scores<-NULL for(i in reorder) rval$scores<-cbind(rval$scores,deta[,i]*weights*mm[[i]]) rval$invinf<-solve(-rval$hessian) dimnames(rval$invinf)<-list(parnms,parnms) db<-rval$scores%*%rval$invinf if (inherits(design,"survey.design2")) rval$sandwich<-svyrecvar(db,design$cluster,design$strata, design$fpc, postStrata=design$postStrata) else if (inherits(design, "twophase")) rval$sandwich<-twophasevar(db,design) else rval$sandwich<-svyCprod(db,design$strata,design$cluster[[1]], design$fpc, design$nPSU, design$certainty, design$postStrata) dimnames(rval$sandwich)<-list(parnms,parnms) } if (influence){ if (nas && (NROW(data)>NROW(db))){ db1<-matrix(0,nrow=NROW(data),ncol=NCOL(db)) db1[-nas,]<-db db<-db1 } attr(rval,"influence")<-db } rval$call<-match.call() rval$design<-design class(rval)<-"svymle" rval } coef.svymle<-function(object,...) object$par vcov.svymle<-function(object,stderr=c("robust","model"),...) { stderr<-match.arg(stderr) if (stderr=="robust"){ rval<-object$sandwich if (is.null(rval)) { p<-length(coef(object)) rval<-matrix(NA,p,p) } } else { rval<-object$invinf*mean(1/object$design$prob) } rval } print.svymle<-function(x,...){ cat("Survey-sampled mle: \n") print(x$call) cat("Coef: \n") print(x$par) } summary.svymle<-function(object,stderr=c("robust","model"),...){ cat("Survey-sampled mle: \n") print(object$call) stderr<-match.arg(stderr) tbl<-data.frame(Coef=coef(object),SE=sqrt(diag(vcov(object,stderr=stderr)))) tbl$p.value<-format.pval(2*(1-pnorm(abs(tbl$Coef/tbl$SE))), digits=3,eps=0.001) print(tbl) print(object$design) } model.frame.survey.design<-function(formula,...,drop=TRUE){ formula$variables } model.frame.svyrep.design<-function(formula,...){ formula$variables } model.frame.survey.design2<-function(formula,...){ formula$variables } .onLoad<-function(...){ if (is.null(getOption("survey.lonely.psu"))) options(survey.lonely.psu="fail") if (is.null(getOption("survey.ultimate.cluster"))) options(survey.ultimate.cluster=FALSE) if (is.null(getOption("survey.want.obsolete"))) options(survey.want.obsolete=FALSE) if (is.null(getOption("survey.adjust.domain.lonely"))) options(survey.adjust.domain.lonely=FALSE) if (is.null(getOption("survey.drop.replicates"))) options(survey.drop.replicates=TRUE) if (is.null(getOption("survey.multicore"))) options(survey.multicore=FALSE) if (is.null(getOption("survey.replicates.mse"))) options(survey.replicates.mse=FALSE) if (is.null(getOption("survey.use_rcpp"))) options(survey.use_rcpp=TRUE) } predterms<-function(object,se=FALSE,terms=NULL){ tt<-terms(object) n <- length(object$residuals) p <- object$rank p1 <- seq_len(p) piv <- object$qr$pivot[p1] beta<-coef(object) X<-mm<-model.matrix(object) aa <- attr(mm, "assign") ll <- attr(tt, "term.labels") hasintercept <- attr(tt, "intercept") > 0L if (hasintercept) ll <- c("(Intercept)", ll) aaa <- factor(aa, labels = ll) asgn <- split(order(aa), aaa) if (hasintercept) { asgn$"(Intercept)" <- NULL } avx <- colMeans(mm) termsconst <- sum(avx[piv] * beta[piv]) nterms <- length(asgn) ip <- matrix(ncol = nterms, nrow = NROW(X)) if (nterms > 0) { predictor <- matrix(ncol = nterms, nrow = NROW(X)) dimnames(predictor) <- list(rownames(X), names(asgn)) if (hasintercept) X <- sweep(X, 2L, avx, check.margin = FALSE) unpiv <- rep.int(0L, NCOL(X)) unpiv[piv] <- p1 for (i in seq.int(1L, nterms, length.out = nterms)) { iipiv <- asgn[[i]] ii <- unpiv[iipiv] iipiv[ii == 0L] <- 0L predictor[, i] <- if (any(iipiv > 0L)) X[, iipiv, drop = FALSE] %*% beta[iipiv] else 0 if (se){ ip[,i]<-if (any(iipiv > 0L)) rowSums(as.matrix(X[, iipiv, drop = FALSE] %*% vcov(object)[ii,ii]) * X[, iipiv, drop = FALSE]) else 0 } } if (!is.null(terms)) { predictor <- predictor[, terms, drop = FALSE] if (se) ip <- ip[, terms, drop = FALSE] } } else { predictor <- ip <- matrix(0, n, 0) } attr(predictor, "constant") <- if (hasintercept) termsconst else 0 if(se) dimnames(ip)<-dimnames(predictor) if (se) list(fit=predictor,se.fit=sqrt(ip)) else predictor } predict.svyglm <- function(object, newdata=NULL, total=NULL, type = c("link", "response","terms"), se.fit=(type!="terms"), vcov=FALSE,...){ if(is.null(newdata)) newdata<-model.frame(object$survey.design) type<-match.arg(type) if (type=="terms") return(predterms(object,se=se.fit,...)) tt<-delete.response(terms(formula(object))) mf<-model.frame(tt,data=newdata, xlev=object$xlevels) mm<-model.matrix(tt,mf,contrasts.arg = object$contrasts) if (!is.null(total) && attr(tt,"intercept")){ mm[,attr(tt,"intercept")]<-mm[,attr(tt,"intercept")]*total } eta<-drop(mm %*% coef(object)) d<-drop(object$family$mu.eta(eta)) eta<-switch(type, link=eta, response=object$family$linkinv(eta)) if(se.fit){ if(vcov){ vv<-mm %*% vcov(object) %*% t(mm) attr(eta,"var")<-switch(type, link=vv, response=d*(t(vv*d))) } else { ## FIXME make this more efficient vv<-drop(rowSums((mm %*% vcov(object)) * mm)) attr(eta,"var")<-switch(type, link=vv, response=drop(d*(t(vv*d)))) } } attr(eta,"statistic")<-type class(eta)<-"svystat" eta } survey/R/svymi.R0000644000176200001440000002336614556563037013332 0ustar liggesusers svydesign.imputationList<-function(ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, pps=FALSE,calibrate.formula=NULL,...){ designs <- lapply(data$imputations, function(d) svydesign(ids=ids, probs=probs, strata=strata,variables=variables,fpc=fpc,nest=nest, check.strata=check.strata, weights=weights,data=d,pps=pps,calibrate.formula=calibrate.formula,...)) rval <- list(designs=designs, call=sys.call(-1)) class(rval) <- "svyimputationList" rval } svrepdesign.imputationList<-function(variables=NULL, repweights,weights,data,degf=NULL, mse=getOption("survey.replicates.mse"),...){ ## dispatch on data= if (!is.null(variables) && !inherits(variables,"imputationList")) stop("'variables' must also be an 'imputationList' (or NULL)") if(!is.null(variables)){ if (inherits(repweights,"imputationList")){ designs <- mapply(function(v,d,r) svrepdesign(variables=v, repweights=r, weights=weights,data=NULL,degf=degf,mse=mse,...), variables$imputations,data$imputations, repweights$imputations,SIMPLIFY=FALSE) } else { designs <- mapply(function(d,v) svrepdesign(variables=v, repweights=repweights, weights=weights,data=d,degf=degf,mse=mse,...), data$imputations,variables$imputations,SIMPLIFY=FALSE) } }else{ if (inherits(repweights,"imputationList")){ designs <- mapply(function(d,r) svrepdesign(repweights=r, weights=weights,data=NULL,degf=degf,mse=mse,...), data$imputations, repweights$imputations,SIMPLIFY=FALSE) } else { designs <- lapply(data$imputations, function(d) svrepdesign( repweights=repweights, weights=weights,data=d,degf=degf,mse=mse,...)) } } rval <- list(designs=designs, call=sys.call(-1)) class(rval) <- "svyimputationList" rval } as.svrepdesign.svyimputationList<-function(design,type=c("auto","JK1","JKn","BRR","bootstrap","subbootstrap","mrbbootstrap","Fay"), fay.rho=0, fpc=NULL, fpctype=NULL, separate.replicates=FALSE,...,compress=TRUE, mse=getOption("survey.replicates.mse")){ if (inherits(design$design[[1]], "svyrep.design")){ warning("already a replicate-weights design") return(design) } pwts<-weights(design$designs[[1]], "sampling") same.weights<-all(sapply(design$designs, function (d) isTRUE(all.equal(pwts, weights(d,"sampling"))))) if (!same.weights && separate.replicates){ repdesigns<-lapply(design$designs, function(d) as.svrepdesign(d, type=type,fay.rho=fay.rho, fpc=fpc,fpctype=fpctype, ..., compress=compress, mse=mse)) } else { design1<-design$designs[[1]] repdesign1<-as.svrepdesign(design1, type=type,fay.rho=fay.rho, fpc=fpc,fpctype=fpctype, ..., compress=compress, mse=mse) repdesigns<-lapply(design$designs, function(d) { repdesign1$variables<-d$variables repdesign1 }) } rval<-list(designs=repdesigns, call=sys.call(-1)) class(rval)<-"svyimputationList" rval } svydesign.DBimputationList<-function(ids, probs = NULL, strata = NULL, variables = NULL, fpc = NULL, data, nest = FALSE, check.strata = !nest, weights = NULL, ...){ design.vars<-c(all.vars(ids), all.vars(probs), all.vars(strata),all.vars(fpc), all.vars(weights)) design.query<-paste("select", paste(design.vars,collapse=","), "from", data$imputations[1]) design.data<-DBI::dbGetQuery(data$db$connection, design.query) rval<-list() rval$design<-svydesign(ids=ids, probs=probs, strata=strata, data=design.data, fpc=fpc, variables=variables, nest=nest,check.strata=check.strata, weights=weights) class(rval$design)<-c("DBIsvydesign", class(rval$design)) rval$design$updates<-data$updates rval$db<-data$db rval$imputations<-data$imputations rval$variables<-NULL rval$call<-sys.call(-1) class(rval)<-"svyDBimputationList" rval } print.svyDBimputationList<-function(x,...){ cat("DB-backed Multiple (",length(x$imputations),") imputations: ",sep="") print(x$call) } print.svyimputationList<-function(x,...){ cat("Multiple (",length(x$designs),") imputations: ",sep="") print(x$call) } dim.svyimputationList<-function(x){ c(dim(x$designs[[1]]),length(x$designs)) } dimnames.svyimputationList<-function(x){ c(dimnames(x$designs[[1]]),list(paste("imputation",1:length(x$designs)))) } subset.svyimputationList<-function(x, subset,...){ n<-nrow(x$designs[[1]]) e<-substitute(subset) r<-eval(e,x$designs[[1]]$variables, parent.frame()) r <- r & !is.na( r ) x$designs[[1]]<-x$designs[[1]][r,] same<-TRUE for(i in 2:length(x$designs)){ r1<-eval(e,x$designs[[i]]$variables, parent.frame()) x$designs[[i]]<-x$designs[[i]][r1,] r1<-r1 & !is.na(r1) if (any(r!=r1)) { same<-FALSE } } if (!same) warning('subset differed between imputations') x$call<-sys.call(-1) x } subset.svyDBimputationList<-function(x, subset,...,all=FALSE){ n<-nrow(x$designs[[1]]) e<-substitute(subset) df<-getvars(all.vars(e), x$db$connection, x$imputations[1], db.only=FALSE, updates=x$design$updates) r<-eval(e,df, parent.frame()) same<-TRUE for(i in 2:length(x$imputations)){ df<-getvars(all.vars(e), x$db$connection, x$imputations[i], db.only=FALSE, updates=x$design$updates) r1<-eval(e,df, parent.frame()) r1<-r1 & !is.na(r1) if (any(r!=r1)) { same<-FALSE if (all) r <- r & r1 else r<- r | r1 } } if (!same) warning('subset differed between imputations') x$design<-x$design[r,] x$call<-sys.call(-1) x } with.svyimputationList<-function (data, expr, fun, ..., multicore=getOption("survey.multicore")) { pf <- parent.frame() if (multicore && !requireNamespace("parallel",quietly=TRUE)) multicore<-FALSE if (!is.null(match.call()$expr)) { expr <- substitute(expr) expr$design<-as.name(".design") if (multicore){ results <- parallel::mclapply(data$designs, function(.design) { eval(expr, list(.design=.design),enclos=pf) } ) } else{ results <- lapply(data$designs, function(.design) { eval(expr, list(.design=.design),enclos=pf) } ) } } else { results <- lapply(data$designs, fun, ...) } if (all(sapply(results, inherits, what = "imputationResult"))) { class(results) <- "imputationResultList" results$call <- sys.call(-1) } else { attr(results, "call") <- sys.call(-1) } results } withReplicates.svyimputationList<-function(design, theta,...,return.replicates=FALSE){ if (!inherits(design$designs[[1]],"svyrep.design")) stop("not a replicate-weights design") m<-match.call() m[[1]]<-as.name("withReplicates") rval<-eval(bquote(with(design, .(m)))) attr(rval,"call")<-m rval } with.svyDBimputationList<-function (data, expr, ..., multicore=getOption("survey.multicore")) { pf <- parent.frame() if (!is.null(match.call()$expr)) { expr <- substitute(expr) expr$design<-as.name(".design") if (multicore && !requireNamespace("parallel")) multicore <-FALSE if (multicore){ results<-parallel::mclapply(data$imputations, function(tablename) { close(data) .design<-data$design db<-data$db db$tablename<-tablename .design$db<-db .design<-open(.design) rval<-eval(expr, list(.design=.design),enclos=pf) close(.design) rval } ) } else { results <- lapply(data$imputations, function(tablename) { .design<-data$design db<-data$db db$tablename<-tablename .design$db<-db eval(expr, list(.design=.design),enclos=pf) } ) } } attr(results, "call") <- sys.call(-1) results } update.svyDBimputationList<-function(object, ...){ dots <- substitute(list(...))[-1] newnames <- names(dots) updates<-lapply(dots, function(dot){ list(inputs=all.vars(dot),expression=dot) }) if (is.null(object$design$updates)) object$design$updates<-list(updates) else object$design$updates<-c(object$design$updates, list(updates)) object } update.svyimputationList<-function(object, ...){ dots <- substitute(list(...))[-1] newnames <- names(dots) for (i in seq(along = object$designs)) { for (j in seq(along = dots)) { object$designs[[i]]$variables[, newnames[j]] <- eval(dots[[j]], object$designs[[i]]$variables, parent.frame()) } } object } close.svyDBimputationList<-function(con,...){ dbcon<-con$db$connection DBI::dbDisconnect(dbcon) invisible(con) } open.svyDBimputationList<-function(con,...){ dbdriver<-DBI::dbDriver(con$db$dbtype) con$db$connection<-DBI::dbConnect(dbdriver,dbname=con$db$dbname,...) con } survey/R/chisqsum.R0000644000176200001440000000553514153237064014004 0ustar liggesusers pchisqsum<-function(x,df,a,lower.tail=TRUE, method=c("satterthwaite","integration","saddlepoint")){ satterthwaite<-function(a,df){ if(any(df>1)){ a<-rep(a,df) } tr<-mean(a) tr2<-mean(a^2)/(tr^2) list(scale=tr*tr2, df=length(a)/tr2) } ## chisqphi<-function(t, df=1,a=1){ ## (1-(0+2i)*a*t)^(-df/2) ## } ## make.integrand<-function(x,DF,A){ ## m<-length(DF) ## function(t){ ## n<-length(t) ## tmp<-matrix(chisqphi(rep(t,each=m),rep(DF,n),rep(A,n) ),ncol=n) ## phi<-apply(tmp,2,prod) ## rval<-Im(phi*exp(-(0+1i)*t*x)/(pi*t)) ## rval[t==0]<-x/(pi) ## rval ## } ## } method<-match.arg(method) sat<-satterthwaite(a,df) guess<-pchisq(x/sat$scale,sat$df,lower.tail=lower.tail) if (method=="satterthwaite") return(guess) method<-match.arg(method) if (method=="integration" && !(requireNamespace("CompQuadForm",quietly=TRUE))){ warning("Package 'CompQuadForm' not found, using saddlepoint approximation") method<-"saddlepoint" } abstol<-guess/1000 abstol<-pmax(1e-9, abstol) reltol<-rep(1/1000,length(abstol)) if (method=="integration"){ if (any(a<=0)){ for(i in seq(length=length(x))){ f<-CompQuadForm::davies(x[i],a,df,acc=1e-7) if (f$ifault>0) warning("Probable loss of accuracy ") guess[i]<-f$Qq } if(any(guess<1e-6)) warning("Probable loss of accuracy ") } else{ for(i in seq(length=length(x))){ ## version 1.4.2 of CompQuadForm changed the *name* of the result. Grr. temp<-CompQuadForm::farebrother(x[i],a,df) guess[i]<-if ("Qq" %in% names(temp)) temp$Qq else temp$res } if(any(guess<1e-9)) warning("Probable loss of accuracy ") } if (lower.tail) guess<-1-guess return(guess) } else if (method=="saddlepoint"){ lambda<-rep(a,df) sad<-sapply(x,saddle,lambda=lambda) if (lower.tail) sad<-1-sad guess<-ifelse(is.na(sad),guess,sad) return(guess) } } saddle<-function(x,lambda){ d<-max(lambda) lambda<-lambda/d x<-x/d k0<-function(zeta) -sum(log(1-2*zeta*lambda))/2 kprime0<-function(zeta) sapply(zeta, function(zz) sum(lambda/(1-2*zz*lambda))) kpprime0<-function(zeta) 2*sum(lambda^2/(1-2*zeta*lambda)^2) n<-length(lambda) if (any(lambda < 0)) { lmin <- max(1/(2 * lambda[lambda < 0])) * 0.99999 } else if (x>sum(lambda)){ lmin <- -0.01 } else { lmin<- -length(lambda)/(2*x) } lmax<-min(1/(2*lambda[lambda>0]))*0.99999 hatzeta <- uniroot(function(zeta) kprime0(zeta) - x, lower = lmin, upper = lmax, tol = 1e-08)$root w<-sign(hatzeta)*sqrt(2*(hatzeta*x-k0(hatzeta))) v<-hatzeta*sqrt(kpprime0(hatzeta)) if (abs(hatzeta)<1e-4) NA else pnorm(w+log(v/w)/w, lower.tail=FALSE) } survey/R/svycdf.R0000644000176200001440000000262113517345031013434 0ustar liggesuserssvycdf<-function(formula,design,na.rm=TRUE,...) UseMethod("svycdf",design) svycdf.default<-function(formula, design,na.rm=TRUE,...){ if (inherits(formula, "formula")) x <- model.frame(formula, model.frame(design), na.action = na.pass) else if (typeof(formula) %in% c("expression", "symbol")) x <- eval(formula, model.frame(design, na.action = na.pass)) else x<-formula if (na.rm) { nas <- rowSums(is.na(x)) x <- x[nas == 0, , drop = FALSE] } rval<-vector("list",ncol(x)) names(rval)<-names(x) for(i in 1:ncol(x)){ xx<-x[,i] w <- weights(design,type="sampling")[nas==0] oo<-order(xx) cum.w<-cumsum(w[oo])/sum(w) cdf <- approxfun( xx[oo],cum.w, method = "constant", yleft =0, yright =1,ties="max") class(cdf)<-"stepfun" call.i<-match.call() call.i$formula<-as.formula(paste("~",names(x)[i])) attr(cdf,"call")<-call.i rval[[names(x)[i]]]<-cdf } class(rval)<-"svycdf" cc<-sys.call() cc[[1]]<-as.name(.Generic) attr(rval,"call")<-cc rval } print.svycdf<-function(x,...){ cat("Weighted ECDFs: ") print(attr(x,"call")) invisible(x) } plot.svycdf<-function(x,xlab=NULL,...){ if(is.null(xlab)) xlab<-names(x) else if (length(xlab)==1) xlab<-rep(xlab,length(names(x))) for (i in 1:length(x)) plot(x[[i]], xlab =xlab[i], ...) } survey/R/ftable.svystat.R0000755000176200001440000000372513517345031015120 0ustar liggesusers ftable.svystat<-function(x, rownames=NULL, ...){ m<-cbind(coef(x),SE(x)) if (is.null(rownames)) return(as.table(m)) statname<-if (is.list(x)) attr(x[[1]],"statistic") else attr(x,"statistic") deff<-attr(x,"deff") has.deff<-!is.null(deff) if (has.deff) m<-cbind(m,diag(deff)) rowdim<-sapply(rownames,length) if (has.deff){ mm<-array(m,dim=c(rowdim,NCOL(m)), dimnames=c(as.list(rownames), list(c(statname,"SE","Deff")))) ftable(mm,row.vars=length(rowdim)+0:1) } else { mm<-array(m,dim=c(rowdim,NCOL(m)), dimnames=c(as.list(rownames), list(c(statname,"SE")))) ftable(mm,row.vars=length(rowdim)+0:1) } } ftable.svrepstat<-ftable.svystat ftable.svyby <- function (x, ...) { info <- attr(x, "svyby") margins <- info$margins dimnames <- lapply(x[, margins, drop = FALSE], levels) dims <- sapply(dimnames, length) dims <- c(dims, variable = info$nstats) senames<-c(se="SE",cv="cv",cvpct="cv%",var="Var")[info$vartype] if (info$vars || info$deffs) { dims <- c(dims, 1 + info$vars + info$deffs) dimnames <- c(dimnames, list(sub("^statistic\\.(.*)$", "\\1", info$variables)), list(c(info$statistic, if (info$vars) senames, if (info$deffs) "DEff"))) } else if (info$nstats == 1) { dimnames <- c(dimnames, list(info$statistic)) } else { dimnames <- c(dimnames, list(info$variables)) } ## fix by Sergio Calva for ordering bug. x <- x[do.call("order",x[,rev(margins),drop = FALSE]),] rval <- array(as.matrix(x[, -margins, drop = FALSE]), dim = dims, dimnames = dimnames) ftable(rval, row.vars = c(1, length(dim(rval)))) } if(FALSE){ odfTable.svystat <- function(x,...) odfTable(as.data.frame(x),...) odfTable.table <- odfTable.matrix } survey/R/regtest.R0000755000176200001440000003122714411724132013617 0ustar liggesusers##deviance methods not exported, used by method="LRT" deviance.svycoxph<-function(object,...) if(length(object$ll)==2) 2 * (object$ll[1] - object$ll[2]) else 0 deviance.coxph<-function(object,...) if(length(object$loglik)==2) 2 * (object$loglik[1] - object$loglik[2]) else 0 explicit1<-function(formula){ if (length(formula)==1) return(formula==1) if (!(formula[[1]]=="+" || formula[[1]]=="*" || formula[[1]]=="/" || formula[[1]]=="^"|| formula[[1]]=="~")) return(FALSE) if (length(formula)==3){ (formula[[2]]==1) || explicit1(formula[[2]]) || explicit1(formula[[3]]) } else { (formula[[2]]==1) || explicit1(formula[[2]]) } } regTermTest<-function(model, test.terms, null=NULL, df=NULL, method=c("Wald","WorkingWald","LRT"), lrt.approximation="saddlepoint"){ method<-match.arg(method) canonicalOrder<-function(term){ tt<-strsplit(term,":") tt<-lapply(tt,sort) sapply(tt,paste,collapse=":") } if(inherits(test.terms,"formula")){ test_intercept<-explicit1(test.terms) test.terms<-attr(terms(test.terms),"term.labels") } else test_intercept<-FALSE okbeta<-!is.na(coef(model,na.rm=FALSE)) ## na.rm for svyglm tt<-attr(terms(model),"term.labels") aa<-attr(model.matrix(model),"assign")[okbeta] if((inherits(model,"svyloglin") || inherits(model,"svyolr")) && attr(terms(model),"intercept")){ aa<-aa[-1] } index<-which(aa %in% match(canonicalOrder(test.terms),canonicalOrder(tt))) if (any(is.na(index))) stop("Terms didn't match:",canonicalOrder(test.terms),canonicalOrder(tt)) if (test_intercept){ if (attr(terms(model),"intercept")) index<-unique(c(1,index)) else stop("model does not have an intercept") } beta<-coef(model)[index] if (!is.null(null)) beta<-beta-null V<-vcov(model)[index,index] ## this should be rewritten as methods, but that's not happening any time soon. if (is.null(df)){ if (inherits(model,"svyglm")) df<-model$df.residual else if (inherits(model, "svycoxph")) df<-model$degf.resid else if (inherits(model,"lm")) df<-model$df.residual else if (inherits(model,"coxph")) df<-model$n-length(coef(model)) else if (inherits(model, "MIresult")) df<-min(model$df[index]) else if (inherits(model,"svyloglin")) df<-model$df+1-length(index) else if (inherits(model, "svyolr")) df<-model$df.residual else df<-length(resid(model))-length(coef(model)) } if (method %in% c("LRT","WorkingWald")){ if (inherits(model,"svyglm")) V0<-model$naive.cov else if (inherits(model, "svycoxph")) V0<-model$inv.info else if (inherits(model,"lm")) V0<-vcov(model) else if (inherits(model,"coxph")){ if (is.null(model$naive.var)) V0<-model$var else V0<-model$naive.var } else if (inherits(model,"svyolr")) { V0<-solve(model$Hess) } else stop("method='LRT' not supported for this model") V0<-V0[index,index] if (test_intercept){ test.formula<-make.formula(c(1,test.terms))[[2]] } else { test.formula<-make.formula(test.terms)[[2]] } if (!("formula" %in% names(model$call))) names(model$call)[[2]]<-"formula" if (method=="LRT"){ model0<-eval(bquote(update(.(model), .~.-(.(test.formula)),design=.(model$survey.design),subset=NULL)), environment(formula(model))) ## same missing data if (inherits(model,"svyglm")){ rescale<-mean(model$prior.weights)/mean(model0$prior.weights) } else if (inherits(model,"svycoxph")){ rescale<-mean(model$weights)/mean(model0$weights) } else rescale<-1 ##FIXME -- probably want a generic drop_and_refit function chisq<-deviance(model0)*rescale-deviance(model) } else { chisq<-beta%*%solve(V0)%*%beta } misspec<-eigen(solve(V0)%*%V, only.values=TRUE)$values if (df==Inf) p<-pchisqsum(chisq,rep(1,length(misspec)),misspec,method=lrt.approximation,lower.tail=FALSE) else p<-pFsum(chisq,rep(1,length(misspec)),misspec,ddf=df,method=lrt.approximation,lower.tail=FALSE) rval<-list(call=sys.call(),mcall=model$call,chisq=chisq, df=length(index),test.terms=test.terms, p=p,lambda=misspec,ddf=df) if (method=="LRT") class(rval)<-"regTermTestLRT" else class(rval)<- "regTermTestWW" return(rval) } chisq<-beta%*%solve(V)%*%beta if (df1) cat("(scale factors: ",signif(x$lambda/mean(x$lambda),2),")") else cat("df=1") if (!is.null(x$ddf) && is.finite(x$ddf)) cat("; denominator df=",x$ddf) cat("\n") invisible(x) } print.regTermTestWW<-function(x,...){ if (is.null(x$ddf) || x$ddf==Inf) cat("Working (Rao-Scott) Wald test for ") else cat("Working (Rao-Scott+F) for ") cat(x$test.terms) cat("\n in ") print(x$mcall) chisq<-x$chisq/mean(x$lambda) cat("Working Wald statistic = ",chisq, 'p=',format.pval(x$p),"\n") if (length(x$lambda)>1) cat("(scale factors: ",signif(x$lambda/mean(x$lambda),2),")") else cat("df=1") if (!is.null(x$ddf) && is.finite(x$ddf)) cat("; denominator df=",x$ddf) cat("\n") invisible(x) } svycontrast<-function(stat, contrasts,add=FALSE,...) UseMethod("svycontrast") match.names <- function(nms,contrasts){ l<-length(nms) ll<-sapply(contrasts,length) if (l==0) stop("No names to match") if (length(unlist(sapply(contrasts,names)))==0) return(contrasts) if( !all( unlist(sapply(contrasts,names)) %in% nms)) stop("names not matched") lapply(contrasts, function(con) { r<-numeric(l) names(r)<-nms r[names(con)]<-con r }) } contrast<-function(coef,var,contrasts, influence=NULL){ nas<-is.na(var[,1]) drop<-nas & apply(contrasts,2,function(v) all(v==0)) if(any(drop)){ contrasts<-contrasts[,!drop,drop=FALSE] coef<-coef[!drop] var<-var[!drop,!drop,drop=FALSE] } if (any(is.na(coef))){ badin<-is.na(coef) bad<-((contrasts!=0)%*%is.na(coef))>0 rval<-rep(NA,NROW(contrasts)) rval[!bad]<-contrasts[!bad,!badin,drop=FALSE]%*%coef[!badin] v<-matrix(NA,length(rval),length(rval)) v[!bad,!bad]<-contrasts[!bad,!badin,drop=FALSE]%*%var[!badin,!badin,drop=FALSE]%*%t(contrasts[!bad,!badin,drop=FALSE]) dimnames(v)<-list(names(rval),names(rval)) rval<-drop(rval) if(!is.null(influence)){ attr(rval,"influence")<- influence[,!badin,drop=FALSE]%*%t(contrasts[!bad,!badin,drop=FALSE]) } attr(rval, "var")<-v } else{ rval<-drop(contrasts%*%coef) v<-contrasts%*%var%*%t(contrasts) dimnames(v)<-list(names(rval),names(rval)) if(!is.null(influence)){ attr(rval,"influence")<- influence%*%t(contrasts) } attr(rval,"var")<-v } rval } addQuote<-function(contrasts, original){ ll<-as.list(original) names(ll)<-original ll<-lapply(ll, as.name) c(contrasts,ll) } addLin<-function(contrasts, original){ id<-diag(length(original)) dimnames(id)<-list(original,original) rbind(contrasts,id) } svycontrast.svystat<-function(stat, contrasts,add=FALSE,...){ if (!is.list(contrasts)) contrasts<-list(contrast=contrasts) if (is.language(contrasts[[1]])){ if(add){ contrasts<-addQuote(contrasts,names(coef(stat))) } rval<-nlcon(contrasts,as.list(coef(stat)), vcov(stat), attr(stat,"influence")) class(rval)<-"svrepstat" attr(rval,"statistic")<-"nlcon" return(rval) } contrasts<-match.names(names(coef(stat)),contrasts) contrasts<-do.call(rbind,contrasts) if (add) contrasts<-addLin(contrasts, names(coef(stat))) coef<-contrast(coef(stat),vcov(stat),contrasts, attr(stat,"influence")) class(coef)<-"svystat" attr(coef,"statistic")<-"contrast" coef } svycontrast.svyolr<-function(stat, contrasts,add=FALSE,...){ if (!is.list(contrasts)) contrasts<-list(contrast=contrasts) if (is.language(contrasts[[1]])){ if(add){ contrasts<-addQuote(contrasts,names(coef(stat))) } rval<-nlcon(contrasts,as.list(c(coef(stat),stat$zeta)), vcov(stat)) class(rval)<-"svystat" attr(rval,"statistic")<-"nlcon" return(rval) } contrasts <- match.names(names(coef(stat)), contrasts) contrasts<-do.call(rbind,contrasts) if (add) contrasts<-addLin(contrasts, names(coef(stat))) coef<-contrast(as.vector(as.matrix(coef(stat))), vcov(stat),contrasts) class(coef)<-"svystat" attr(coef,"statistic")<-"contrast" coef } svycontrast.svrepstat<-function(stat, contrasts,add=FALSE,...){ if (!is.list(contrasts)) contrasts<-list(contrast=contrasts) if (is.language(contrasts[[1]])){ if(add){ contrasts<-addQuote(contrasts,names(coef(stat))) } if (is.list(stat) && !is.null(stat$replicates)){ ##replicates rval<-list(nlcon=nlcon(contrasts,as.list(coef(stat)),varmat=NULL)) reps<-as.matrix(stat$replicates) colnames(reps)<-names(coef(stat)) xreps<-apply(reps,1, function(repi) nlcon(datalist=as.list(repi), exprlist=contrasts, varmat=NULL)) rval$replicates<-if(is.matrix(xreps)) t(xreps) else as.matrix(xreps) attr(rval$nlcon,"var")<-svrVar(rval$replicates, scale=attr(stat$replicates,"scale"), rscales=attr(stat$replicates,"rscales"),mse=attr(stat$replicates,"mse"), coef=rval$nlcon) attr(rval$nlcon,"statistic")<-"nlcon" } else { rval<-nlcon(contrasts,as.list(coef(stat)), vcov(stat)) attr(rval,"statistic")<-"nlcon" } class(rval)<-"svrepstat" return(rval) } else { contrasts<-match.names(names(coef(stat)), contrasts) contrasts<-do.call(rbind,contrasts) if (add) contrasts<-addLin(contrasts, names(coef(stat))) coef<-contrast(coef(stat), vcov(stat), contrasts) attr(coef,"statistic")<-"contrast" if (is.list(stat) && !is.null(stat$replicates)){ coef<-list(contrast=coef, replicates=tcrossprod(stat$replicates, contrasts)) } class(coef)<-"svrepstat" coef } } nlcon<-function(exprlist, datalist, varmat, influence=NULL){ if (!is.list(exprlist)) exprlist<-list(contrast=exprlist) dexprlist<-lapply(exprlist, function(expr) deriv(expr, names(datalist))[[1]]) values<-lapply(dexprlist, function(dexpr) eval(do.call(substitute, list(dexpr,datalist)))) if (is.null(varmat)) return(do.call(c,values)) jac<-do.call(rbind,lapply(values, function(value) attr(value,"gradient"))) var<-jac%*%varmat%*%t(jac) values<-do.call(c, values) dimnames(var)<-list(names(values),names(values)) attr(values, "var")<-var if(!is.null(influence)){ attr(values,"influence")<-influence%*%t(jac) } values } svycontrast.svyglm<-svycontrast.svystat svycontrast.svycoxph<-svycontrast.svystat svycontrast.svrepglm<-svycontrast.svrepstat svycontrast.svrepcoxph<-svycontrast.svrepstat svycontrast.svyby<-svycontrast.svystat svycontrast.default<-svycontrast.svystat svycontrast.svyby<-function(stat, contrasts,...){ if(!is.null(r<-attr(stat, "replicates"))){ s<-coef(stat) attr(s,"var")<-vcov(stat) attr(s,"statistic")<-attr(stat,"svyby")$statistic repstat<-list(stat=s, replicates=r) class(repstat)<-"svrepstat" svycontrast(repstat, contrasts,...) } else NextMethod() ## default } survey/R/smoothUnit.R0000644000176200001440000000542114571730126014314 0ustar liggesuserssvysmoothUnit <- function(formula, domain, design, family = c("gaussian", "binomial"), X.pop = NULL, adj.mat = NULL, domain.size = NULL, pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3, level = .95, n.sample = 250, return.samples = FALSE, X.pop.weights = NULL,...) { UseMethod("svysmoothUnit",design) } svysmoothUnit.survey.design <- function(formula, domain, design, family = c("gaussian", "binomial"), X.pop = NULL, adj.mat = NULL, domain.size = NULL, pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3, level = .95, n.sample = 250, return.samples = FALSE, X.pop.weights = NULL,...) { if (!requireNamespace("SUMMER", quietly = TRUE)) { stop( "Package \"SUMMER\" must be installed to use this function.", call. = FALSE ) } family<-match.arg(family) rval<-SUMMER::smoothUnit(formula = formula, domain = domain, design = design, family = family, X.pop = X.pop, adj.mat = adj.mat, domain.size = domain.size, pc.u = pc.u, pc.alpha = pc.alpha, pc.u.phi = pc.u.phi, pc.alpha.phi = pc.alpha.phi, level = level, n.sample = n.sample, return.samples = return.samples, X.pop.weights = X.pop.weights) rval$call<-sys.call(-1) rval } svysmoothUnit.default <- function(formula, domain, design, family = c("gaussian", "binomial"), X.pop = NULL, adj.mat = NULL, domain.size = NULL, pc.u = 1, pc.alpha = 0.01, pc.u.phi = 0.5, pc.alpha.phi = 2/3, level = .95, n.sample = 250, return.samples = FALSE, X.pop.weights = NULL,...) { stop("svysmoothUnit is only available for survey.design objects") } survey/R/svyivreg.R0000644000176200001440000000340514204276141014014 0ustar liggesusers svyivreg<-function(formula, design, ...) UseMethod("svyivreg",design) svyivreg.survey.design<-function(formula, design,...){ .data<-model.frame(design) .data$.weights<-weights(design,"sampling") .weights<-NULL ## make CMD check happy ##estfun<-get("estfun",mode="function") model<- AER::ivreg(formula, data=.data, weights=.weights) U<-estfun.ivreg(model)/weights(design,"sampling") n<-NROW(U) infl<- U%*%model$cov.unscaled v<-vcov(svytotal(infl, design)) model$invinf<-model$cov.unscaled model$cov.unscaled<-v model$df.residual<-degf(design)+1-length(coef(model)) model$sigma<-model$sigma/sqrt(mean(weights(design,"sampling"))) model$call<-sys.call(-1) class(model)<-c("svyivreg","ivreg") model } summary.svyivreg<-function(object, df = NULL, ...){ V<-vcov(object) class(object)<-"ivreg" summary(object, vcov.=V, df=df, diagnostics=FALSE,...) } vcov.svyivreg<-function(object,...) object$cov.unscaled svyivreg.svyrep.design<-function(formula, design,return.replicates=FALSE,...){ .pweights<-NULL ## make CMD check happy withReplicates(design, return.replicates=return.replicates, function(.weights, .data){ .data$.pweights<-.weights m<-AER::ivreg(formula,data= .data, weights=.pweights) coef(m) }) } estfun.ivreg<-function (x, ...) { xmat <- model.matrix(x) if (any(alias <- is.na(coef(x)))) xmat <- xmat[, !alias, drop = FALSE] wts <- weights(x) if (is.null(wts)) wts <- 1 res <- residuals(x) rval <- as.vector(res) * wts * xmat attr(rval, "assign") <- NULL attr(rval, "contrasts") <- NULL return(rval) } survey/R/loglin.R0000644000176200001440000001471314526736254013443 0ustar liggesuserssvyloglin<-function(formula,design,...) UseMethod("svyloglin",design) withOptions<-function(optlist,expr){ oldopt<-options(optlist) on.exit(options(oldopt)) expr<-substitute(expr) eval.parent(expr) } tr<-function(m)sum(diag(m)) tr2<-function(m) sum(m*m) svyloglin.survey.design<-function(formula,design,...){ if (length(formula)!=2) stop("needs a one-sided formula") mdata<-model.frame(design)[,all.vars(formula)] mf<-model.frame(formula,mdata,na.action=na.pass) n<-as.numeric(nrow(mf)) hatp<-svymean(~I(do.call(interaction,mf)),design,na.rm=TRUE) dat<-do.call(expand.grid,lapply(mdata,function(x) sort(unique(x)))) dat<-as.data.frame(lapply(dat,as.factor)) dat$y<-coef(hatp)*n ff<-update(formula, y~.) m1<-withOptions(list(contrasts=c("contr.sum","contr.poly")), glm(ff, data=dat,family=quasipoisson) ) P1<-(diag(fitted(m1)/n)-tcrossprod(fitted(m1)/n))/n V<-vcov(hatp) XX<-model.matrix(m1)[,-1,drop=FALSE] XX<-sweep(XX,2,colMeans(XX)) Vtheta<-solve(t(XX)%*%P1%*%XX)%*%(t(XX)%*%V%*%XX)%*%solve(t(XX)%*%P1%*%XX)/(n*n) rval<-list(model=m1, var=Vtheta, prob.table=hatp,df.null=degf(design),n=n) call<-sys.call() call[[1]]<-as.name(.Generic) rval$call<-call class(rval)<-"svyloglin" rval } print.svyloglin<-function(x,...) {cat("Loglinear model: ");print(x$call)} coef.svyloglin<-function(object,...,intercept=FALSE){ if (intercept) coef(object$model) else coef(object$model)[-1] } vcov.svyloglin<-function(object,...) object$var deviance.svyloglin<-function(object,...) deviance(object$model) degf.svyloglin<-function(design,...) length(design$prob.table)-length(coef(design))-1 terms.svyloglin<-function(x,...) terms(x$model,...) model.matrix.svyloglin<-function(object,...) model.matrix(object$model,...) update.svyloglin<-function(object, formula,...){ n<-object$n model<-withOptions(list(contrasts=c("contr.sum","contr.sum")), update(object$model, formula,data=object$model$model)) P1<-(diag(fitted(model)/n)-tcrossprod(fitted(model)/n))/n V<-vcov(object$prob.table) XX<-model.matrix(model)[,-1,drop=FALSE] XX<-sweep(XX,2,colMeans(XX)) A<-solve(t(XX)%*%P1%*%XX) B<-t(XX)%*%V%*%XX Vtheta<-A%*%B%*%A/(n*n) rval<-list(model=model, var=Vtheta, prob.table=object$prob.table, df.null=object$df.null,n=n) call<-sys.call() call[[1]]<-as.name(.Generic) rval$call<-call class(rval)<-"svyloglin" rval } anova.svyloglin<-function(object,object1,...,integrate=FALSE){ if(length(coef(object1))="4.4.0") FALSE else NULL an<-anova(m0,m1,test=no_tests) dev<-an$Deviance[2] if (integrate){ pdev<-pchisqsum(dev,rep(1,ncol(wX2)), a=eigen(Delta,only.values=TRUE,symmetric=TRUE)$values, lower.tail=FALSE,method="integration") } else pdev<-NA pdev1<-pchisq(dev*ncol(wX2)/tr(Delta),df=ncol(wX2),lower.tail=FALSE) pdev2a<-pf(dev/tr(Delta), tr(Delta)^2/tr2(Delta), dfnull*tr(Delta)^2/tr2(Delta), lower.tail=FALSE) pdevsad<-pchisqsum(dev,rep(1,ncol(wX2)), a=eigen(Delta,only.values=TRUE)$values, lower.tail=FALSE,method="saddlepoint") pearson<-n*sum( (pi1-pi0)^2/pi0 ) if (integrate){ pearsonp<-pchisqsum(pearson, rep(1,ncol(wX2)), a=eigen(Delta,only.values=TRUE,symmetric=TRUE)$values, lower.tail=FALSE,method="integration") } else pearsonp<-NA prs1<-pchisq(pearson*ncol(wX2)/tr(Delta),df=ncol(wX2),lower.tail=FALSE) prs2<-pchisq(pearson*ncol(wX2)/tr(Delta),df=tr(Delta)^2/tr2(Delta),lower.tail=FALSE) prs2a<-pf(pearson/tr(Delta), ncol(wX2)*tr(Delta)^2/tr2(Delta),ncol(wX2)*dfnull*tr(Delta)^2/tr2(Delta), lower.tail=FALSE) pchisqsad<-pchisqsum(pearson, rep(1,ncol(wX2)), a=eigen(Delta,only.values=TRUE,symmetric=TRUE)$values, lower.tail=FALSE,method="saddlepoint") rval<-list(an, dev=list(dev=dev, p=c(pdev,pdev1,pdev2a,pdevsad)), score=list(chisq=pearson,p=c(pearsonp,prs1,prs2a,pchisqsad)), integrate=integrate,a=eigen(Delta,only.values=TRUE,symmetric=TRUE)$values,p=ncol(wX2)) class(rval)<-"anova.svyloglin" rval } print.anova.svyloglin<-function(x,pval=c("F","saddlepoint","lincom","chisq"),...){ cat(attr(x[[1]],"heading"),"\n") pval<-match.arg(pval) if (pval=="lincom" && !x$integrate){ x$dev$p[1]<-pchisqsum(x$dev$dev, rep(1,x$p), a=x$a, lower.tail=FALSE,method="integration") x$score$p[1]<-pchisqsum(x$score$chisq, rep(1,x$p), a=x$a, lower.tail=FALSE,method="integration") } cat("Deviance=",x$dev$dev,"p=", switch(pval,lincom=x$dev$p[1], saddlepoint=x$dev$p[4], chisq=x$dev$p[2], F=x$dev$p[3]),"\n") cat("Score=",x$score$chisq,"p=", switch(pval,lincom=x$score$p[1], saddlepoint=x$score$p[4], chisq=x$score$p[2], F=x$score$p[3]),"\n") invisible(x) } summary.svyloglin<-function(object,...){ rval<-list(ll=object) class(rval)<-"summary.svyloglin" rval } print.summary.svyloglin<-function(x,...){ print(x$ll) print(cbind(coef=coef(x$ll), se=SE(x$ll), p=2*pnorm(abs(coef(x$ll)/SE(x$ll)),lower.tail=FALSE))) invisible(x) } svyloglin.svyrep.design<-svyloglin.survey.design svyloglin.DBIsvydesign<-function (formula, design, ...) { design$variables <- dropFactor(getvars(formula, design$db$connection, design$db$tablename, updates = design$updates), weights(design)) class(design)<-c("survey.design2","survey.design") rval<-svyloglin(formula,design) rval$call<-sys.call() rval$call[[1]]<-as.name(.Generic) rval } survey/R/dbiupdate.R0000644000176200001440000000536413517615617014120 0ustar liggesusers ## ## stored variable updates. ## updatesInfilter<-function(varlist, updates){ if (is.null(updates)) return(list(varlist=varlist)) n<-length(updates) v<-vector("list",n) for(i in n:1){ if (any(idx<-(varlist %in% names(updates[[i]])))){ v[[i]]<-varlist[idx] ups<-match(v[[i]], names(updates[[i]])) varlist<-unique(c(varlist[!idx], do.call(c, lapply(updates[[i]][ups], "[[", "inputs")))) } } list(varlist=varlist, history=v) } updatesOutfilter<-function(df, varlist,history, updates){ if (is.null(updates)) return(df) if (all(sapply(history,length)==0)) return(df) n<-length(updates) for(i in 1:n){ if (mi<-length(history[[i]])){ outputs<-vector("list", mi) for(j in 1:mi){ idx.j<-match(history[[i]][j],names(updates[[i]])) outputs[[j]]<-eval(updates[[i]][[idx.j]]$expression, df) } names(outputs)<-history[[i]] if (any(mod<-names(df) %in% names(outputs))){ df<-df[,!mod,drop=FALSE] } df<-cbind(df,outputs) } } df[, names(df) %in% varlist,drop=FALSE] } checkConnection<-function(dbconnection, error=TRUE){ if (is(dbconnection,"DBIConnection")) { if (!DBI::dbIsValid(dbconnection)) if (error) stop("Database connection is closed") else return(FALSE) } invisible(TRUE) } getvars<-function (formula, dbconnection, tables, db.only = TRUE, updates=NULL, subset=NULL) { checkConnection(dbconnection) if (is.null(formula)) return(NULL) if (inherits(formula, "formula")) { var0<- all.vars(formula) } else if (is.character(formula)){ var0<-formula } else { return(formula) } infilter<-updatesInfilter(var0, updates) if (db.only) { in.db <- infilter$varlist } else { query <- sub("@tab@", tables, "select * from @tab@ limit 1") if (is(dbconnection,"DBIConnection")) oneline <- DBI::dbGetQuery(dbconnection, query) in.db <- infilter$varlist[infilter$varlist %in% names(oneline)] } query <- paste("select", paste(in.db, collapse = ", "), "from", tables) df <- DBI::dbGetQuery(dbconnection, query) if (!is.null(subset)) df<-df[subset,,drop=FALSE] df<-updatesOutfilter(df, var0, infilter$history, updates) is.string <- sapply(df, is.character) if (any(is.string)) { for (i in which(is.string)) df[[i]] <- as.factor(df[[i]]) } df } update.DBIsvydesign<-function(object, ...){ dots <- substitute(list(...))[-1] newnames <- names(dots) updates<-lapply(dots, function(dot){ list(inputs=all.vars(dot),expression=dot) }) if (is.null(object$updates)) object$updates<-list(updates) else object$updates<-c(object$updates, list(updates)) object } survey/R/newsvyquantile.R0000644000176200001440000002564614411725461015253 0ustar liggesuserssvyquantile<-function(x,design,quantiles,...) UseMethod("svyquantile", design) svyquantile.survey.design <- function (x, design, quantiles, alpha = 0.05, interval.type = c("mean", "beta","xlogit", "asin","score"), na.rm = FALSE, ci=TRUE, se = ci, qrule=c("math","school","shahvaish","hf1","hf2","hf3","hf4","hf5","hf6","hf7","hf8","hf9"), df = NULL, ...) { if (inherits(x, "formula")) x <- model.frame(x, model.frame(design), na.action = na.pass) else if (typeof(x) %in% c("expression", "symbol")) x <- eval(x, model.frame(design, na.action = na.pass)) if (na.rm) { nas <- rowSums(is.na(x)) design <- design[nas == 0, ] if (length(nas) > length(design$prob)) x <- x[nas == 0, , drop = FALSE] else x[nas > 0, ] <- 0 } if(is.null(df)) df<-degf(design) if (is.character(qrule)){ qrulename<-paste("qrule",match.arg(qrule),sep="_") qrule<-get(qrulename, mode="function") } qcrit<-if(df==Inf) qnorm else function(...) qt(...,df=df) interval.type<-match.arg(interval.type) if (interval.type=="score"){ ci_fun<-ffullerCI } else { ci_fun<-function(...) woodruffCI(...,method=interval.type) } w<-weights(design) rvals<-lapply(x, function(xi){ r<-t(sapply(quantiles, function(p){ qhat<-qrule(xi,w,p) if(ci){ ci<-ci_fun(xi,qhat,p,design,qrule,alpha,df) names(ci)<-c(round(100*alpha/2,2),round(100-100*alpha/2,2)) c(quantile=qhat,ci=ci, se=as.numeric(diff(ci)/(2*qcrit(1-alpha/2))) ) } else { c(quantile=qhat) } } )) if(!ci) colnames(r)<-quantiles else rownames(r)<-quantiles r } ) attr(rvals,"hasci")<-ci class(rvals)<-"newsvyquantile" rvals } svyquantile.svyrep.design <- function (x, design, quantiles, alpha = 0.05, interval.type = c("mean", "beta","xlogit", "asin","quantile"), na.rm = FALSE, ci=TRUE, se = ci, qrule=c("math","school","shahvaish","hf1","hf2","hf3","hf4","hf5","hf6","hf7","hf8","hf9"), df = NULL, return.replicates=FALSE,...) { interval.type <- match.arg(interval.type) if (design$type %in% c("JK1", "JKn") && interval.type == "quantile") warning("Jackknife replicate weights may not give valid standard errors for quantiles") if (design$type %in% "other" && interval.type == "quantile") message("Not all replicate weight designs give valid standard errors for quantiles.") if (inherits(x, "formula")) x <- model.frame(x, design$variables, na.action = if (na.rm) na.pass else na.fail) else if (typeof(x) %in% c("expression", "symbol")) x <- eval(x, design$variables) if (na.rm) { nas <- rowSums(is.na(x)) design <- design[nas == 0, ] if (length(nas) > length(design$prob)) x <- x[nas == 0, , drop = FALSE] else x[nas > 0, ] <- 0 } if (is.character(qrule)){ qrulename<-paste("qrule",match.arg(qrule),sep="_") qrule<-get(qrulename, mode="function") } if (is.null(df)) df <- degf(design) if (df == Inf) qcrit <- qnorm else qcrit <- function(...) qt(..., df = df) w<-weights(design,"sampling") if (interval.type=="quantile"){ ci_fun<-function(...) repCI(...,return.replicates=return.replicates) } else { if(return.replicates) warning("return.replicates=TRUE only implemented for interval.type='quantile'") ci_fun<- function(...) woodruffCI(..., method = interval.type) } if ((interval.type=="quantile") && return.replicates){ rvals<-lapply(x, function(xi){ lapply(quantiles, function(p){ qhat<-qrule(xi,w,p) ci<-ci_fun(xi,qhat,p,design,qrule,alpha,df) list(quantile=qhat, replicates=attr(ci,"replicates")) } ) } ) ests<-sapply(rvals, function(v) sapply(v, function(qi) qi$quantile)) ests <- matrix(ests, nrow = length(quantiles), ncol = length(rvals), dimnames=list(paste("q",round(quantiles,2),sep=""), names(x))) attr(ests, "scale") <- design$scale attr(ests, "rscales") <- design$rscales attr(ests, "mse") <- design$mse reps<-sapply(rvals, function(v) sapply(v, function(qi) qi$replicates)) reps <- matrix(reps, ncol = nrow(ests) * ncol(ests)) colnames(reps) <- as.vector(outer(names(x), paste("q",round(quantiles,2),sep=""), paste)) rval <- ests attr(rval,"statistic") <- 'quantiles' vv <- svrVar(reps, design$scale, design$rscales, mse=design$mse, coef=ests) attr(rval,"var") <- matrix(diag(vv), nrow = nrow(ests), ncol = ncol(ests), dimnames=list(paste("q",round(quantiles,2),sep=""), names(x))) rval<-list(quantile=rval,replicates=reps) class(rval)<-"svrepstat" return(rval) } else { rvals<-lapply(x, function(xi){ r<- t(sapply(quantiles, function(p){ qhat<-qrule(xi,w,p) if(ci){ ci<-ci_fun(xi,qhat,p,design,qrule,alpha,df) names(ci)<-c(round(100*alpha/2,2),round(100-100*alpha/2,2)) c(quantile=qhat,ci=ci, se=unname(diff(ci)/(2*qcrit(1-alpha/2))) )} else{ c(quantile=qhat) } } )) if(!ci) colnames(r)<-quantiles else rownames(r)<-quantiles r } ) attr(rvals,"hasci")<-ci | se class(rvals)<-"newsvyquantile" } rvals } SE.newsvyquantile<-function(object,...) { if(!attr(object,"hasci")) stop("object does not have uncertainty estimates") do.call(c,lapply(object,function(ai) ai[,4])) } vcov.newsvyquantile<-function(object,...) { if(!attr(object,"hasci")) stop("object does not have uncertainty estimates") r<-do.call(c,lapply(object,function(ai) ai[,4]))^2 v<-matrix(NA,nrow=length(r),ncol=length(r)) diag(v)<-r v } coef.newsvyquantile<-function(object,...){ if(attr(object,"hasci")) do.call(c,lapply(object,function(ai) ai[,1])) else do.call(c,lapply(object,function(ai) ai[1,])) } confint.newsvyquantile<-function(object,...){ if(!attr(object,"hasci")) stop("object does not have uncertainty estimates") l<-do.call(c,lapply(object,function(ai) ai[,2])) u<-do.call(c,lapply(object,function(ai) ai[,3])) cbind(l,u) } woodruffCI<-function(x, qhat,p, design, qrule,alpha,df,method=c("mean","beta","xlogit","asin")){ method<-match.arg(method) m<-svymean(x<=qhat, design) names(m)<-"pmed" pconfint<-switch(method, mean=as.vector(confint(m, 1, level = 1-alpha, df = df)), xlogit= {xform <- svycontrast(m, quote(log(`pmed`/(1 - `pmed`)))); expit(as.vector(confint(xform, 1, level = 1-alpha, df = df)))}, beta={n.eff <- coef(m) * (1 - coef(m))/vcov(m); rval <- coef(m)[1] n.eff <- n.eff * (qt(alpha/2, nrow(design) - 1)/qt(alpha/2, degf(design)))^2 c(qbeta(alpha/2, n.eff * rval, n.eff * (1 - rval) + 1), qbeta(1 - alpha/2, n.eff * rval + 1, n.eff * (1 - rval))) }, asin={xform <- svycontrast(m, quote(asin(sqrt(`pmed`)))) sin(as.vector(confint(xform, 1, level = 1-alpha, df = df)))^2 } ) lower<-if(is.nan(pconfint[1]) || (pconfint[1]<0)) NaN else qrule(x,weights(design,"sampling"), pconfint[1]) upper<-if(is.nan(pconfint[2])|| (pconfint[2]>1)) NaN else qrule(x,weights(design,"sampling"), pconfint[2]) rval<-c(lower, upper) names(rval)<-c(round(100*alpha/2,1),round(100*(1-alpha/2),1)) rval } ffullerCI<-function(x, qhat, p, design, qrule, alpha, df){ qcrit<-if(df==Inf) qnorm else function(...) qt(...,df=df) U <- function(theta) { ((x > theta) - (1 - p)) } scoretest <- function(theta, qlimit) { umean <- svymean(U(theta), design) umean/SE(umean) - qlimit } iqr <- IQR(x) lowerT <- min(x) + iqr/100 upperT <- max(x) - iqr/100 tol <- 1/(100 * sqrt(nrow(design))) qlow<- uniroot(scoretest, interval = c(lowerT, upperT), qlimit = qcrit(alpha/2, lower.tail = FALSE), tol = tol)$root qup<-uniroot(scoretest, interval = c(lowerT, upperT), qlimit = qcrit(alpha/2, lower.tail = TRUE), tol = tol)$root w<-weights(design) c(qrule(x, w, mean(x<=qlow)), qrule(x, w, mean(x<=qup))) } repCI<-function(x, qhat, p, design, qrule, alpha, df,return.replicates){ qcrit<-if(df==Inf) qnorm else function(...) qt(...,df=df) wrep<-weights(design,"analysis") reps<-apply(wrep,2, function(wi) qrule(x,wi,p)) v<-with(design, svrVar(reps, scale=scale, rscales=rscales, mse=mse,coef=qhat)) ci<- qhat+ c(-1,1)*sqrt(v)*qcrit(1-alpha/2) if (return.replicates) attr(ci,"replicates")<-reps ci } survey/R/gofchisq.R0000644000176200001440000000175513743755164013765 0ustar liggesuserssvygofchisq<-function(formula, p, design,...){ p<-p/sum(p) means<-svytotal(formula, design,...) rval<-chisq.test(means,p=p) nm<-names(coef(means)) ncat<-length(coef(means)) means<-svycontrast(means, list(N=rep(1,ncat)), add=TRUE) pN<-split(cbind(p,0*diag(p)), paste0("p_",nm)) names(pN)<-paste0("p_",nm) means<-svycontrast(means, pN,add=TRUE) for(i in 1:length(nm)){ O<-as.name(nm[i]) E<-as.name(names(pN)[i]) expr<-list(bquote((.(O)-.(E))/sqrt(.(E)))) names(expr)[[1]]<-paste0("X2_",O) means<-svycontrast(means,expr,add=TRUE) } result<-svycontrast(means, rep(c(1,0),c(ncat,2*ncat+1))) lambda<-eigen(vcov(means)[1:ncat,1:ncat])$values tr <- mean(lambda) tr2 <- mean(lambda ^2)/(tr^2) scale = tr * tr2 df = ncat/tr2 rval$parameter<-c(scale=scale,df=df) rval$p.value<-pchisqsum(rval$statistic,rep(1,ncat), lambda,lower.tail=FALSE) rval$data.name<-deparse(formula) rval$method<-"Design-based chi-squared test for given probabilities" rval$lambda<-lambda rval }survey/R/pFsum.R0000644000176200001440000000235413517345031013233 0ustar liggesuserspFsum<-function(x,df,a,ddf=Inf,lower.tail=TRUE,method=c("saddlepoint","integration","satterthwaite"),...){ if (ddf==Inf) return(pchisqsum(x,df=df,a=a,lower.tail=lower.tail,...)) method<-match.arg(method) if (method=="integration" && !(requireNamespace("CompQuadForm",quietly=TRUE))){ warning("Package 'CompQuadForm' not found, using saddlepoint approximation") method<-"saddlepoint" } if (method=="integration"){ int<-CompQuadForm::davies(0,lambda=c(a,-x/ddf), h=c(df,ddf),acc=1e-7) if ( (int$ifault %in% c(0,2))){ rval<-int$Qq } else { rval<-CompQuadForm::davies(0,lambda=c(a,-x/ddf), h=c(df,ddf),acc=1e-5)$Qq } if(lower.tail) return(1-rval) else return(rval) } else if (method %in% c("satterthwaite","saddlepoint")){ if(any(df>1)){ a<-rep(a,df) } tr<-mean(a) tr2<-mean(a^2)/(tr^2) scale=tr*tr2 ndf=length(a)/tr2 rval<-pf(x/ndf/scale, ndf,ddf,lower.tail=lower.tail) if (method=="saddlepoint"){ a<-c(a,-x/ddf) df<-c(df,ddf) if(any(df>1)) a<-rep(a,df) s<-saddle(0,a) if (!is.na(s)) { if (lower.tail) rval<-1-s else rval<-s } } rval } } survey/R/RcppExports.R0000644000176200001440000000152314462643117014435 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 arma_onestage <- function(Y, samp_unit_ids, strata_ids, strata_samp_sizes, strata_pop_sizes, singleton_method, use_singleton_method_for_domains, stage) { .Call('_survey_arma_onestage', PACKAGE = 'survey', Y, samp_unit_ids, strata_ids, strata_samp_sizes, strata_pop_sizes, singleton_method, use_singleton_method_for_domains, stage) } arma_multistage <- function(Y, samp_unit_ids, strata_ids, strata_samp_sizes, strata_pop_sizes, singleton_method, use_singleton_method_for_domains, use_only_first_stage, stage) { .Call('_survey_arma_multistage', PACKAGE = 'survey', Y, samp_unit_ids, strata_ids, strata_samp_sizes, strata_pop_sizes, singleton_method, use_singleton_method_for_domains, use_only_first_stage, stage) } survey/R/compressweights.R0000755000176200001440000000245313517345031015372 0ustar liggesusers "dim.repweights_compressed"<-function(x){ c(length(x$index),ncol(x$weights)) } "dimnames.repweights_compressed"<-function(x){ list(names(x$index), colnames(x$weights)) } "[.repweights_compressed"<-function(x,i,...,drop=FALSE){ if (!missing(i)){ x$index<-x$index[i] if(!missing(..1)) x$weights<-x$weights[,..1,drop=FALSE] } else{ ## this is faster than just subscripting x$weights (!) x<-list(index=x$index, weights=x$weights[,...,drop=FALSE]) class(x)<-c("repweights_compressed","repweights") } x } "as.matrix.repweights_compressed"<-function(x,...){ x$weights[x$index,,drop=FALSE] } "as.vector.repweights_compressed"<-function(x,...){ as.vector(x$weights[x$index,]) } "as.matrix.repweights"<-function(x,...){ x } compressWeights<-function(rw,...){ UseMethod("compressWeights") } "compressWeights.repweights_compressed"<-function(rw,...){ compressWeights(as.matrix(rw)) } compressWeights.default<-function(rw,...){ mat<-as.matrix(rw) tmp<-apply(mat,1,function(x) paste(x,collapse="\r")) unq<-!duplicated(mat) rval<-list(weights=mat[unq,],index=match(tmp,tmp[unq])) class(rval)<-c("repweights_compressed","repweights") rval } compressWeights.svyrep.design<-function(rw,...){ rw$repweights<-compressWeights(rw$repweights,...) rw } survey/R/svyqq.R0000644000176200001440000000444513707657463013347 0ustar liggesusers svyqqmath<-function(x, design, null=qnorm, na.rm=TRUE,xlab="Expected",ylab="Observed",...){ if (inherits(x, "formula")) x <- model.frame(x, model.frame(design), na.action = na.pass) else if (typeof(x) %in% c("expression", "symbol")) x <- eval(x, model.frame(design, na.action = na.pass)) if (na.rm) { nas <- rowSums(is.na(x)) design <- design[nas == 0, ] if (length(nas) > length(design$prob)) x <- x[nas == 0, , drop = FALSE] else x[nas > 0, ] <- 0 } n<-NROW(x) for(variable in seq_len(NCOL(x))){ ii<-order(x[, variable]) obsi<-x[ii, variable] w<-weights(design,"sampling")[ii] cumw<-(cumsum(w)/sum(w))*(n/(n+1)) expi<-null(cumw) plot(expi,obsi,xlab=xlab,ylab=ylab,...) } invisible(NULL) } svyqqplot<-function(formula, design, designx=NULL, na.rm=TRUE,qrule="hf8",xlab=NULL,ylab=NULL,...){ if (is.null(designx)){ if (inherits(formula, "formula")) x <- model.frame(formula, model.frame(design), na.action = na.pass) else if (typeof(x) %in% c("expression", "symbol")) x <- eval(formula, model.frame(design, na.action = na.pass)) if (na.rm) { nas <- rowSums(is.na(x)) design <- design[nas == 0, ] if (length(nas) > length(design$prob)) x <- x[nas == 0, , drop = FALSE] else x[nas > 0, ] <- 0 } Y<-x[,1] X<-x[,2] wx<-wy<-weights(design,"sampling") } else { xform<-formula[-2] yform<-make.formula(formula[[2]]) environment(yform)<-environment(formula) Y<- model.frame(formula, model.frame(design), na.action = na.pass)[[1]] wy<-weights(design,"sampling") X<- model.frame(formula, model.frame(designx), na.action = na.pass)[[1]] wx<-weights(designx,"sampling") } n<-length(Y) m<-length(X) if(is.null(xlab)) xlab<-deparse(formula[[3]]) if(is.null(ylab)) ylab<-deparse(formula[[2]]) if(is.character(qrule)) qrule<-get(paste("qrule",qrule,sep="_"), mode="function") if (nm){ Y<-sapply(1:m, function(i) qrule(Y,wy, i/m)) } plot(sort(X),sort(Y),xlab=xlab,ylab=ylab,...) } survey/R/surveygraph.R0000755000176200001440000002314213707676710014535 0ustar liggesusersmake.panel.svysmooth<-function(design,bandwidth=NULL){ function(x,y,span=NULL,col.smooth="red",col=par("col"),bg=NA,pch=par("pch"),cex=1,...){ if(!is.null(span)) bandwidth<-diff(range(x))*span/3 s<-svysmooth(y~x,design=design,bandwidth=bandwidth) points(x,y,pch=pch,bg=bg,col=col) lines(s[[1]],col=col.smooth,...) } } svyplot<-function(formula, design,...) UseMethod("svyplot",design) svyplot.default<-function(formula, design, style=c("bubble","hex","grayhex","subsample","transparent"), sample.size=500, subset=NULL,legend=1,inches=0.05, amount=NULL,basecol="black",alpha=c(0,0.8), xbins=30,...){ style<-match.arg(style) if (style %in% c("hex","grayhex") && !requireNamespace("hexbin",quietly=TRUE)){ stop(style," plots require the hexbin package") } subset<-substitute(subset) subset<-with(design$variables, subset) if(length(subset)>0) design<-design[subset,] W<-weights(design, "sampling") mf<-model.frame(formula, design$variables,na.action=na.pass) Y<-model.response(mf) X<-mf[,attr(attr(mf,"terms"),"term.labels")] switch(style, bubble={ if(is.function(basecol)) basecol<-basecol(model.frame(design)) symbols(X,Y,circles=sqrt(W),inches=inches,fg=basecol,...) }, hex={ ## CRAN will be happier if we stop supporting the old version of hexbin ## new version rval<-hexbin::hexbin(X,Y,IDs=TRUE,xbins=xbins) cell<-rval@cID rval@count<-as.vector(tapply(W,cell,sum)) rval@xcm<-as.vector(tapply(1:length(X), cell, function(ii) weighted.mean(X[ii],W[ii]))) rval@ycm<-as.vector(tapply(1:length(Y), cell, function(ii) weighted.mean(Y[ii],W[ii]))) hexbin::gplot.hexbin(rval, legend=legend, style="centroids",...) }, grayhex={ ## new version rval<-hexbin::hexbin(X,Y,IDs=TRUE,xbins=xbins) cell<-rval@cID rval@count<-as.vector(tapply(W,cell,sum)) hexbin::gplot.hexbin(rval, legend=legend,...) }, subsample={ index<-sample(length(X),sample.size,replace=TRUE, prob=W) if (is.numeric(X)) xs<-jitter(X[index],factor=3,amount=amount$x) else xs<-X[index] if (is.numeric(Y)) ys<-jitter(Y[index],factor=3,amount=amount$y) else ys<-Y[index] plot(xs,ys,...) }, transparent={ transcol<-function(base,opacity){ rgbs<-col2rgb(base)/255 rgb(rgbs[1,],rgbs[2,], rgbs[3,], alpha=opacity) } if(is.function(basecol)) basecol<-basecol(model.frame(design)) w<-weights(design) maxw<-max(w) minw<-0 alphas<- (alpha[1]*(maxw-w)+alpha[2]*(w-minw))/(maxw-minw) plot(X,Y,col=transcol(basecol,alphas),...) }) } svyboxplot<-function(formula, design,all.outliers=FALSE,...) UseMethod("svyboxplot",design) svyboxplot.default<-function(formula, design, all.outliers=FALSE,col=NULL,names,...){ formula<-as.formula(formula) if(length(formula)!=3) stop("need a two-sided formula") ##if(length(formula[[3]])>2) stop("only one rhs variable allowed") outcome<-eval(bquote(~.(formula[[2]]))) outcome.values<-model.frame(outcome, model.frame(design),na.action=na.pass) if (length(attr(terms(formula),"term.labels"))){ groups<-eval(bquote(~.(formula[[3]]))) qs <- svyby(outcome,groups,design,svyquantile,ci=FALSE, keep.var=FALSE, quantiles=c(0,0.25,0.5,0.75,1),na.rm=TRUE) group.values<-model.frame(groups, model.frame(design),na.action=na.pass)[[1]] n<-NCOL(qs) iqr<- qs[,n-1]-qs[,n-3] low<-pmax(qs[,n-4],qs[,n-3]-1.5*iqr) hi<-pmin(qs[,n],qs[,n-1]+1.5*iqr) stats<-t(as.matrix(cbind(low,qs[,n-(3:1)],hi))) z<-list(stats=stats,n=coef(svytotal(groups,design,na.rm=TRUE))) for(i in 1:ncol(stats)){ out<-c(if(qs[i,n]!=hi[i]) qs[i,n], if(qs[i,n-4]!=low[i])qs[i,n-4]) if (all.outliers){ outlo<-sort(outcome.values[!is.na(outcome.values) & (as.numeric(group.values) %in% i) & outcome.valueshi[i] ]) out<-na.omit(unique(c(outlo,outhi))) } z$out<-c(z$out,out) z$group<-c(z$group,rep(i,length(out))) z$names<-as.character(qs[,1]) } } else { qs<-coef(svyquantile(outcome,design,ci=FALSE, quantiles=c(0,0.25,0.5,0.75,1),na.rm=TRUE)) iqr<-qs[4]-qs[2] z<-list(stats=matrix(c(max(qs[1],qs[2]-1.5*iqr), qs[2:4],min(qs[5],qs[4]+1.5*iqr))), n=sum(weights(design,"sampling"))) z$out<-c(if(qs[5]!=z$stats[5]) qs[5], if(qs[1]!=z$stats[1]) qs[1]) if (all.outliers){ outlo<-sort(outcome.values[!is.na(outcome.values) & outcome.valuesqs[4]+1.5*iqr]) z$out<-na.omit(unique(c(outlo,outhi))) } z$group<-rep(1,length(z$out)) } if (is.null(col)) col<-par("bg") if (!missing(names)) z$names<-names bxp(z,boxfill=col,...) } svycoplot<-function(formula, design, style=c("hexbin","transparent"), basecol="black",alpha=c(0,0.8),hexscale=c("relative","absolute"),...) UseMethod("svycoplot",design) svycoplot.default<-function(formula, design, style=c("hexbin","transparent"), basecol="black",alpha=c(0,0.8),hexscale=c("relative","absolute"),xbins=15,...){ style<-match.arg(style) wt<-weights(design,"sampling") switch(style, hexbin={ hexscale<-match.arg(hexscale) xyplot(formula, data=model.frame(design), xbins=xbins, panel=function(x,y,style="centroids",xbins,subscripts,...) { if (!length(x)) return(panel.xyplot(x,y,...)) vp<-current.viewport() wd<-convertWidth(vp$width,unitTo="cm",valueOnly=TRUE) ht<-convertHeight(vp$height,unitTo="cm",valueOnly=TRUE) W<-wt[subscripts] rval<-hexbin::hexbin(x,y,IDs=TRUE,xbins=xbins,shape=ht/wd,xbnds=vp$xscale,ybnds=vp$yscale) cell<-rval@cID rval@count<-as.vector(tapply(W,cell,sum)) rval@xcm<-as.vector(tapply(1:length(x), cell, function(ii) weighted.mean(x[ii],W[ii]))) rval@ycm<-as.vector(tapply(1:length(y), cell, function(ii) weighted.mean(x[ii],W[ii]))) hexbin::grid.hexagons(rval,style=style, maxarea=switch(hexscale, relative=0.8, absolute=0.8*sum(W)/sum(wt))) },...) }, transparent={ if(is.function(basecol)) basecol<-basecol(model.frame(design)) transcol<-function(base,opacity){ rgbs<-col2rgb(base)/255 rgb(rgbs[1,],rgbs[2,], rgbs[3,], alpha=opacity) } maxw<-max(wt) minw<-0 alphas<- (alpha[1]*(maxw-wt)+alpha[2]*(wt-minw))/(maxw-minw) cols<-transcol(basecol,alphas) xyplot(formula, data=model.frame(design), panel=function(x,y,basecol="black",subscripts,...) { a<-alphas[subscripts] panel.xyplot(x,y,col=cols[subscripts],pch=19,...) },...) } ) } barplot.svystat<-function(height,...) barplot(coef(height),...) barplot.svrepstat<-function(height,...) barplot(coef(height),...) plot.svystat<-function(x,...) barplot(coef(x),...) plot.svrepstat<-function(x,...) barplot(coef(x),...) barplot.svyby<-function(height,beside=TRUE,...){ aa <- attr(height, "svyby") rval <- height[, max(aa$margins) + (1:aa$nstats)] if (is.null(dim(rval))) { if (length(aa$margins)<2){ names(rval) <- row.names(height) } else { rval<-matrix(rval, nrow=length(unique(height[,aa$margins[1]]))) rownames(rval) <- unique(height[,aa$margins[1]]) colnames(rval)<-levels(do.call(interaction, height[,aa$margins[-1],drop=FALSE])) } } else { rval <- as.matrix(rval) colnames(rval)<-sub("statistics\\.","",colnames(rval)) rval<-t(rval) } barplot(rval,beside=beside,...) } plot.svyby<-function(x,...) barplot.svyby(x,...) dotchart.default<-graphics::dotchart dotchart<-function(x,...,pch=19) UseMethod("dotchart") dotchart.svystat<-function(x,...,pch=19) dotchart(coef(x),...,pch=pch) dotchart.svrepstat<-function(x,...,pch=19) dotchart(coef(x),...,pch=pch) dotchart.svyby<-function(x,...,pch=19){ height<-x aa <- attr(height, "svyby") rval <- height[, max(aa$margins) + (1:aa$nstats)] if (is.null(dim(rval))) { if (length(aa$margins)<2){ names(rval) <- row.names(height) } else { rval<-matrix(rval, nrow=length(unique(height[,aa$margins[1]]))) rownames(rval) <- unique(height[,aa$margins[1]]) colnames(rval)<-levels(do.call(interaction, height[,aa$margins[-1],drop=FALSE])) } } else { rval <- as.matrix(rval) colnames(rval)<-sub("statistics\\.","",colnames(rval)) rval<-t(rval) } dotchart(rval,...,pch=pch) } survey/R/multivariate.R0000644000176200001440000001122313517345031014642 0ustar liggesusers svyfactanal<-function(formula, design, factors,n=c("none", "sample","degf","effective","min.effective"),...){ v<-svyvar(formula,design) n<-match.arg(n) s2<-diag(v) ses2<-diag(matrix(SE(v), length(s2), length(s2))) neff<-2*(s2/ses2)^2 n<-switch(n, sample=nrow(design)-1, degf=degf(design), effective=1/mean(1/neff),min.effective=min(neff), none=NA)+1 f<-factanal(covmat=v, factors=factors, n.obs=n,...) f$call<-sys.call() f } svyprcomp<-function (formula, design, center = TRUE, scale. = FALSE, tol = NULL, scores=FALSE, ...) { tms<-terms(formula) attr(tms,"intercept")<-0 mf<-model.frame(formula,model.frame(design)) naa<-attr(mf,"na.action") x <- model.matrix(tms,mf) if(length(naa)) w<-weights(design,"sampling")[-naa] else w<-weights(design,"sampling") x<-x*sqrt(w/mean(w)) x <- scale(x, center = center, scale = scale.) cen <- attr(x, "scaled:center") sc <- attr(x, "scaled:scale") if (any(sc == 0)) stop("cannot rescale a constant/zero column to unit variance") s <- svd(x, nu = 0) s$d <- s$d/sqrt(max(1, nrow(x) - 1)) if (!is.null(tol)) { rank <- sum(s$d > (s$d[1L] * tol)) if (rank < ncol(x)) { s$v <- s$v[, 1L:rank, drop = FALSE] s$d <- s$d[1L:rank] } } dimnames(s$v) <- list(colnames(x), paste("PC", seq_len(ncol(s$v)), sep = "")) r <- list(sdev = s$d, rotation = s$v, center = if (is.null(cen)) FALSE else cen, scale = if (is.null(sc)) FALSE else sc) r$weights<-w/mean(w) if (scores) r$x <- (x %*% s$v)/sqrt(r$weights) r$naa<-naa r$design<-design class(r) <- c("svyprcomp","prcomp") r } biplot.svyprcomp<-function(x, cols=c("black","darkred"),xlabs=NULL,weight=c("transparent","scaled","none"), max.alpha=0.5,max.cex=0.5,xlim=NULL,ylim=NULL,pc.biplot=FALSE,expand=1,xlab=NULL,ylab=NULL, arrow.len=0.1, ...){ if(is.null(xlabs)){ xlabs<-1:NROW(x$x) } else { if (inherits(xlabs,"formula")){ mf<-model.frame(xlabs,model.frame(x$design),na.action=na.pass) if(length(x$na.action)) mf<-mf[-x$na.action,] if(ncol(mf)>1) xlabs<-sapply(mf,paste,collapse=".") else xlabs<-as.character(mf[[1]]) } } scores<-x$x lam <- x$sdev[1:2] n <- NROW(scores) lam <- lam * sqrt(n) if (pc.biplot) lam <- lam/sqrt(n) xx<-t(t(scores[, 1:2])/lam) yy<-t(t(x$rotation[,1:2]) * lam) if (missing(xlabs)) { xlabs <- dimnames(x)[[1L]] if (is.null(xlabs)) xlabs <- 1L:n } xlabs <- as.character(xlabs) dimnames(xx) <- list(xlabs, dimnames(xx)[[2L]]) ylabs <- dimnames(yy)[[1L]] ylabs <- as.character(ylabs) dimnames(yy) <- list(ylabs, dimnames(yy)[[2L]]) weight<-match.arg(weight) w<-weights(x$design) if (length(x$na.action)) w<-w[-x$na.action] if (weight=="transparent"){ xcexs<-par("cex")*max.cex rgbcol<-col2rgb(rep(cols[1],length=length(w))) xcols<-rgb(rgbcol[1,],rgbcol[2,],rgbcol[3,],alpha=pmax(1,255*w*max.alpha/max(w)), maxColorValue=255) } else if (weight=="scaled"){ xcexs<-par("cex")*pmax(0.2, max.cex*sqrt(w/max(w))) rgbcol<-col2rgb(cols[1]) xcols<-rgb(rgbcol[1,],rgbcol[2,],rgbcol[3,],alpha=max.alpha*255, maxColorValue=255) } else if (weight=="none"){ rgbcol<-col2rgb(cols[1]) xcols<-rgb(rgbcol[1,],rgbcol[2,],rgbcol[3,],alpha=max.alpha*255, maxColorValue=255) xcexs<-par("cex")*max.cex } unsigned.range <- function(x) c(-abs(min(x, na.rm = TRUE)), abs(max(x, na.rm = TRUE))) rangx1 <- unsigned.range(xx[, 1L]) rangx2 <- unsigned.range(xx[, 2L]) rangy1 <- unsigned.range(yy[, 1L]) rangy2 <- unsigned.range(yy[, 2L]) if (is.null(xlim) && is.null(ylim)) xlim <- ylim <- rangx1 <- rangx2 <- range(rangx1, rangx2) else if (is.null(xlim)) xlim <- rangx1 else if (is.null(ylim)) ylim <- rangx2 ratio <- max(rangy1/rangx1, rangy2/rangx2)/expand on.exit(par(op)) op <- par(pty = "s") plot(xx, type = "n", xlim = xlim, ylim = ylim, col = cols[1], xlab = xlab, ylab = ylab, ...) text(xx, xlabs, cex = xcexs, col = xcols, ...) par(new = TRUE) plot(yy, axes = FALSE, type = "n", xlim = xlim * ratio, ylim = ylim * ratio, xlab = "", ylab = "", col = xcols, ...) axis(3, col = cols[2L], ...) axis(4, col = cols[2L], ...) box(col = cols[1L]) text(yy, labels = ylabs, col = cols[2L], ...) arrows(0, 0, yy[, 1L] * 0.8, yy[, 2L] * 0.8, col = cols[2L], length = arrow.len) invisible() } survey/R/ht.R0000644000176200001440000000163013517345031012550 0ustar liggesusers htvar.list<-function(xcheck, Dcheck){ rval<-sapply(Dcheck, function(stagei) {htvar.matrix(rowsum(xcheck,stagei$id),stagei$dcheck)}) rval } ## used in twophase2var() htvar.matrix<-function(xcheck, Dcheck){ if (is.null(dim(xcheck))) xcheck<-as.matrix(xcheck) rval<-apply(xcheck,2, function(xicheck) apply(xcheck,2, function(xjcheck) as.matrix(Matrix::crossprod(xicheck, Dcheck%*%xjcheck)) )) if(is.null(dim(rval))) dim(rval)<-c(1,1) rval } ## used in ppsvar, twophase2var ygvar.matrix<-function(xcheck,Dcheck){ ht<-htvar.matrix(xcheck,Dcheck) if (is.null(dim(xcheck))){ corr <- sum(Dcheck%*%(xcheck*xcheck)) } else { corr <- apply(xcheck,2, function(xicheck) apply(xcheck,2, function(xjcheck) sum(Dcheck%*%(xicheck*xjcheck)) )) } rval<-ht-corr } survey/R/mrb.R0000644000176200001440000000354214020241104012703 0ustar liggesusers## Rescaled multistage bootstrap ## Preston http://www.statcan.gc.ca/pub/12-001-x/2009002/article/11044-eng.pdf ## mrbweights<-function(clusters,stratas,fpcs, replicates=50, multicore=getOption("survey.multicore")){ nstages<-NCOL(clusters) if (is.null(fpcs$popsize)){ warning("Design is sampled with replacement: only first stage used") fpcs$popsize<-matrix(Inf, ncol=1,nrow=NROW(clusters)) nstages<-1 } if (multicore & !requireNamespace("parallel", quietly=TRUE)) multicore<-FALSE do.it<-if(multicore) parallel::mclapply else lapply weightlist<-do.it(1:replicates, function(k){ weights<-matrix(1,nrow=NROW(clusters),ncol=nstages) kept<-rep(TRUE, NROW(clusters)) cumffs<-rep(1,NROW(clusters)) for(i in 1:nstages){ ustrata<-unique(stratas[,i]) nstrata<-length(ustrata) for(j in 1:nstrata){ thisstratum<-stratas[,i]==ustrata[j] su <- unique(clusters[thisstratum & kept,i] ) n <-length(su) nstar<-floor(n/2) cumff<-cumffs[thisstratum][1] fpc<- fpcs$sampsize[thisstratum,i][1]/fpcs$popsize[thisstratum,i][1] if (nstar==0) { wstar<-0 keep<- rep(FALSE,sum(thisstratum)) } else { lambda<-sqrt(cumff*nstar*(1-fpc)/(n-nstar)) keep<-clusters[thisstratum,i] %in% sample(su,nstar) wstar<-(-lambda+lambda*(n/nstar)*keep) } weights[thisstratum, i]<-wstar*weights[thisstratum, i] if (nstar>0 & i 30) { ## plot_breaks <- c(seq(0, m, by = 30), m) ## plot_labels <- paste0( ## "Areas ", ## (seq_along(plot_breaks)[-1] - 2) * 30 + 1, ## " to ", ## pmin((seq_along(plot_breaks)[-1] - 1) * 30, m) ## ) ## combined_est$plot <- ## cut(1:m, breaks = plot_breaks, labels = plot_labels) ## } else { ## combined_est$plot <- 1 ## } ## plot_list <- split(combined_est, combined_est$plot) ## for (y in plot_list) { ## n_domains <- length(unique(y$domain)) ## plot_dat <- data.frame( ## domain = y$domain, ## xval = match(y$domain, unique(y$domain)), ## est = y$median, ## lower = y$lower, ## upper = y$upper, ## method = as.factor(y$method) ## ) ## plot_dat$xval <- plot_dat$xval + (as.numeric(plot_dat$method) - 1) * ## .2 - (.2 * (n_methods - 1) / 2) ## par(mar = c(9, 5, 4, 3)) ## plot(est ~ xval, data = plot_dat, xaxt = "n", xlab = "", pch = 16, ## col = (2:(n_methods + 1))[plot_dat$method], cex = .5, ## ylim = c(min(plot_dat$lower), max(plot_dat$upper)), ## ylab = "Estimate", ## cex.axis = .75) ## segments(plot_dat$xval, plot_dat$lower, plot_dat$xval, plot_dat$upper, ## col = (2:(n_methods + 1))[plot_dat$method], cex = .5) ## axis(1, at = 1:n_domains, labels = unique(as.character(y$domain)), las = 2, ## cex.axis = .65) ## legend("topleft", inset = .01, legend = levels(plot_dat$method), ## col=(2:(n_methods + 1)), lty=1, cex=0.8, box.lty=0) ## } ## } survey/R/svyranktest.R0000644000176200001440000001045014226370323014532 0ustar liggesusers svyranktest<-function(formula,design,test=c('wilcoxon','vanderWaerden','median',"KruskalWallis"),...){ UseMethod("svyranktest", design) } svyranktest.survey.design<-svyranktest.svyrep.design<-function(formula, design, test=c('wilcoxon','vanderWaerden','median',"KruskalWallis"),...) { mf<-model.frame(formula,model.frame(design),na.action=na.omit) if (!is.null(naa<-attr(mf,"na.action"))){ design<-design[-naa,] mf<-model.frame(formula,model.frame(design)) } y<-mf[,1] g<-mf[,2] if (length(unique(g))!=2) { return(multiranktest(formula,design, test,...)) } if (is.character(test)) { test<-match.arg(test) testf<-switch(test, wilcoxon=,KruskalWallis=function(r,N) r/N, vanderWaerden=function(r,N) qnorm(r/N), median=function(r,N) as.numeric(r>N/2)) } else{ testf<-test } if (identical(test,"wilcoxon")) test<-"KruskalWallis" ii<-order(y) n<-length(y) rankhat<-numeric(n) w<-weights(design,"sampling") ## calibrated designs have weights set to zero, not removed na.fixup<-FALSE if (is.calibrated(design) && length(w)>n && !is.null(naa)){ w<-w[-naa] na.fixup<-TRUE } N<-sum(w) rankhat[ii]<-ave(cumsum(w[ii])-w[ii]/2,factor(y[ii])) rankscore<-testf(rankhat,N) m <- lm(rankscore~g, weights=w) delta<-coef(m)[2] xmat<-model.matrix(m) if (na.fixup){ infn<-matrix(0,nrow=nrow(xmat)+length(naa),ncol=ncol(xmat)) infn[-naa,]<-(xmat*(rankscore-fitted(m)))%*%summary(m)$cov.unscaled } else { infn<- (xmat*(rankscore-fitted(m)))%*%summary(m)$cov.unscaled } tot.infn<-svytotal(infn,design) if (is.character(test)) method<-paste("Design-based",test,"test") else if (!is.null(attr(test,"name"))) method<-paste("Design-based",attr(test,"name"),"test") else method<-"Design-based rank test" rval <- list(statistic = coef(m)[2]/SE(tot.infn)[2], parameter = degf(design) - 1, estimate = coef(m)[2], null.value = 0, alternative = "two.sided", method = method, data.name = deparse(formula)) rval$p.value <- 2 * pt(-abs(rval$statistic), df = rval$parameter) names(rval$statistic) <- "t" names(rval$parameter) <- "df" names(rval$estimate) <- "difference in mean rank score" names(rval$null.value) <- "difference in mean rank score" class(rval) <- "htest" rval } multiranktest<-function(formula,design,test=c('wilcoxon','vanderWaerden','median','KruskalWallis'),...){ mf<-model.frame(formula,model.frame(design),na.action=na.omit) if (!is.null(naa<-attr(mf,"na.action"))){ design<-design[-naa,] #mf<-model.frame(formula,model.frame(design),na.action=na.fail) } y<-mf[,1] g<-mf[,2] if (is.character(test)) { test<-match.arg(test) testf<-switch(test, wilcoxon=,KruskalWallis=function(r,N) r/N, vanderWaerden=function(r,N) qnorm(r/N), median=function(r,N) as.numeric(r>N/2)) } else{ testf<-test } if (identical(test,"wilcoxon")) test<-"KruskalWallis" ii<-order(y) n<-length(y) rankhat<-numeric(n) w<-weights(design,"sampling") ## calibrated designs have weights set to zero, not removed if (is.calibrated(design) && length(w)>n && !is.null(naa)){ w<-w[-naa] } N<-sum(w) rankhat[ii]<-ave(cumsum(w[ii])-w[ii]/2,factor(y[ii])) rankscore<-testf(rankhat,N) m <- glm(rankscore~factor(g),weights=w) m$na.action<-naa V<-svy.varcoef(m,design) ndf<-length(unique(g))-1 beta<-coef(m)[-1] V<-V[-1,-1] chisq<-beta%*%solve(V,beta) ddf<-degf(design)-ndf if (is.character(test)) method<-paste("Design-based",test,"test") else if (!is.null(attr(test,"name"))) method<-paste("Design-based",attr(test,"name"),"test") else method<-"Design-based rank test" names(chisq)<-"Chisq" names(ndf)<-"df" rval<-list(parameter=chisq,statistic=ndf,ddf=ddf,p.value=pf(chisq/ndf,ndf,ddf,lower.tail=FALSE), method=method, data.name = deparse(formula)) class(rval)<-"htest" rval } cov_inffun_glm<-function(m, design){ A<-summary(m)$cov.unscaled xmat <- model.matrix(m) U<-residuals(m, "working") * m$weights * xmat/weights(design,"sampling") h<-U%*%A vcov(svytotal(h, design)) } survey/R/grake.R0000644000176200001440000004320514423650257013240 0ustar liggesusers make.calfun<-function(Fm1,dF, name){ if (!identical(names(formals(Fm1)), c("u","bounds"))) stop("wrong argument names for Fm1") if(!identical(names(formals(dF)), c("u","bounds"))) stop("wrong argument names for dF") rval<-list(Fm1=Fm1, dF=dF, name=name) class(rval)<-"calfun" rval } cal_names <- function(formula, design, ...) UseMethod("cal_names",design) cal_names.DBIsvydesign<-function(formula, design,...){ design$variables <- getvars(formula, design$db$connection, design$db$tablename, updates = design$updates, subset = design$subset) colnames(model.matrix(formula, model.frame(formula,design$variables[0,]))) } cal_names.survey.design<-function(formula,design,...) { colnames(model.matrix(formula, model.frame(formula,model.frame(design)[0,]))) } print.calfun<-function(x,...) cat("calibration metric: ",x$name,"\n") calibrate<-function(design, ...) UseMethod("calibrate") calibrate.pps<-function(design, formula, population, aggregate.stage=NULL, stage=0,...){ if (!is.null(stage) && !(stage==0)) stop("'stage'(>0) not supported for pps designs") if (!is.null(aggregate.stage)) stop("'aggregate.stage' not supported for pps designs") calibrate.survey.design2(design, formula, population, aggregate.stage=NULL, stage=0,,...) } calibrate.survey.design2<-function(design, formula, population, aggregate.stage=NULL, stage=0, variance=NULL, bounds=c(-Inf,Inf), calfun=c("linear","raking","logit"), maxit=50, epsilon=1e-7, verbose=FALSE, force=FALSE, trim=NULL, bounds.const = FALSE, sparse=FALSE, ...){ if(is.list(formula) && is.list(population)){ ## inputs as marginal totals, as in rake() population<-margins2totals(formula,population) formula<-as.formula(paste("~",paste(sapply(formula,function(f) paste(all.vars(f),collapse="*")),collapse="+"))) if (verbose){ print(formula) print(population) } } if (!is.list(bounds)) bounds <- list(lower = bounds[1], upper = bounds[2]) ## converting to list if not already if (is.character(calfun)) calfun<-match.arg(calfun) if (is.character(calfun) && calfun=="linear" && all(unlist(bounds) == c(-Inf, Inf))){ ## old code is better for ill-conditioned linear calibration rval<-regcalibrate(design,formula,population, aggregate.stage=aggregate.stage, stage=stage, lambda=variance,sparse=sparse,...) rval$call<-sys.call(-1) return(rval) } if(is.character(calfun)) calfun<-switch(calfun,linear=cal.linear, raking=cal.raking, logit=cal.logit) else if(!inherits(calfun,"calfun")) stop("'calfun' must be a string or of class 'calfun'.") if (length(epsilon)!=1 && length(epsilon)!=length(population)) stop("'epsilon' must be a scalar or of the same length as 'population'") if (!is.null(aggregate.stage)){ aggindex<-design$cluster[[aggregate.stage]] } expit<-function(x) 1-1/(1+exp(x)) ## calibration to population totals if(sparse){ mm<-sparse.model.matrix(formula, model.frame(formula, model.frame(design))) }else{ mm<-model.matrix(formula, model.frame(formula, model.frame(design))) } ww<-weights(design) if (bounds.const) bounds<-lapply(bounds, function(x) ifelse(ww>0,x/ww,x)) # if bounds are set to a constant convert to multiplicative value and run as normal if (!is.null(aggregate.stage)){ mm<-apply(mm,2,function(mx) ave(mx,aggindex)) ww<-ave(ww,aggindex) } whalf<-sqrt(ww) sample.total<-colSums(mm*ww) if(any(sample.total==0)){ ## drop columsn where all sample and population are zero zz<-(population==0) & (apply(mm,2,function(x) all(x==0))) mm<-mm[,!zz] population<-population[!zz] sample.total<-sample.total[!zz] if (length(epsilon)>1) epsilon <- epsilon[!zz] } if (length(sample.total)!=length(population)){ print(sample.total) print(population) stop("Population and sample totals are not the same length.") } if(!is.null(names(population))){ if (!all(names(sample.total) %in% names(population))){ warning("Sampling and population totals have different names.") cat("Sample: "); print(names(sample.total)) cat("Popltn: "); print(names(population)) } else if (!all(names(sample.total) == names(population))){ warning("Sample and population totals reordered to make names agree: check results.") population <- population[match(names(sample.total), names(population))] } } tqr<-qr(mm*whalf) #if (!all(abs(qr.resid(tqr,whalf))<1e-10)) # warning("G-calibration models must have an intercept") g<-grake(mm,ww,calfun, bounds=bounds,population=population, verbose=verbose,epsilon=epsilon,maxit=maxit, variance=variance) if(!is.null(trim)) { gnew<-pmax(trim[1], pmin(g, trim[2])) outside<-gtrim[2] if (any(outside)){ trimmings<-(g-gnew)*ww gnew[!outside]<-gnew[!outside]+sum(trimmings)/sum(ww[!outside]) g<-gnew attr(g,"failed")<-NULL message(paste(sum(outside),"weights were trimmed")) } } if (!is.null(attr(g,"failed"))){ if (!force) stop("Calibration failed") } design$prob<-design$prob/g caldata <- list(qr=tqr, w=g*whalf, stage=0, index=NULL) class(caldata) <- c("greg_calibration","gen_raking") design$postStrata <- c(design$postStrata, list(caldata)) design$call <- sys.call(-1) design } calibrate.svyrep.design<-function(design, formula, population,compress=NA, aggregate.index=NULL, variance=NULL, bounds=c(-Inf,Inf), calfun=c("linear","raking","logit"), maxit=50, epsilon=1e-7, verbose=FALSE,force=FALSE, trim=NULL, bounds.const=FALSE, sparse=FALSE, ...){ if(is.list(formula) && is.list(population)){ ## inputs as marginal totals, as in rake() population<-margins2totals(formula,population) formula<-as.formula(paste("~",paste(sapply(formula,function(f) paste(all.vars(f),collapse="*")),collapse="+"))) if (verbose){ print(formula) print(population) } } if (is.character(calfun)) calfun<-match.arg(calfun) if (length(epsilon)!=1 && length(epsilon)!=length(population)) stop("'epsilon' must be a scalar or of the same length as 'population'") if (!is.list(bounds)) bounds <- list(lower = bounds[1], upper = bounds[2]) ## converting to list if not already if (is.character(calfun) && calfun=="linear" && all(unlist(bounds)==c(-Inf,Inf))){ ## old code is better for ill-conditioned linear calibration rval<-regcalibrate(design,formula,population, compress=compress, aggregate.index=aggregate.index, lambda=variance,...) rval$call<-sys.call(-1) return(rval) } mf<-model.frame(formula, design$variables) if(sparse){ mm<-sparse.model.matrix(formula, model.frame(formula, model.frame(design))) }else{ mm<-model.matrix(formula, model.frame(formula, model.frame(design))) } ww<-design$pweights repwt<-as.matrix(design$repweights) if (!design$combined.weights) repwt<-repwt*design$pweights if (inherits(aggregate.index,"formula")){ if (length(aggregate.index)!=2) stop("aggregate.index must be a one-sided formula") aggregate.index<-model.frame(aggregate.index, design$variables) if (NCOL(aggregate.index)>1) stop("aggregate.index must specify a single variable") aggregate.index<-aggregate.index[[1]] } if (!is.null(aggregate.index)){ if (sqrt(max(ave(ww,aggregate.index,FUN=var),na.rm=TRUE))>1e-2*mean(ww)) warning("Sampling weights are not constant within clusters defined by aggregate.index") mm<-apply(mm,2,function(mx) ave(mx,aggregate.index)) ww<-ave(ww,aggregate.index) repwt<-apply(repwt,2,function(wx) ave(wx, aggregate.index)) } whalf<-sqrt(ww) sample.total<-colSums(mm*ww) if(any(sample.total==0)){ ## drop columsn where all sample and population are zero zz<-(population==0) & (apply(mm,2,function(x) all(x==0))) mm<-mm[,!zz] population<-population[!zz] sample.total<-sample.total[!zz] if (length(epsilon)>1) epsilon <- epsilon[!zz] } if (length(sample.total)!=length(population)){ print(sample.total) print(population) stop("Population and sample totals are not the same length.") } if (!is.null(names(population))){ if (!all(names(sample.total) %in% names(population))){ warning("Sampling and population totals have different names.") cat("Sample: "); print(names(sample.total)) cat("Popltn: "); print(names(population)) } else if (!all(names(sample.total) == names(population))){ warning("Sample and population totals reordered to make names agree: check results.") population <- population[match(names(sample.total), names(population))] } } if(is.character(calfun)) calfun<-switch(calfun, linear=cal.linear, raking=cal.raking, logit=cal.logit) else if (!inherits(calfun,"calfun")) stop("'calfun' must be a string or a 'calfun' object") gtotal <- grake(mm,ww,calfun,bounds=bounds,population=population, verbose=verbose, epsilon=epsilon, maxit=maxit, variance=variance) if(!is.null(trim)) { gnew<-pmax(trim[1], pmin(gtotal, trim[2])) outside<-gtotaltrim[2] if (any(outside)){ trimmings<-(gtotal-gnew)*ww gnew[!outside]<-gnew[!outside]+sum(trimmings)/sum(ww[!outside]) gtotal<-gnew attr(gtotal,"failed")<-NULL message(paste(sum(outside),"weights were trimmed")) } } if (!force && !is.null(attr(gtotal,"failed"))) stop("Calibration failed") design$pweights<-design$pweights*gtotal for(i in 1:NCOL(repwt)){ wwi<-repwt[,i] if(verbose) cat("replicate = ",i,"\n") g<-grake(mm, wwi, calfun, eta=rep(0,NCOL(mm)), bounds=bounds, population=population, epsilon=epsilon, verbose=verbose, maxit=maxit, variance=variance) if(length(trim)==2){ outside<-(gtrim[2]) if (any(outside)) { gnew<-pmax(trim[1],pmin(g,trim[2])) trimmings<-(g-gnew)*wwi gnew[!outside]<-gnew[!outside]+sum(trimmings)/sum(wwi[!outside]) g<-gnew }} repwt[,i]<-as.vector(design$repweights[,i])*g } if (!design$combined.weights) repwt<-repwt/gtotal if (compress || (is.na(compress && inherits(design$repweights,"repweights_compressed")))){ repwt<-compressWeights(repwt) } design$repweights<-repwt design$call<-sys.call(-1) design } cal.linear<-make.calfun(function(u,bounds) pmin(pmax(u+1,bounds$lower),bounds$upper)-1, function(u, bounds) as.numeric(u < bounds$upper-1 & u > bounds$lower-1), "linear calibration") cal.raking<-make.calfun(function(u,bounds) pmin(pmax(exp(u),bounds$lower),bounds$upper)-1, function(u, bounds) ifelse(ubounds$lower-1,exp(u),0), "raking") cal.logit<-make.calfun( function(u,bounds) { if (any(!is.finite(unlist(bounds)))) stop("Logit calibration requires finite bounds") L <- bounds$lower U <- bounds$upper A <- (U-L)/((U-1)*(1-L)) eAu <- exp(A*u) ( L*(U-1) + U*(1-L)*eAu)/(U-1+(1-L)*eAu)-1 }, function(u,bounds) { L <- bounds$lower U <- bounds$upper A <- (U-L)/((U-1)*(1-L)) eAu <- exp(A*u) U*(1-L)*eAu*A/(U-1+(1-L)*eAu)-( (L*(U-1)+U*(1-L)*eAu)*( (1-L)*eAu*A ) )/(U-1+(1-L)*eAu)^2 }, "logit calibration" ) cal.sinh <- make.calfun( Fm1 = function(u, bounds) { if (any(!is.finite(unlist(bounds)))) stop("Sinh calibration requires finite bounds") dasinh <- function(u, alpha = 1) { p <- asinh(2 * alpha * u) m <- (p + sqrt((p^2) + 4*(alpha^2)))/(2*alpha) m <- as.vector(m) m } # linear truncation pmin(pmax(dasinh(u), bounds$lower), bounds$upper) - 1 }, dF = function(u, bounds) { ddasinh <- function(u, alpha = 1) { p <- asinh(2 * alpha * u) m <- 1/sqrt(1 + (2*alpha*u)^2)*(1 + p/sqrt(p^2 + 4*alpha^2)) m <- as.vector(m) m } ifelse(u < bounds$upper - 1 & u > bounds$lower - 1, ddasinh(u), 0) }, name = 'sinh calibration' ) grake<-function(mm,ww,calfun,eta=rep(0,NCOL(mm)),bounds,population,epsilon, verbose, maxit, variance=NULL){ sample.total<-colSums(mm*ww) if(!inherits(calfun,"calfun")) stop("'calfun' must be of class 'calfun'") Fm1<-calfun$Fm1 dF<-calfun$dF if (is.null(variance)){ sigma2<-rep(1,nrow(mm)) } else if(length(variance) == nrow(mm)){ sigma2<-drop(variance) }else{ sigma2<-drop(mm%*%variance) } xeta<-drop(mm%*%eta/sigma2) g<-1+Fm1(xeta, bounds) deriv <- dF(xeta, bounds) iter<-1 ## pre-scaling for people starting with no weights SOMETHRESHOLD<-20 scales<-population/sample.total if (min(scales)> SOMETHRESHOLD){ scale<-mean(scales) ww<-ww*scale sample.total<-sample.total*scale if(verbose) message(paste("Sampling weights rescaled by",signif(scale,3))) if (any(is.finite(unlist(bounds)))) warning(paste("Bounds were set but will be interpreted after rescaling by",signif(scale,3))) } else scale<-NULL repeat({ Tmat<-crossprod(mm*ww/sqrt(sigma2)*deriv, mm/sqrt(sigma2)) misfit<-(population-sample.total-colSums(mm*ww*Fm1(xeta, bounds))) deta<-MASS::ginv(as(Tmat, "matrix"), tol=256*.Machine$double.eps)%*%misfit eta<-eta+deta xeta<- drop(mm%*%eta/sigma2) g<-1+Fm1(xeta, bounds) deriv <- dF(xeta, bounds) while(itermaxit) { achieved<-max((abs(misfit)/(1+abs(population)))) warning("Failed to converge: eps=",achieved," in ",iter," iterations") attr(g,"failed")<-achieved break; } }) if (!is.null(scale)) g<-g*scale attr(g,"eta")<-eta g } trimWeights<-function(design, upper=Inf,lower=-Inf, ...){ UseMethod("trimWeights") } do_trimWeights<-function(pw, upper,lower, has_trimmed){ outside<-pwupper if (!any(outside)) return(list(weights=pw,has_trimmed=has_trimmed)) pwnew<-pmax(lower,pmin(pw, upper)) trimmings<-pw-pwnew can_trim<-!outside & !has_trimmed if (!any(can_trim)){ warning("trimming failed") } else { pwnew[can_trim]<-pwnew[can_trim]+sum(trimmings)/sum(can_trim) } list(weights=pwnew,has_trimmed=outside | has_trimmed) } trimWeights.survey.design2<-function(design, upper=Inf, lower= -Inf, strict=FALSE,...){ pw<-weights(design,"sampling") outside<-pwupper if (!any(outside)) return(design) ## doesn't need trimming has_trimmed<-rep(FALSE,length(outside)) while(any(outside)){ trim<-do_trimWeights(pw, upper,lower, has_trimmed) pw<-trim$weights if (!strict) break outside<-pwupper has_trimmed<-trim$has_trimmed } design$prob<-1/pw design$call<-sys.call() design$call[[1]]<-as.name(.Generic) design } trimWeights.svyrep.design<-function(design, upper=Inf, lower= -Inf, strict=FALSE, compress=FALSE,...){ pw<-weights(design,"sampling") outside<-pwupper if (any(outside)) { has_trimmed<-rep(FALSE,length(outside)) while(any(outside)){ trim<-do_trimWeights(pw, upper,lower, has_trimmed) pw<-trim$weights if (!strict) break outside<-pwupper has_trimmed<-trim$has_trimmed } design$prob<-1/pw } rw<-weights(design, "analysis") outside<-rwupper if (any(outside)) { rwnew<-matrix(pmax(lower,pmin(rw, upper)),nrow=nrow(rw)) trimmings<-rw-rwnew rwnew<-rwnew+t(t(!outside)*colSums(trimmings)/colSums(!outside)) if (compress) design$repweights<-compressWeights(rwnew) else design$repweights<-rwnew design$combined.weights<-TRUE } design$call<-sys.call() design$call[[1]]<-as.name(.Generic) design } margins2totals<-function(formulas, totals){ totals<-mapply(onemargin2totals,formulas,totals,SIMPLIFY=FALSE) totaln<-do.call(c,totals) totalorder<-do.call(c,lapply(totals,function(x) attr(x,"order"))) totaln<-totaln[order(totalorder)] totaln[!duplicated(names(totaln))] } onemargin2totals<-function(formula,total){ if (is.table(total)) total<-as.data.frame(total) if (!is.data.frame(total) && is.vector(total) && (length(formula[[2]])==1)){ ## just a vector total<-as.table(total) d<-dimnames(total) names(d)<-paste(deparse(formula[[2]]),collapse="") total<-as.data.frame(total) } if (!is.data.frame(total)) stop("incorrect format for population totals") newformula<-as.formula(paste("Freq",paste(all.vars(formula),collapse="*"),sep="~")) mf<-model.frame(newformula,as.data.frame(total)) mm<-model.matrix(newformula,mf) intorder<-c(1,attr(terms(newformula),"order")[attr(mm,"assign")]) rval<-colSums(mf$Freq*mm) attr(rval,"order")<-intorder rval } survey/R/transform.R0000644000176200001440000000076413517345031014157 0ustar liggesusers## another name for update() transform.survey.design<-function(`_data`, ...) update(`_data`,...) transform.svyrep.design<-function(`_data`, ...) update(`_data`,...) transform.twophase<-function(`_data`, ...) update(`_data`,...) transform.twophase2<-function(`_data`, ...) update(`_data`,...) transform.ODBCsvydesign<-function(`_data`, ...) update(`_data`,...) transform.DBIsvydesign<-function(`_data`, ...) update(`_data`,...) transform.svyimputationList<-function(`_data`, ...) update(`_data`,...) survey/R/mse.R0000644000176200001440000000066613517345031012731 0ustar liggesusers mse<-function(repstat, design){ v<-attr(repstat,"var") center<-attr(v,"means") if ((length(v)!=length(center)^2) && (length(v)==length(center))){ attr(repstat,"var")<-vcov(repstat)+(center-coef(repstat))^2*sum(design$rscales)*design$scale } else { attr(repstat,"var")<-as.matrix(vcov(repstat)+outer((center-coef(repstat)))*sum(design$rscales)*design$scale) } repstat } survey/R/qrule.R0000644000176200001440000000622414526736254013305 0ustar liggesusers ## Need to call qs() from qrule(), because definition of p varies by rule. ## Discrete qrule_math <-qrule_hf1 <- function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } qdata<-qs(x,w,p) if(qdata$wlow<=0) qdata$qlow else qdata$qup } qrule_school<-qrule_hf2 <- function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } qdata<-qs(x,w,p) if(qdata$wlow<=0) (qdata$qlow+qdata$qup)/2 else qdata$qup } qrule_hf3 <- function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } w<-rowsum(w,x) x<-sort(unique(x)) qdata<-qs(x,w,p) if((qdata$wlow<=0)&& (qdata$ilow %%2 ==0)) qdata$qlow else qdata$qup } ## Continuous qrule_hf4 <- function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } qdata<-qs(x,w,p) gamma<-with(qdata, wlow/(wup+wlow)) qdata$qlow*(1-gamma)+qdata$qup*gamma } qrule_hf5<-function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } n<-length(x) if (n==1) return(x) ii<-order(x) x<-x[ii] w<-w[ii] cumw<-cumsum(w) pk<-(cumw-w/2)/(cumw[n]) approx( pk,x, p, method="linear", rule=2)$y } qrule_hf6<-function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } n<-length(x) if (n==1) return(x) ii<-order(x) x<-x[ii] w<-w[ii] cumw<-cumsum(w) pk<-cumw/(cumw[n]+w[n]) approx( pk,x, p, method="linear", rule=2)$y } qrule_shahvaish<-function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } n<-length(x) if (n==1) return(x) ii<-order(x) x<-x[ii] w<-w[ii] wbar<-w/mean(w) S<-cumsum(wbar) pk<-(S+0.5-w/2)/(n+1) approx( pk,x, p, method="constant", f=0, rule=2)$y } qrule_hf7<-function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } n<-length(x) if (n==1) return(x) ii<-order(x) x<-x[ii] cumw<-cumsum(w[ii]) pk<-c(0, cumw[-n])/cumw[n-1] approx( pk,x, p, method="linear", rule=2)$y } qrule_hf8<-function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } n<-length(x) if (n==1) return(x) ii<-order(x) x<-x[ii] w<-w[ii] cumw<-cumsum(w) pk<-(c(0, cumw[-n])*1/3+cumw*2/3)/(cumw[n]+w[n]/3) approx( pk,x, p, method="linear", rule=2)$y } qrule_hf9<-function(x,w,p){ if (any(zero<-w==0)){ w<-w[!zero] x<-x[!zero] } n<-length(x) if (n==1) return(x) ii<-order(x) x<-x[ii] w<-w[ii] cumw<-cumsum(w) pk<-(c(0, cumw[-n])*3/8+cumw*5/8)/(cumw[n]+w[n]/4) approx( pk,x, p, method="linear", rule=2)$y } last<-function(a) { if (any(a)) max(which(a)) else 1 } qs <- function(x, w, p){ ## already has missings removed, ties handled. n<-length(x) ii<-order(x) x<-x[ii] cumw<-cumsum(w[ii]) pos<-last(cumw<=p*sum(w)) posnext<-if(pos==length(x)) pos else pos+1 list(qlow=x[pos], qup=x[posnext], ilow=pos,iup=posnext, wlow=p-cumw[pos]/sum(w),wup=cumw[posnext]/sum(w)-p) } survey/R/score.R0000644000176200001440000001356414526736254013275 0ustar liggesusers fisherinf<-function(beta, model, X=model.matrix(model)){ design<-model$survey.design eta<-X %*%beta mu<-drop(family(model)$linkinv(eta)) V<-family(model)$variance(mu) d<-drop(family(model)$mu.eta(eta)) w<-weights(design,"sampling") D<-X*d t(D)%*%(w*D/V) } scores<-function(model, newbeta, X=model.matrix(model),fullrank=TRUE){ design<-model$survey.design out<-newbeta==0 eta<-X %*%newbeta Xin<-X[, !out, drop=FALSE] Xout<-X[, out, drop=FALSE] Xout_in<-qr.resid(qr(Xin),Xout) mu<-drop(family(model)$linkinv(eta)) V<-family(model)$variance(mu) d<-drop(family(model)$mu.eta(eta)) Y<-model$y Uin<-Xin*((d/V)*(Y-mu)) Uout<-Xout*((d/V)*(Y-mu)) I_rss98<-fisherinf(newbeta, model, X) ## equation 3.3, Rao, Scott, and Skinner 1998 Atwiddle<-I_rss98[out,!out]%*%solve(I_rss98[!out,!out]) #equation 3.2, ibid Uout_in<-Uout-Uin%*%t(Atwiddle) if(fullrank){ rankcheck<-qr(Uout_in) if (rankcheck$rank1){ N<-ncol(ids) for(i in 2:N){ ids[,i]<-do.call("interaction", ids[,1:i,drop=FALSE]) } } ## force clusters nested in strata if (nest && has.strata && NCOL(ids)){ N<-NCOL(ids) NS<-NCOL(strata) for(i in 1:N) ids[,i]<-do.call("interaction", c(strata[,1:min(i,NS),drop=FALSE], ids[,i,drop=FALSE])) } ## check if clusters nested in strata if (check.strata && nest) warning("No point in check.strata=TRUE if nest=TRUE") if(check.strata && !is.null(strata) && NCOL(ids)){ sc<-(rowSums(table(ids[,1],strata[,1])>0)) if(any(sc>1)) stop("Clusters not nested in strata at top level; you may want nest=TRUE.") } ## force substrata nested in clusters N<-ncol(ids) NS<-ncol(strata) if (N>1){ for(i in 2:N) strata[,i]<-interaction(strata[,min(i,NS)], ids[,i-1]) } ## PPS: valid choices currently are FALSE and "brewer" if (is.logical(pps) && pps) stop("'pps' must be FALSE or a character string") if (is.character(pps)) { pps<-TRUE } ## Finite population correction: specified per observation ## Also incorporates design sample sizes formerly in nPSU if (!is.null(fpc) && !is.numeric(fpc) && !is.data.frame(fpc)) stop("fpc must be a matrix or dataframe or NULL") fpc<-as.fpc(fpc,strata, ids, pps=pps) ## if FPC specified, but no weights, use it for weights if (is.null(probs) && is.null(weights)){ if (is.null(fpc$popsize)){ if (missing(probs) && missing(weights)) warning("No weights or probabilities supplied, assuming equal probability") probs<-rep(1,nrow(ids)) } else { probs<-1/weights(fpc, final=FALSE) } } if (is.numeric(probs) && length(probs)==1) probs<-rep(probs, NROW(variables)) if (length(probs)==0) probs<-rep(1,NROW(variables)) if (NCOL(probs)==1) probs<-data.frame(probs) rval<-list(cluster=ids) rval$strata<-strata rval$has.strata<-has.strata rval$prob<- apply(probs,1,prod) rval$allprob<-probs rval$call<-match.call() rval$variables<-variables rval$fpc<-fpc rval$call<-sys.call(-1) rval$pps<-pps class(rval)<-c("survey.design2","survey.design") if (!is.null(calibrate.formula)){ rval<-recalibrate(rval, calibrate.formula) rval$call<-sys.call(-1) } rval } onestrat<-function(x,cluster,nPSU,fpc, lonely.psu,stratum=NULL,stage=1,cal=cal){ stratum_center <- attr(x, "recentering") if (is.null(stratum_center)) stratum_center <- 0 if (is.null(fpc)) f<-rep(1,NROW(x)) else{ f<-ifelse(fpc==Inf, 1, (fpc-nPSU)/fpc) } if (nPSU>1) scale<-f*nPSU/(nPSU-1) else scale<-f if (all(f<0.0000001))## self-representing stratum return(matrix(0,NCOL(x),NCOL(x))) scale<-scale[!duplicated(cluster)] x<-rowsum(x,cluster) nsubset<-nrow(x) if (nsubset1 || (nPSU>1 & !getOption("survey.adjust.domain.lonely"))) { stratum_center <- colMeans(x) } x<-sweep(x=x, MARGIN=2, STATS=stratum_center, FUN="-") if (nsubset==1 && nPSU>1 && getOption("survey.adjust.domain.lonely")){ warning("Stratum (",stratum,") has only one PSU at stage ",stage) if (lonely.psu=="average" && getOption("survey.adjust.domain.lonely")) scale<-NA } if (nPSU>1){ return(crossprod(x*sqrt(scale))) } else { rval<-switch(lonely.psu, certainty=crossprod(x*sqrt(scale)), remove=crossprod(x*sqrt(scale)), adjust=crossprod(x*sqrt(scale)), average=NA*crossprod(x), fail= stop("Stratum (",stratum,") has only one PSU at stage ",stage), stop("Can't handle lonely.psu=",lonely.psu) ) rval } } onestage<-function(x, strata, clusters, nPSU, fpc, lonely.psu=getOption("survey.lonely.psu"),stage=0, cal){ if (NROW(x)==0) return(matrix(0,NCOL(x),NCOL(x))) ## For the 'adjust' option for lonely PSUs, ## recenter around mean from all PSUs in all strata if (!is.null(lonely.psu) && lonely.psu == "adjust") { n_PSUs_from_all_strata <- sum(tapply(X = nPSU, INDEX = as.numeric(strata), FUN = head, 1)) recentering <- colSums(x) / n_PSUs_from_all_strata } else { recentering <- 0 } stratvars<- tapply(1:NROW(x), list(factor(strata)), function(index){ onestrat(x[index,,drop=FALSE] |> `attr<-`('recentering', recentering), clusters[index], nPSU[index][1], fpc[index], ##changed from fpc[index][1], to allow pps(brewer) lonely.psu=lonely.psu,stratum=strata[index][1], stage=stage,cal=cal) }) p<-NCOL(x) nstrat<-length(unique(strata)) nokstrat<-sum(sapply(stratvars,function(m) !any(is.na(m)))) apply(array(unlist(stratvars),c(p,p,length(stratvars))),1:2,sum,na.rm=TRUE)*nstrat/nokstrat } svyrecvar<-function(x, clusters, stratas, fpcs, postStrata=NULL, lonely.psu=getOption("survey.lonely.psu"), one.stage=getOption("survey.ultimate.cluster")){ x<-as.matrix(x) cal<-NULL if(isFALSE(fpcs$pps)) use_rcpp<-getOption("survey.use_rcpp") else use_rcpp<-FALSE ## Brewer's method, doesn't work with the current rcpp version ## Remove post-stratum means, which may cut across clusters ## Also center the data using any "g-calibration" models if(!is.null(postStrata)){ for (psvar in postStrata){ if (inherits(psvar, "greg_calibration")) { if (psvar$stage==0){ ## G-calibration at population level x<-as.matrix(qr.resid(psvar$qr,x/psvar$w)*psvar$w) } else { ## G-calibration within clusters cal<-c(cal, list(psvar)) use_rcpp<-FALSE } } else if (inherits(psvar, "raking")){ ## raking by iterative proportional fitting for(iterations in 1:10){ for(margin in psvar){ psw<-attr(margin, "weights") x<- x - psw*apply(x/psw, 2, ave, margin) } } } else { ## ordinary post-stratification psw<-attr(psvar, "weights") oldw<-attr(psvar, "oldweights") if (is.null(oldw)) oldw<-rep(1,length(psw)) zeroes<-which(psw==0 & oldw==0) if (length(zeroes)) psw[zeroes]=1 psvar<-as.factor(psvar) psmeans<-rowsum(x*oldw/psw,psvar,reorder=TRUE)/as.vector(by(oldw,psvar,sum)) x<- x-psmeans[match(psvar,sort(unique(psvar))),]*psw } } } if (use_rcpp) { multistage_rcpp(x, clusters,stratas,fpcs$sampsize, fpcs$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage,stage=1,cal=cal) } else { multistage(x, clusters,stratas,fpcs$sampsize, fpcs$popsize, lonely.psu=getOption("survey.lonely.psu"), one.stage=one.stage,stage=1,cal=cal) } } multistage<-function(x, clusters, stratas, nPSUs, fpcs, lonely.psu=getOption("survey.lonely.psu"), one.stage=FALSE,stage,cal){ n<-NROW(x) v <- onestage(x,stratas[,1], clusters[,1], nPSUs[,1], fpcs[,1], lonely.psu=lonely.psu,stage=stage,cal=cal) if (one.stage!=TRUE && !is.null(fpcs) && NCOL(clusters)>1) { v.sub<-by(1:n, list(as.numeric(clusters[,1])), function(index){ ## residuals for G-calibration using population information ## only on clusters at this stage. for(cali in cal){ if (cali$stage != stage) next j<-match(clusters[index,1],cali$index) if (length(unique(j))!=1) stop("Internal problem in g-calibration data: stage",stage, ", cluster", j) j<-j[[1]] x[index,]<-as.matrix(qr.resid(cali$qr[[j]], x[index,,drop=FALSE]/cali$w[[j]])*cali$w[[j]]) } multistage(x[index,,drop=FALSE], clusters[index,-1,drop=FALSE], stratas[index,-1,drop=FALSE], nPSUs[index,-1,drop=FALSE], fpcs[index,-1,drop=FALSE], lonely.psu=lonely.psu,one.stage=one.stage-1, stage=stage+1,cal=cal)*nPSUs[index[1],1]/fpcs[index[1],1] }) for(i in 1:length(v.sub)) v<-v+v.sub[[i]] } dimnames(v)<-list(colnames(x),colnames(x)) v } ## Ben Schneider's code to use C++. Should *agree* with multistage() except ## for one known bug in lonely.psu ## multistage_rcpp <- function(x, clusters, stratas, nPSUs, fpcs, lonely.psu=getOption("survey.lonely.psu"), one.stage=FALSE, stage, cal){ lonely.psu <- switch(lonely.psu, certainty = 'certainty', remove = 'remove', adjust = 'adjust', average = 'average', fail = 'fail', stop("Can't handle lonely.psu=",lonely.psu) ) use_singleton_method_for_domains <- isTRUE(getOption("survey.adjust.domain.lonely")) # Prepare the inputs to pass to Rcpp function if (is.data.frame(clusters)) { for (j in seq_len(ncol(clusters))) { if (!is.numeric(clusters[[j]])) clusters[[j]] <- as.numeric(as.factor(clusters[[j]])) } } clusters <- as.matrix(clusters) if (is.data.frame(stratas)) { for (j in seq_len(ncol(stratas))) { if (!is.numeric(stratas[[j]])) stratas[[j]] <- as.numeric(as.factor(stratas[[j]])) } } stratas <- as.matrix(stratas) if (is.null(fpcs)) { strata_pop_sizes <- matrix(Inf, nrow = nrow(nPSUs), ncol = ncol(nPSUs)) } else { strata_pop_sizes <- as.matrix(fpcs) } strata_samp_sizes <- as.matrix(nPSUs) # Call the Rcpp function v <- arma_multistage(Y = as.matrix(x), samp_unit_ids = clusters, strata_ids = stratas, strata_samp_sizes = strata_samp_sizes, strata_pop_sizes = strata_pop_sizes, singleton_method = lonely.psu, use_singleton_method_for_domains = getOption("survey.adjust.domain.lonely"), use_only_first_stage = one.stage, stage = stage) dimnames(v) <- list(colnames(x),colnames(x)) v } ## fpc not given are zero: full sampling. as.fpc<-function(df,strata,ids,pps=FALSE){ count<-function(x) sum(!duplicated(x)) sampsize<-matrix(ncol=ncol(ids),nrow=nrow(ids)) for(i in 1:ncol(ids)) split(sampsize[,i],strata[,i])<-lapply(split(ids[,i],strata[,i]),count) if (is.null(df)){ ## No fpc rval<-list(popsize=NULL, sampsize=sampsize) class(rval)="survey_fpc" return(rval) } fpc<-as.matrix(df) if (xor(ispopsize<-any(df>1), all(df>=1))){ big<-which(fpc>=1,arr.ind=TRUE) small<-which(fpc<1,arr.ind=TRUE) cat("record",big[1,1]," stage",big[1,2],": fpc=", fpc[big[1,,drop=FALSE]],"\n") cat("record",small[1,1]," stage ",small[1,2],": fpc=", fpc[small[1,,drop=FALSE]],"\n") stop("Must have all fpc>=1 or all fpc<=1") } if (ispopsize){ if(pps) stop("fpc must be specified as sampling fraction for PPS sampling") popsize<-fpc } else { popsize<-sampsize/(fpc) } if (any(popsize100% sampling in some strata") } if (!ispopsize && any(is.finite(popsize) & (popsize>1e10))){ big<-which(popsize>1e10 & is.finite(popsize),arr.ind=TRUE) warning("FPC implies population larger than ten billion (record",big[1,1]," stage ",big[1,2],")") } if(!pps){ ## check that fpc is constant within strata. for(i in 1:ncol(popsize)){ diff<-by(popsize[,i], list(strata[,i]), count) if (any(as.vector(diff)>1)){ j<-which(as.vector(diff)>1)[1] warning("`fpc' varies within strata: stratum ",names(diff)[j], " at stage ",i) } } } else{ ## check that fpc is constant with clusters diff<-by(popsize[,i], list(ids[,i]), count) if (any(as.vector(diff)>1)){ j<-which(as.vector(diff)>1)[1] warning("`fpc' varies within cluster: cluster ",names(diff)[j], " at stage ",i) } } rval<-list(popsize=popsize, sampsize=sampsize,pps=pps) class(rval)<-"survey_fpc" rval } "weights.survey_fpc"<-function(object,final=TRUE,...){ if (is.null(object$popsize) || any(object$popsize>1e12)) stop("Weights not supplied and can't be computed from fpc.") if (final) { pop<-apply(object$popsize,1,prod) samp<-apply(object$sampsize,1,prod) pop/samp } else { object$popsize/object$sampsize } } print.survey.design2<-function(x,varnames=FALSE,design.summaries=FALSE,...){ n<-NROW(x$cluster) if (x$has.strata) cat("Stratified ") un<-length(unique(x$cluster[,1])) if(n==un){ cat("Independent Sampling design") is.independent<-TRUE if (is.null(x$fpc$popsize)) cat(" (with replacement)\n") else cat("\n") } else { cat(NCOL(x$cluster),"- level Cluster Sampling design") if (is.null(x$fpc$popsize)) cat(" (with replacement)\n") else cat("\n") nn<-lapply(x$cluster,function(i) length(unique(i))) cat(paste("With (",paste(unlist(nn),collapse=", "),") clusters.\n",sep="")) is.independent<-FALSE } print(x$call) if (design.summaries){ cat("Probabilities:\n") print(summary(x$prob)) if(x$has.strata){ if (NCOL(x$cluster)>1) cat("First-level ") cat("Stratum Sizes: \n") oo<-order(unique(x$strata[,1])) a<-rbind(obs=table(x$strata[,1]), design.PSU=x$fpc$sampsize[!duplicated(x$strata[,1]),1][oo], actual.PSU=table(x$strata[!duplicated(x$cluster[,1]),1])) print(a) } if (!is.null(x$fpc$popsize)){ if (x$has.strata) { cat("Population stratum sizes (PSUs): \n") s<-!duplicated(x$strata[,1]) a<-x$fpc$popsize[s,1] names(a)<-x$strata[s,1] a<-a[order(names(a))] print(a) } else { cat("Population size (PSUs):",x$fpc$popsize[1,1],"\n") } } } if (varnames){ cat("Data variables:\n") print(colnames(x)) } invisible(x) } summary.survey.design2<-function(object,...){ class(object)<-c("summary.survey.design2",class(object)) object } print.summary.survey.design2<-function(x,...){ y<-x class(y)<-c("survey.design2",class(x)) print(y,varnames=TRUE,design.summaries=TRUE,...) } .svycheck<-function(object){ if (inherits(object,"survey.design") && !is.null(object$nPSU)) warning("This is an old-style design object. Please use as.svydesign2 to update it.") } as.svydesign2<-function(object){ if (inherits(object,"survey.design2")) return(object) if (!inherits(object,"survey.design")) stop("This function is for updating old-style survey.design objects") count<-function(x) length(unique(x)) strata<-data.frame(one=object$strata) if ((nc<-ncol(object$cluster))>1){ for(i in 2:nc){ strata<-cbind(strata,object$cluster[,i-1]) } } sampsize<-matrix(ncol=nc,nrow=nrow(object$cluster)) sampsize[,1]<-object$nPSU[match(object$strata, names(object$nPSU))] if (nc>1){ for(i in 2:nc){ split(sampsize[,i],strata[,i])<-lapply(split(object$cluster[,i],strata[,i]),count) } } if (!is.null(object$fpc)){ popsize<-sampsize popsize[,1]<-object$fpc$N[match(object$strata,object$fpc$strata)] } else popsize<-NULL if (nc>1 && !is.null(object$fpc)){ warning("Assuming complete sampling at stages 2 -",nc) } fpc<-list(popsize=popsize,sampsize=sampsize) class(fpc)<-"survey_fpc" object$fpc<-fpc object$strata<-strata object$nPSU<-NULL class(object)<-c("survey.design2","survey.design") object } is.pps<-function(x) if(is.null(x$pps)) FALSE else (x$pps!=FALSE) "[.survey.design2"<-function (x,i, ..., drop=TRUE){ if (!missing(i)){ if (is.calibrated(x) || is.pps(x) || !drop){ ## Set weights to zero: no memory saving possible ## There should be an easier way to complement a subscript.. if (is.logical(i)) x$prob[!i]<-Inf else if (is.numeric(i) && length(i)) x$prob[-i]<-Inf else { tmp<-x$prob[i,] x$prob<-rep(Inf, length(x$prob)) x$prob[i,]<-tmp } index<-is.finite(x$prob) psu<-!duplicated(x$cluster[index,1]) tt<-table(x$strata[index,1][psu]) if(any(tt==1) && getOption("survey.adjust.domain.lonely")){ warning(sum(tt==1)," strata have only one PSU in this subset.") } } else { ## subset everything. if (!is.null(x$variables)) ## phase 2 of twophase design x$variables<-"[.data.frame"(x$variables,i,..1,drop=FALSE) x$cluster<-x$cluster[i,,drop=FALSE] x$prob<-x$prob[i] x$allprob<-x$allprob[i,,drop=FALSE] x$strata<-x$strata[i,,drop=FALSE] x$fpc$sampsize<-x$fpc$sampsize[i,,drop=FALSE] x$fpc$popsize<-x$fpc$popsize[i,,drop=FALSE] } } else { if(!is.null(x$variables)) x$variables<-x$variables[,..1,drop=FALSE] } x } svytotal.survey.design2<-function(x,design, na.rm=FALSE, deff=FALSE,influence=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$variables,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else{ if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if (length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } N<-sum(1/design$prob) total <- colSums(x/as.vector(design$prob),na.rm=na.rm) class(total)<-"svystat" attr(total, "var")<-v<-svyrecvar(x/design$prob,design$cluster, design$strata, design$fpc, postStrata=design$postStrata) attr(total,"statistic")<-"total" if (influence){ if (na.rm && (length(nas)>length(design$prob))) { infl<-matrix(0,ncol=NCOL(x),nrow=length(nas)) infl[nas==0,]<-x/design$prob attr(total, "influence")<-infl } else { attr(total, "influence")<-x/design$prob } } if (is.character(deff) || deff){ nobs<-sum(weights(design)!=0) if (deff=="replace") vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design))^2/nobs else vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design))^2*(N-nobs)/(N*nobs) attr(total, "deff")<-v/vsrs } return(total) } svymean.survey.design2<-function(x,design, na.rm=FALSE,deff=FALSE,influence=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$variables,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if (length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } pweights<-1/design$prob psum<-sum(pweights) average<-colSums(x*pweights/psum) x<-sweep(x,2,average) v<-svyrecvar(x*pweights/psum,design$cluster,design$strata, design$fpc, postStrata=design$postStrata) attr(average,"var")<-v attr(average,"statistic")<-"mean" if (influence){ if (na.rm && (length(nas)>length(pweights))) { infl<-matrix(0,ncol=NCOL(x),nrow=length(nas)) infl[nas==0,]<-x*pweights/psum } else { infl<-x*pweights/psum } attr(average,"influence") <- infl } class(average)<-"svystat" if (is.character(deff) || deff){ nobs<-sum(weights(design)!=0) if(deff=="replace"){ vsrs<-svyvar(x,design,na.rm=na.rm)/(nobs) } else { if(psumlength(design$prob))) { infl<-matrix(0,ncol=NCOL(r),nrow=length(nas)) infl[nas==0,]<-r/design$prob } else { infl<-r/design$prob } attr(rval,"influence") <- infl } class(rval)<-"svyratio" rval } survey/R/svynls.R0000644000176200001440000001506614113105662013500 0ustar liggesusers## svynls svynls<-function(formula, design, start, weights=NULL, ...){ UseMethod("svynls", design) } var_power<-function(d, maxit=3){ ## this should use svytotal dispersion<-0 rval<-list( precision_weights=function(res,mu){ dispersion<<-sum((res^2)^d) /sum(abs(mu)^d) variance<-dispersion*abs(mu)^d 1/variance }, iterations=maxit, name=paste0("weights: variance =",signif(dispersion,3),"mu^",d) ) class(rval)<-"svynls_weights" rval } svynls.DBIsvydesign<-function(formula, design,start, weights=NULL, ...){ design$variables <- getvars(formula, design$db$connection, design$db$tablename, updates = design$updates, subset = design$subset) NextMethod("svynls", design) } utils::globalVariables(c(".survey.prob.weight", ".survey.repwt")) svynls.svyrep.design<-function(formula, design, start, weights=NULL, ..., return.replicates=FALSE){ has_vars<- intersect(all.vars(formula),colnames(design)) dat<-model.frame(design)[,has_vars] if (is.numeric(weights)) prior.weights<-weights else prior.weights<-rep(1, nrow(dat)) meanweight<-mean(weights(design, "sampling")) dat$.survey.prob.weight<-prior.weights*weights(design, "sampling")/meanweight if (inherits(weights, "svynls_weights")){ maxit<-weights$iterations } else { maxit<-0 } first<-nls(formula,dat, weights=.survey.prob.weight,start=start, ...) for(i in seq_len(maxit)){ prior.weights<-weights$precision_weights(fitted(first), resid(first)) dat$.survey.prob.weight<-prior.weights*weights(design, "sampling")/meanweight first<-nls(formula, dat, weights=.survey.prob.weight,start=start, ...) first$precision_weights<-prior.weights } theta<-coef(first) repwts<-weights(design,"analysis") thetas<-matrix(0, ncol=length(theta),nrow=ncol(repwts)) for(i in ncol(repwts)){ dat$.survey.repwt<-prior.weights*repwts[,i]/meanweight model<-nls(formula,dat, weights=.survey.repwt,start=theta, ...) thetas[i, ] <- coef(model) } rval<-list() rval$fit<-first v<-svrVar(thetas, design$scale, design$rscales, coef=theta) rval$naive.cov<-summary(first)$cov.unscaled rval$cov<-v rval$coef<-theta rval$design<-design rval$meanweight<-meanweight rval$call<-sys.call(-1) if (return.replicates) rval$replicates<-thetas class(rval)<-"svynls" rval } svynls.survey.design2<-function(formula, design, start, weights=NULL, ..., influence=FALSE){ has_vars<- intersect(all.vars(formula),colnames(design)) dat<-model.frame(design)[,has_vars] if (is.numeric(weights)) prior.weights<-weights else prior.weights<-rep(1, nrow(dat)) meanweight<-mean(weights(design, "sampling")) w<-prior.weights*weights(design, "sampling")/meanweight if (inherits(weights, "svynls_weights")){ maxit<-weights$iterations } else { maxit<-0 } dat$.survey.prob.weight<-w fit<-nls(formula, dat, weights=.survey.prob.weight,start=start, ...) for(i in seq_len(maxit)){ precwt<-weights$precision_weights(fitted(fit), resid(fit)) w<-precwt*weights(design, "sampling")/meanweight dat$.survey.prob.weight<-w fit<-nls(formula, dat, weights=.survey.prob.weight,start=start, ...) fit$precision_weights<-precwt } v0<-summary(fit)$cov.unscaled theta<-coef(fit) grads<-fit$m$gradient()/sqrt(w) ## nls scales by sqrt(weights) resids<-fit$m$resid() infl<-resids*grads%*%v0 v<-svyrecvar(infl*w, design$cluster, design$strata, design$fpc, postStrata = design$postStrata) rval<-list() rval$fit<-fit rval$coef<-theta rval$cov<-v rval$naive.cov<-vcov(fit) rval$call<-sys.call(-1) rval$design<-design rval$meanweight<-meanweight if(influence) attr(rval,"inference")<-infl class(rval)<-"svynls" rval } .p.nls.convInfo<-function (x, digits, show. = getOption("show.nls.convergence", TRUE)) { if (!is.null(x$convInfo)) with(x$convInfo, { if (identical(x$call$algorithm, "port")) cat("\nAlgorithm \"port\", convergence message: ", stopMessage, "\n", sep = "") else { if (!isConv || show.) { cat("\nNumber of iterations", if (isConv) "to convergence:" else "till stop:", finIter, "\nAchieved convergence tolerance:", format(finTol, digits = digits)) cat("\n") } if (!isConv) { cat("Reason stopped:", stopMessage) cat("\n") } } }) invisible() } print.svynls<-function (x, digits = max(3L, getOption("digits") - 3L), ...) { cat("Nonlinear survey regression model\n") cat(" model: ", deparse(formula(x$fit)), "\n", sep = "") cat(" design: ") print(x$design) print(x$fit$m$getAllPars(), digits = digits, ...) cat(" ", "weighted ", "residual sum-of-squares: ", format(x$fit$m$deviance()*x$meanweight, digits = digits), "\n", sep = "") .p.nls.convInfo(x, digits = digits) invisible(x) } coef.svynls<-function(object,...) object$coef vcov.svynls<-function(object,...) object$cov summary.svynls <- function (object, correlation = FALSE, ...) { r <- as.vector(object$fit$m$resid()) w <- object$fit$weights n <- if (!is.null(w)) sum(w > 0) else length(r) param <- coef(object) pnames <- names(param) p <- length(param) rdf <- n - p resvar <- if (rdf <= 0) NaN else deviance(object$fit)/rdf se <-SE(object) tval <- param/se param <- cbind(param, se, tval, 2 * pt(abs(tval), rdf, lower.tail = FALSE)) dimnames(param) <- list(pnames, c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) ans <- list(formula = formula(object$fit), residuals = r, sigma = sqrt(resvar), df = c(p, rdf), cov.unscaled = vcov(object)/sqrt(resvar), cov.scaled=vcov(object), call = object$call, convInfo = object$fit$convInfo, control = object$fit$control, na.action = object$fit$na.action, coefficients = param, parameters = param) if (correlation && rdf > 0) { ans$correlation <- cov2cor(vcov(object)) } class(ans) <- c("summary.svynls","summary.nls") ans } survey/R/svykm.R0000644000176200001440000002525114423650257013321 0ustar liggesusers svykm<-function(formula, design, se=FALSE, ...) UseMethod("svykm",design) svykm.survey.design<-function(formula, design,se=FALSE, ...){ if (!inherits(formula,"formula")) stop("need a formula") if (length(formula)!=3) stop("need a two-sided formula") mf<-model.frame(formula, model.frame(design), na.action=na.pass) mf<-na.omit(mf) drop<-attr(mf,"na.action") if (!is.null(drop)) design<-design[-drop,] y<-model.response(mf) if (!is.Surv(y) || attr(y,"type")!="right") stop("response must be a right-censored Surv object") if (ncol(mf)==1) { if (se) s<-km.stderr(y,design) else s<-svykm_fit(y,weights(design)) } else { x<-mf[,-1] if (NCOL(x)>1) groups<-do.call(interaction,x) else groups<-as.factor(x) if (se){ lhs<-formula lhs[[3]]<-1 s<-lapply(levels(groups), function(g) svykm(lhs, subset(design,groups==g),se=TRUE)) }else{ s<-lapply(levels(groups), function(g) svykm_fit(y[groups==g],weights(design)[groups==g])) } names(s)<-levels(groups) class(s)<-"svykmlist" } call<-match.call() call[[1]]<-as.name(.Generic) attr(s,"call")<-call attr(s, "formula")<-formula attr(s, "na.action")<-drop return(s) } svykm.svyrep.design<-function(formula, design,se=FALSE, ...){ if (!inherits(formula,"formula")) stop("need a formula") if (length(formula)!=3) stop("need a two-sided formula") mf<-model.frame(formula, model.frame(design), na.action=na.pass) mf<-na.omit(mf) drop<-attr(mf,"na.action") if (!is.null(drop)) design<-design[-drop,] y<-model.response(mf) if (!is.Surv(y) || attr(y,"type")!="right") stop("response must be a right-censored Surv object") if (ncol(mf)==1) { if (se) stop("SE not yet available") else s<-svykm_fit(y,weights(design,"sampling")) } else { x<-mf[,-1] if (NCOL(x)>1) groups<-do.call(interaction,x) else groups<-as.factor(x) if (se){ lhs<-formula lhs[[3]]<-1 s<-lapply(levels(groups), function(g) svykm(lhs, subset(design,groups==g),se=TRUE)) }else{ s<-lapply(levels(groups), function(g) svykm_fit(y[groups==g],weights(design)[groups==g])) } names(s)<-levels(groups) class(s)<-"svykmlist" } call<-match.call() call[[1]]<-as.name(.Generic) attr(s,"call")<-call attr(s, "formula")<-formula attr(s, "na.action")<-drop return(s) } svykm_fit<-function(y,w){ t<-y[,"time"] s<-y[,"status"] nn<-rowsum(cbind(s,1)*w,t) tt<-sort(unique(t)) N<-c(sum(w),sum(w),sum(w)-cumsum(nn[-nrow(nn),2])) d<-c(0,nn[,1]) surv<-pmax(0,cumprod(1-d/N)) rval<-list(time=c(0,tt), surv=surv) class(rval)<-"svykm" rval } km.stderr<-function(survobj,design){ time<-survobj[,'time'] status<-survobj[,'status'] ## Brute force and ignorance: compute Y and dN as totals, use delta-method keep<-which((status==1) & (weights(design)!=0)) y<-outer(time,time[keep],">=") dN<-diag(status)[,keep,drop=FALSE] oo<-order(time[keep], -status[keep]) okeep<-keep[oo] ntimes<-length(oo) ttime<-time[okeep] sstatus<-status[okeep] totals<-svytotal(cbind(dN[,oo,drop=FALSE],y[,oo,drop=FALSE]), design) rm(dN) y<-coef(totals)[-(1:ntimes)] dNbar<-coef(totals)[1:ntimes] h<-cumsum(dNbar/y) dVn<- vcov(totals)[(1:ntimes),(1:ntimes)]/outer(y,y) dVy <- vcov(totals)[-(1:ntimes),-(1:ntimes)]*outer(dNbar/y^2,dNbar/y^2) dCVny<- -vcov(totals)[(1:ntimes),-(1:ntimes)]*outer(1/y,dNbar/y^2) dV<-dVn+dVy+dCVny+t(dCVny) V<-numeric(ntimes) if (ntimes>0) V[1]<-dV[1,1] if (ntimes>1) for(i in 2:ntimes) V[i]<-V[i-1]+sum(dV[1:(i-1),i])+sum(dV[i,1:i]) rval<-list(time=ttime,surv=exp(-h),varlog=V) class(rval)<-"svykm" rval } plot.svykm<-function(x,xlab="time",ylab="Proportion surviving",ylim=c(0,1),ci=NULL,lty=1,...){ if (is.null(ci)) ci<-!is.null(x$varlog) plot(x$time,x$surv,xlab=xlab,ylab=ylab, type="s",ylim=ylim,lty=lty,...) if (ci){ if (is.null(x$varlog)) warning("No standard errors available in object") else{ lines(x$time,exp(log(x$surv)-1.96*sqrt(x$varlog)),lty=2,type="s",...) lines(x$time,pmin(1,exp(log(x$surv)+1.96*sqrt(x$varlog))),lty=2,type="s",...) } } invisible(x) } lines.svykm<-function(x,xlab="time",type="s",ci=FALSE,lty=1,...){ lines(x$time,x$surv, type="s",lty=lty,...) if (ci){ if (is.null(x$varlog)) warning("no standard errors available in object") else { lines(x$time,exp(log(x$surv)-1.96*sqrt(x$varlog)),lty=2,type="s",...) lines(x$time,pmin(1,exp(log(x$surv)+1.96*sqrt(x$varlog))),lty=2,type="s",...) } } invisible(x) } plot.svykmlist<-function(x, pars=NULL, ci=FALSE,...){ if (!is.null(pars)) pars<-as.data.frame(pars,stringsAsFactors=FALSE) if(is.null(pars)) plot(x[[1]],ci=ci,...) else do.call(plot,c(list(x[[1]]),pars[1,,drop=FALSE],ci=ci,...)) m<-length(x) if(m==1) return() for(i in 2:m){ if(is.null(pars)) lines(x[[i]],ci=ci,...) else do.call(lines,c(list(x[[i]]),pars[i,,drop=FALSE],ci=ci,...)) } invisible(x) } print.svykm<-function(x, digits=3,...,header=TRUE){ if (header) {cat("Weighted survival curve: ") print(attr(x,"call"))} suppressWarnings({iq1<-min(which(x$surv<=0.75)) iq2<-min(which(x$surv<=0.5)) iq3<-min(which(x$surv<=0.25))}) if (is.finite(iq1)) q1<-x$time[iq1] else q1<-Inf if (is.finite(iq2)) q2<-x$time[iq2] else q2<-Inf if (is.finite(iq3)) q3<-x$time[iq3] else q3<-Inf cat("Q1 =",round(q1,digits)," median =",round(q2,digits)," Q3 =",round(q3,digits),"\n") invisible(x) } print.svykmlist<-function(x, digits=3,...){ cat("Weighted survival curves:\n") print(attr(x,"call")) for(i in 1:length(x)){ cat(names(x)[i],": ") print(x[[i]],digits=digits,header=FALSE) } invisible(x) } quantile.svykm<-function(x, probs=c(0.75,0.5,0.25),ci=FALSE,level=0.95,...){ iq<-sapply(probs, function(p) suppressWarnings(min(which(x$surv<=p)))) qq<-sapply(iq, function(i) if (is.finite(i)) x$time[i] else Inf) names(qq)<-probs if (ci){ if(is.null(x$varlog)){ warning("no confidence interval available.") } else { halfalpha<-(1-level)/2 z<-qnorm(halfalpha, lower.tail=FALSE) su<-exp(log(x$surv)+z*sqrt(x$varlog)) iu<-sapply(probs, function(p) suppressWarnings(min(which(su<=p)))) qu<-sapply(iu, function(i) if (is.finite(i)) x$time[i] else Inf) sl<-exp(log(x$surv)-z*sqrt(x$varlog)) il<-sapply(probs, function(p) suppressWarnings(min(which(sl<=p)))) ql<-sapply(il, function(i) if (is.finite(i)) x$time[i] else Inf) ci<-cbind(ql,qu) rownames(ci)<-probs colnames(ci)<-format(c(halfalpha,1-halfalpha),3) attr(qq,"ci")<-ci } } qq } confint.svykm<-function(object, parm, level=0.95,...){ if (is.null(object$varlog)) stop("no standard errors in object") parm<-as.numeric(parm) idx<-sapply(parm, function(t) max(which(object$time<=t))) z<-qnorm((1-level)/2) ci<-exp(log(object$surv[idx])+outer(sqrt(object$varlog[idx]),c(z,-z))) ci[,2]<-pmin(ci[,2],1) rownames(ci)<-parm colnames(ci)<-format( c((1-level)/2, 1-(1-level)/2),3) ci } predict.svycoxph<-function(object, newdata, se=FALSE, type=c("lp", "risk", "terms","curve"), ...){ type<-match.arg(type) if(type!="curve") return(NextMethod()) design<-object$survey.design response<-object$y if (!is.null(attr(terms(object), "specials")$strata)) stop("Stratified models are not supported yet") if (attr(response,"type")=="counting"){ time<-object$y[,2] status<-object$y[,'status'] entry<-object$y[,1] } else if (attr(response,'type')=="right"){ time<-object$y[,"time"] status<-object$y[,"status"] entry<-rep(-Inf,length(time)) } else stop("unsupported survival type") if(is.null(object$na.action)){ design<-object$survey.design } else { design<-object$survey.design[-object$na.action,] } ff<-delete.response(terms(formula(object))) zmf<-model.frame(ff, newdata) z.pred<-model.matrix(ff, zmf)[,-1,drop=FALSE] ## ## The simple case first ## risk<-getS3method("predict","coxph")(object,type="risk",se.fit=FALSE) if(se==FALSE){ tt<-c(time,entry) ss<-c(status,rep(0,length(entry))) ee<-c(rep(1,length(status)),rep(-1,length(entry))) oo<-order(tt,-ee,-ss) dN<-ss[oo] w<-rep(weights(design),2)[oo] risks<-rep(risk,2) Y<-rev(cumsum(rev(risks[oo]*w*ee[oo]))) keep<-dN>0 s<-vector("list",nrow(z.pred)) beta<-coef(object) h0<- cumsum( (w*dN/Y)[keep] ) for(i in 1:nrow(z.pred)){ zi<-z.pred[i,]-object$means s[[i]]<-list(time=time[oo][keep], surv=exp(-h0 * exp(sum(beta*zi))), call=sys.call()) class(s[[i]])<-c("svykmcox","svykm") } names(s)<-rownames(newdata) return(s) } ## ## The hard case: curves with standard errors ## if(!inherits(design,"survey.design")) stop("replicate-weight designs not supported yet") keep<-which((status==1) & (weights(design)!=0)) y<-outer(time,time[keep],">=")*risk*outer(entry,time[keep],"<=") dN<-diag(status)[,keep] oo<-order(time[keep], -status[keep]) okeep<-keep[oo] ntimes<-length(oo) ttime<-time[okeep] sstatus<-status[okeep] totals<-svytotal(cbind(dN[,oo],y[,oo]), design) rm(dN) y<-coef(totals)[-(1:ntimes)] dNbar<-coef(totals)[1:ntimes] vtotals<-vcov(totals) rm(totals) h<-cumsum(dNbar/y) dVn<- vtotals[(1:ntimes),(1:ntimes)]/outer(y,y) dVy <- vtotals[-(1:ntimes),-(1:ntimes)]*outer(dNbar/y^2,dNbar/y^2) dCVny<- -vtotals[(1:ntimes),-(1:ntimes)]*outer(1/y,dNbar/y^2) dV<-dVn+dVy+dCVny+t(dCVny) det<-suppressWarnings(coxph.detail(object)) ze<-sweep(as.matrix(det$means)[rep(1:length(det$time), det$nevent),,drop=FALSE], 2, object$means) rm(det) dH<-dNbar/y h.ze<-dH*ze varbeta<-vcov(object) Vh<-numeric(ntimes) Vh[1]<-dV[1,1] for(i in 2:ntimes) Vh[i]<-Vh[i-1]+sum(dV[1:(i-1),i])+sum(dV[i,1:i]) dVb<-numeric(ntimes) for(i in 1:ntimes) dVb[i]<-crossprod(h.ze[i,],varbeta%*%(h.ze[i,])) Vb<-cumsum(dVb) dCV<-matrix(nrow=ntimes,ncol=NCOL(ze)) for(i in 1:ntimes) dCV[i,] <- -varbeta%*%(h.ze[i,]) CV<-apply(dCV,2,cumsum) V0<-Vh+Vb s0<-exp(-h) s<-vector("list",nrow(z.pred)) for(i in 1:nrow(z.pred)){ zi<-z.pred[i,]-object$means riski<-exp(sum(zi*coef(object))) Vz<-drop(crossprod(zi,varbeta%*%zi))*riski^2*h^2 CVz<-colSums(t(dCV)*zi)*riski^2*h V<-V0*riski^2+Vz+CVz*2 s[[i]]<-list(time=ttime,surv=exp(-h*riski), varlog=V) class(s[[i]])<-c("svykm.cox","svykm") } names(s)<-rownames(newdata) scall<-sys.call() scall[[1]]<-as.name(.Generic) attr(s,"call")<-scall class(s)<-c("svykmlist.cox","svykmlist") return(s) } survey/R/twophase2.R0000644000176200001440000005736214565003231014063 0ustar liggesusers## ## Constructing cov(R_i,R_j)/pi^*_ij, or \check{\check{\Delta}}_ij in Sarndal's notation ## We use this form because it can be sparse and because it is easy to combine ## multistage and multiphase sampling. ## ## For (stratified, cluster) simple random sampling, ## Dcheck simplifies to (n/N-1)/(n-1) or -(1-p)/(n-1) ## Dcheck_strat<-function(strata, prob){ if (is.character(strata)) strata<-as.factor(strata) strata<-as.numeric(strata) ## for ave() n<-length(strata) rval<-matrix(0, n,n) sampsize<-ave(strata,strata,FUN=length) strats<-unique(strata) for(strat in strats){ these <- strata == strat rval[these,these]<- -(1-prob[these])/(sampsize[these]-1) } diag(rval)<-(1-prob) rval } Dcheck_multi<-function(id,strata,probs){ nstage<-NCOL(id) rval<-matrix(0,NROW(id),NROW(id)) for(stage in 1:nstage){ uid<-!duplicated(id[,stage]) idx<-match(id[,stage],id[uid,stage]) this_stage<-Dcheck_strat(strata[uid,stage],probs[uid,stage])[idx,idx] rval<- twophaseDcheck(rval, this_stage) } rval } ## subsetting now happens in Dcheck_multi_subset ## (no: need to know strata before subsetting to get sampsize. Dcheck_subset<-function(strata, prob,sampsize, withreplacement){ if (is.character(strata)) strata<-as.factor(strata) strata<-as.numeric(strata) ## for ave() N<-length(strata) n<-NROW(strata) rval<-matrix(0, n,n) #sampsize<-ave(strata,strata,FUN=length) strats<-unique(strata) if (!withreplacement){ for(strat in strats){ these <- strata == strat ithese<-which(these) rval[these,these]<- -(1-prob[ithese])/(sampsize[ithese]-1) } } diag(rval)<-(1-prob) rval } oldDcheck_subset<-function(strata, subset, prob, withreplacement){ if (is.character(strata)) strata<-as.factor(strata) strata<-as.numeric(strata) ## for ave() N<-length(strata) n<-sum(subset) rval<-matrix(0, n,n) sampsize<-ave(strata,strata,FUN=length) strats<-unique(strata) if (!withreplacement){ for(strat in strats){ these <- strata == strat ithese<-which(these) rval[these[subset],these[subset]]<- -(1-prob[ithese[subset]])/(sampsize[ithese[subset]]-1) } } diag(rval)<-(1-prob[subset]) rval } oldDcheck_multi_subset<-function(id,strata,subset,probs,withreplacement){ nstage<-NCOL(id) n<-sum(subset) rval<-matrix(0,n,n) if (all(probs==1) && withreplacement) return(as(diag(n),"sparseMatrix")) for(stage in 1:nstage){ uid<-!duplicated(id[,stage]) insubset<-rowsum(as.integer(subset),id[,stage],reorder=FALSE)>0 idx<-match(id[subset,stage],id[subset,stage][uid]) this_stage<-Dcheck_subset(strata[uid,stage],insubset,probs[uid,stage],withreplacement)[idx,idx] rval<- twophaseDcheck(rval, this_stage) } rval } Dcheck_multi_subset<-function(id,strata,subset,probs,withreplacement){ nstage<-NCOL(id) n<-sum(subset) rval<-matrix(0,n,n) if (all(probs==1) && withreplacement) return(as(diag(n),"sparseMatrix")) sampsize<-NULL for(stage in 1:nstage){ uid<-rep(FALSE,NROW(id)) uid[subset]<-!duplicated(id[subset,stage]) insubset<-rowsum(as.integer(subset),id[,stage],reorder=FALSE)>0 idx<-match(id[subset,stage],id[subset,stage][uid[subset]]) sampsize<-ave(as.numeric(id[,stage]),strata[,stage],FUN=function(i) length(unique(i))) this_stage<-Dcheck_subset(strata[uid,stage],probs[uid,stage],sampsize, withreplacement)[idx,idx] rval<- twophaseDcheck(rval, this_stage) } rval } twophaseDcheck<-function(Dcheck1,Dcheck2){ as(-Dcheck1*Dcheck2+Dcheck1+Dcheck2,"sparseMatrix") } make_covmat<-function(design1,design2,subset){ if (!is.null(design1$dcheck)) phase1<-design1$dcheck[[1]]$dcheck else{ withreplacement<-is.null(design1$fpc$popsize) phase1<-Dcheck_multi_subset(design1$cluster, design1$strata, subset, design1$allprob, withreplacement) } if (!is.null(design2$dcheck)) phase2<-design2$dcheck[[1]]$dcheck else phase2<-Dcheck_multi(design2$cluster, design2$strata, design2$allprob) dcheck<-twophaseDcheck(phase1,phase2) list(phase1=phase1,phase2=phase2,full=dcheck) } ## ## Based on twophase(), so it computes some stuff that is no longer necessary. ## Will be pruned in the future. ## twophase2<-function(id,strata=NULL, probs=NULL, fpc=NULL, pps=NULL, subset, data){ data<-detibble(data) pps2<-NULL if(!is.null(pps)){ if (!is.null(pps[[1]])) stop("can't handle pps= at phase 1 yet") pps2<-pps[[2]] } d1<-svydesign(ids=id[[1]],strata=strata[[1]],weights=NULL, probs=probs[[1]],fpc=fpc[[1]],data=data) if(inherits(subset,"formula")) subset<-eval.parent(model.frame(subset,data=data,na.action=na.pass))[[1]] if(!is.logical(subset) && sort(unique(subset))==c(0,1)) subset<-as.logical(subset) if (any(is.na(subset))) stop("missing values in 'subset'") d1s<-svydesign(ids=id[[1]],strata=strata[[1]],weights=NULL, probs=probs[[1]],fpc=fpc[[1]],data=data[subset,]) d1s$prob<-d1$prob[subset] d1s$allprob<-d1$allprob[subset,,drop=FALSE] ## work out phase-two fpc if (is.null(fpc[[2]])){ complete.vars<-names(data)[apply(data, 2, function(v) all(!is.na(v)))] if (all(c(all.vars(id[[2]]), all.vars(strata[[2]])) %in% complete.vars)){ dfpc<-svydesign(ids=id[[2]], strata=strata[[2]], data=data, probs=NULL) popsize<-mapply(function(s,i) ave(!duplicated(i),s,FUN=sum), dfpc$strata, dfpc$cluster) rm(dfpc) } else { warning("Second-stage fpc not specified and not computable") popsize<-NULL } } else popsize<-NULL if (is.null(pps2)){ d2<-svydesign(ids=id[[2]], strata=strata[[2]], probs=probs[[2]], weights=NULL, fpc=fpc[[2]], data=data[subset,]) } else { d2<-svydesign(ids=id[[2]], strata=strata[[2]], weights=NULL, fpc=probs[[2]], data=data[subset,],pps=pps2) } ## ugly hack to get nicer labels if(!is.null(fpc[[2]])){ d2call<-bquote(svydesign(ids=.(id[[2]]),strata=.(strata[[2]]), probs=.(probs[[2]]), fpc=.(fpc[[2]]))) } else{ d2call<-bquote(svydesign(ids=.(id[[2]]),strata=.(strata[[2]]), probs=.(probs[[2]]), fpc=`*phase1*`)) } for(i in names(d2call)[-1]) d2call[[i]]<-d2call[[i]] d2$call<-d2call d1call<-bquote(svydesign(ids=.(id[[1]]), strata=.(strata[[1]]), probs=.(probs[[1]]), fpc=.(fpc[[1]]))) for(i in names(d1call)[-1]) d1call[[i]]<-d1call[[i]] d1$call<-d1call ## Add phase 2 fpc and probs if they were computed rather than specified. if (!is.null(popsize)) d2$fpc<-as.fpc(popsize[subset,,drop=FALSE],d2$strata,d2$cluster) if(is.null(probs[[2]]) && !is.null(d2$fpc$popsize)){ d2$allprob<-1/weights(d2$fpc,final=FALSE) d2$prob<-apply(as.data.frame(d2$allprob),1,prod) } d2$variables<-NULL deltacheck<-make_covmat(d1,d2, subset) rval<-list(phase1=list(full=d1,sample=d1s), phase2=d2, subset=subset, dcheck=deltacheck) rval$prob<-rval$phase1$sample$prob ## Are phase 2 PSUs the same as Phase 1 USUs, or smaller? rval$samescale<- !any(duplicated(d1s$cluster[,NCOL(d1s$cluster)][!duplicated(d2$cluster[,1])])) ## For each phase 1 sampling unit, need probability of being represented ## at phase 2. nunique<-function(x) sum(!duplicated(x)) m<-NCOL(rval$phase1$sample$cluster) if(d2$has.strata){ if (inherits(strata[[2]],"formula")) sa<-eval(attr(terms(strata[[2]]),"variables")[[2]],d1$variables) else sa<-d1$strata[,1] cm<-rval$phase1$full$cluster[,m] if (nunique(sa)!=nunique(sa[subset])) stop("Some phase-2 strata have zero sampling fraction") rval$usu<-ave(as.numeric(cm[subset]),sa[subset],FUN=nunique)/ave(as.numeric(cm),sa,FUN=nunique)[subset] } else { rval$usu<-drop(with(rval$phase1$sample,ave(as.numeric(cluster[,m]), strata[,m], FUN=nunique))/rval$phase1$full$fpc$sampsize[rval$subset]) } if (length(rval$phase1$sample$prob)==length(d2$prob)) rval$prob<-rval$phase1$sample$prob*d2$prob else{ rval$prob<-rep(Inf,length(rval$phase1$sample$prob)) rval$prob[subset]<-rval$prob[subset]*d2$prob } rval$call<-sys.call() class(rval) <- c("twophase2","survey.design") rval } print.twophase2<-function(x,...){ cat("Two-phase sparse-matrix design:\n ") print(x$call) cat("Phase 1:\n") print(x$phase1$full) cat("Phase 2:\n") print(x$phase2) invisible(x) } summary.twophase2<-function(object,...){ class(object)<-"summary.twophase2" object } print.summary.twophase2<-function(x,...,varnames=TRUE){ cat("Two-phase sparse-matrix design:\n ") print(x$call) cat("Phase 1:\n") print(x$phase1$full,design.summaries=TRUE,varnames=FALSE) cat("Phase 2:\n") print(x$phase2,design.summaries=TRUE, varnames=FALSE) if (varnames){ cat("Data variables:\n") print(names(x$phase1$full$variables)) } invisible(x) } twophase2var<-function(x,design){ ## calibration is allowed at phase one or phase two, ## but not for clusters within a phase postStrata2<-design$phase2$postStrata postStrata1<-design$phase1$full$postStrata if (is.null(postStrata1) && is.null(postStrata2)){ rval<-htvar.matrix(x,design$dcheck$full) ph2<-htvar.matrix(x,design$dcheck$phase2) attr(rval,"phases")<-list(phase1=rval-ph2,phase2=ph2) return(rval) } if (!is.null(postStrata1)){ ##phase 1 calibration ## x is size of phase-2 sample,need to expand to allow calibration. y<-matrix(0,ncol=ncol(x),nrow=length(design$subset)) y[design$subset,]<-x for (psvar in postStrata1){ if (inherits(psvar, "greg_calibration")) { if (psvar$stage==0){ ## G-calibration at population level y<-qr.resid(psvar$qr,y/psvar$w)*psvar$w } else { ## G-calibration within clusters stop("calibration within clusters not allowed for two-phase designs") } } else { ## ordinary post-stratification psw<-attr(psvar, "weights") postStrata<-as.factor(psvar) psmeans<-rowsum(y/psw,psvar,reorder=TRUE)/as.vector(table(factor(psvar))) y<- y-psmeans[match(psvar,sort(unique(psvar))),]*psw x1<-y[design$subset,,drop=FALSE] } } } else x1<-x phase1var<-htvar.matrix(x1,design$dcheck$full)-htvar.matrix(x1,design$dcheck$phase2) if (!is.null(postStrata2)){ ##phase 2 calibration for (psvar in postStrata2){ if (inherits(psvar, "greg_calibration")) { if (psvar$stage==0){ ## G-calibration at population level x2<-qr.resid(psvar$qr,x/psvar$w)*psvar$w } else { ## G-calibration within clusters stop("calibration within clusters not allowed for two-phase designs") } } else { ## ordinary post-stratification psw<-attr(psvar, "weights") postStrata<-as.factor(psvar) psmeans<-rowsum(x/psw,psvar,reorder=TRUE)/as.vector(table(factor(psvar))) x2<- x-psmeans[match(psvar,sort(unique(psvar))),]*psw } } } else x2<-x phase2var<-htvar.matrix(x2,design$dcheck$phase2) rval<-phase1var+phase2var attr(rval,"phases")<-list(phase1=phase1var,phase2=phase2var) rval } svytotal.twophase2<-function(x,design, na.rm=FALSE, deff=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$phase1$sample$variables, na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if(length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } N<-sum(1/design$prob) total <- colSums(x/as.vector(design$prob),na.rm=na.rm) class(total)<-"svystat" attr(total, "var")<-v<-twophase2var(x/design$prob,design) attr(total,"statistic")<-"total" if (is.character(deff) || deff){ nobs<-NROW(design$cluster) if (deff=="replace") vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design))^2*(N-nobs)/N else vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design))^2 attr(total, "deff")<-v/vsrs } return(total) } svymean.twophase2<-function(x,design, na.rm=FALSE,deff=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$phase1$sample$variables ,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) if (any(nas>0)) design<-design[nas==0,] x[nas>0,]<-0 } pweights<-1/design$prob psum<-sum(pweights) average<-colSums(x*pweights/psum) x<-sweep(x,2,average) v<-twophase2var(x*pweights/psum,design) attr(average,"var")<-v attr(average,"statistic")<-"mean" class(average)<-"svystat" if (is.character(deff) || deff){ nobs<-nrow(design) if(deff=="replace"){ vsrs<-svyvar(x,design,na.rm=na.rm)/(nobs) } else { if(psum1) stop("number of plausible values must be the same for all variables") nreps<-nreps[1] results<-vector("list",nreps) if(rewrite){ sublist<-vector("list",nvars) names(sublist)<-PVnames for(i in 1:nreps){ for(j in 1:nvars) sublist[[j]]<-as.name(names(PVframes[[j]])[i]) if (is.function(action)){ actioni<-action body(actioni) <- eval(bquote(substitute(.(body(actioni)), sublist))) results[[i]]<- action(data) } else { actioni <- eval(bquote(substitute(.(action), sublist))) results[[i]] <- eval(actioni) } } } else { .DESIGN<-data for(i in 1:nreps){ dfi<-lapply(PVframes, function(d) d[[i]]) names(dfi)<-PVnames .DESIGN$variables<-cbind(df, as.data.frame(dfi)) if (is.function(action)) results[[i]] <- action(.DESIGN) else results[[i]] <- eval(action) } } attr(results,"call")<-sys.call() results } survey/R/svykappa.R0000644000176200001440000000265513641214070013777 0ustar liggesusers svykappa<-function(formula, design,...) UseMethod("svykappa",design) svykappa.default<-function(formula, design,...) { if (ncol(attr(terms(formula), "factors")) != 2) stop("kappa is only computed for two variables") rows <- formula[[2]][[2]] cols <- formula[[2]][[3]] df <- model.frame(design) nrow <- length(unique(df[[as.character(rows)]])) ncol <- length(unique(df[[as.character(cols)]])) rnames<-paste(".",letters,"_",sep="") cnames<-paste(".",LETTERS,"_",sep="") if (nrow != ncol) stop("number of categories is different") probs <- eval(bquote(svymean(~.(rows) + .(cols) + interaction(.(rows), .(cols)), design, ...))) nms <- c(rnames[1:nrow], cnames[1:ncol], outer(1:nrow, 1:ncol, function(i, j) paste(rnames[i], cnames[j], sep = "."))) names(probs) <- nms v <- vcov(probs) dimnames(v) <- list(nms, nms) attr(probs, "var") <- v obs <- parse(text = paste(nms[nrow + ncol + 1+ (0:(nrow-1))*(ncol+1)], collapse = "+"))[[1]] expect <- parse(text = paste(nms[1:nrow], nms[nrow + 1:ncol], sep = "*", collapse = "+"))[[1]] svycontrast(probs, list(kappa = bquote((.(obs) - .(expect))/(1 - .(expect))))) } "names<-.svrepstat"<-function(x, value){ if (is.list(x) && !is.null(x$replicates)){ names(x[[1]])<-value colnames(x$replicates)<-value x } else NextMethod() } survey/R/paley.R0000644000176200001440000001307513517345031013255 0ustar liggesusers## Paley construction of Hadamard matrices ## Only implemented for GF(p), because it's ## not entirely straightforward to find ## representations of GF(p^m) paley<-function(n, nmax=2*n, prime=NULL, check=!is.null(prime)){ if(!is.null(prime) && missing(n)) n<-prime ## these are primes with p+1 a multiple of 4 small.primes<-c(3, 7, 11, 19, 23, 31, 43, 47, 59, 67, 71, 79, 83, 103, 107, 127, 131, 139, 151, 163, 167, 179, 191, 199, 211, 223, 227, 239, 251, 263, 271, 283, 307, 311, 331, 347, 359, 367, 379, 383, 419, 431, 439, 443, 463, 467, 479, 487, 491, 499, 503, 523, 547, 563, 571, 587, 599, 607, 619, 631, 643, 647, 659, 683, 691, 719, 727, 739, 743, 751, 787, 811, 823, 827, 839, 859, 863, 883, 887, 907, 911, 919, 947, 967, 971, 983, 991, 1019, 1031, 1039, 1051, 1063, 1087, 1091, 1103, 1123, 1151, 1163, 1171, 1187, 1223, 1231, 1259, 1279, 1283, 1291, 1303, 1307, 1319, 1327, 1367, 1399, 1423, 1427, 1439, 1447, 1451, 1459, 1471, 1483, 1487, 1499, 1511, 1523, 1531, 1543, 1559, 1567, 1571, 1579, 1583, 1607, 1619, 1627, 1663, 1667, 1699, 1723, 1747, 1759, 1783, 1787, 1811, 1823, 1831, 1847, 1867, 1871, 1879, 1907, 1931, 1951, 1979, 1987, 1999, 2003, 2011, 2027, 2039, 2063, 2083, 2087, 2099, 2111, 2131, 2143, 2179, 2203, 2207, 2239, 2243, 2251, 2267, 2287, 2311, 2339, 2347, 2351, 2371, 2383, 2399, 2411, 2423, 2447, 2459, 2467, 2503, 2531, 2539, 2543, 2551, 2579, 2591, 2647, 2659, 2663, 2671, 2683, 2687, 2699, 2707, 2711, 2719, 2731, 2767, 2791, 2803, 2819, 2843, 2851, 2879, 2887, 2903, 2927, 2939, 2963, 2971, 2999, 3011, 3019, 3023, 3067, 3079, 3083, 3119, 3163, 3167, 3187, 3191, 3203, 3251, 3259, 3271, 3299, 3307, 3319, 3323, 3331, 3343, 3347, 3359, 3371, 3391, 3407, 3463, 3467, 3491, 3499, 3511, 3527, 3539, 3547, 3559, 3571, 3583, 3607, 3623, 3631, 3643, 3659, 3671, 3691, 3719, 3727, 3739, 3767, 3779, 3803, 3823, 3847, 3851, 3863, 3907, 3911, 3919, 3923, 3931, 3943, 3947, 3967, 4003, 4007, 4019, 4027, 4051, 4079, 4091, 4099, 4111, 4127, 4139, 4159, 4211, 4219, 4231, 4243, 4259, 4271, 4283, 4327, 4339, 4363, 4391, 4423, 4447, 4451, 4463, 4483, 4507, 4519, 4523, 4547, 4567, 4583, 4591, 4603, 4639, 4643, 4651, 4663, 4679, 4691, 4703, 4723, 4751, 4759, 4783, 4787, 4799, 4831, 4871, 4903, 4919, 4931, 4943, 4951, 4967, 4987, 4999, 5003, 5011, 5023, 5039, 5051, 5059, 5087, 5099, 5107, 5119, 5147, 5167, 5171, 5179, 5227, 5231, 5279, 5303, 5323, 5347, 5351, 5387, 5399, 5407, 5419, 5431, 5443, 5471, 5479, 5483, 5503, 5507, 5519, 5527, 5531, 5563, 5591, 5623, 5639, 5647, 5651, 5659, 5683, 5711, 5743, 5779, 5783, 5791, 5807, 5827, 5839, 5843, 5851, 5867, 5879, 5903, 5923, 5927, 5939, 5987, 6007, 6011, 6043, 6047, 6067, 6079, 6091, 6131, 6143, 6151, 6163, 6199, 6203, 6211, 6247, 6263, 6271, 6287, 6299, 6311, 6323, 6343, 6359, 6367, 6379, 6427, 6451, 6491, 6547, 6551, 6563, 6571, 6599, 6607, 6619, 6659, 6679, 6691, 6703, 6719, 6763, 6779, 6791, 6803, 6823, 6827, 6863, 6871, 6883, 6899, 6907, 6911, 6947, 6959, 6967, 6971, 6983, 6991, 7019, 7027, 7039, 7043, 7079, 7103, 7127, 7151, 7159, 7187, 7207, 7211, 7219, 7243, 7247, 7283, 7307, 7331, 7351, 7411, 7451, 7459, 7487, 7499, 7507, 7523, 7547, 7559, 7583, 7591, 7603, 7607, 7639, 7643, 7687, 7691, 7699, 7703, 7723, 7727, 7759, 7823, 7867, 7879, 7883, 7907, 7919) if (is.null(prime)){ nceil<-nn <- n + 4 - (n %% 4) if ( (n %% 4) +4 == (n %% 8)) { while (!(nn %% 8)){ nn <- nn /2} if ((nn-1) %in% small.primes){ m<-paley(prime=nn-1,check=check) while(nnmax(small.primes)) return(NULL) p<-min(small.primes[small.primes>=n]) if ((p+1 > nceil+4) && (nceil+4 < nmax)) return(paley(nceil+3)) if (p>nmax) return(NULL) } else{ p<-prime if ((p+1) %% 4 !=0) { warning("'prime'+1 is not divisible by 4") return(NULL) } if (p0]<-1 rval<-cbind(1,rbind(1,m)) if(check) { if(!is.hadamard(rval)) warning("matrix is not Hadamard: is 'prime' really prime?") } rval } is.hadamard<-function(H, style=c("0/1","+-"), full.orthogonal.balance=TRUE){ if (is.matrix(H) && is.numeric(H) && (ncol(H)==nrow(H))){ H<-switch(match.arg(style), "0/1"= 2*H-1, "+-"=H) isTRUE(all.equal(crossprod(H), diag(ncol(H))*ncol(H))) && all.equal(max(abs(H)),1) && (!full.orthogonal.balance || sum(H[-1,])==0) } else FALSE } survey/R/svypredmeans.R0000644000176200001440000000207213743475476014700 0ustar liggesusers svypredmeans<-function(adjustmodel, groupfactor, predictat=NULL){ design<-eval(bquote(update(adjustmodel$survey.design, .groupfactor=.(groupfactor[[2]])))) if(is.null(predictat)){ groups<-unique(model.frame(design)$.groupfactor) groups<-groups[!is.na(groups)] } else { groups <- predictat } model<-update(adjustmodel, .~.+.groupfactor,design=design) w<-weights(design,"sampling") fits<-matrix(nrow=NROW(design),ncol=length(groups)) dg_deta<-matrix(nrow=length(coef(model)),ncol=length(groups)) for(i in 1:length(groups)){ mf<-model.frame(design) mf$.groupfactor<-groups[i] mu<-predict(model,newdata=mf,type="response",se.fit=FALSE) eta<-predict(model,newdata=mf,type="link",se.fit=FALSE) fits[,i]<-coef(mu) mm<-model.matrix(terms(model),mf) dg_deta[,i]<-t(colSums(w*model$family$mu.eta(eta)*mm))/sum(w) } colnames(fits)<-as.character(groups) cond<-svymean(fits,design) addvar<-t(dg_deta)%*%vcov(model)%*%dg_deta vv<-addvar+attr(cond,"var") attr(vv,"parts")<-list(addvar,attr(cond,"var")) attr(cond,"var")<-vv cond } survey/R/svysmooth.R0000644000176200001440000000744413517345031014221 0ustar liggesuserssvysmooth<-function(formula,design,...) UseMethod("svysmooth", design) svysmooth.default<-function(formula, design,method=c("locpoly","quantreg"),bandwidth=NULL,quantile,df=4,...){ switch(match.arg(method), locpoly=svylocpoly(formula,design,bandwidth=bandwidth,...), quantreg=svyrqss(formula,design,quantile=quantile,df=df,...) ) } fitted.rq<-function(object,...) object$x%*% object$coefficients/object$weights svyrqss<-function(formula,design,quantile=0.5,df=4,...){ mf<-model.frame(formula, model.frame(design), na.action=na.omit) naa<-attr(mf,"na.action") tt<-attr(terms(formula),"term.labels") df<-rep(df, length=length(tt)) quantile<-rep(quantile, length=length(tt)) if (length(formula)==3){ density<-FALSE } else { density<-TRUE stop("type='quantreg' does not do densities") } w<-weights(design,type="sampling") if (length(naa)) w<-w[-naa] environment(formula)<-environment() ll<-vector("list", length(tt)) for(i in 1:length(tt)){ termi<-as.name(tt[i]) ff<-eval(bquote(update(formula,.~splines::bs(.(termi),df=.(df[i]))))) rqfit<-quantreg::rq(ff, tau=quantile[i],weights=w,data=mf,...) xx<-mf[,i+1] oo<-order(xx) ll[[i]]<-list(x=xx[oo],y=fitted.rq(rqfit)[oo]) } names(ll)<-attr(terms(formula),"term.labels") attr(ll,"call")<-sys.call(-2) attr(ll,"density")<-density if(density) attr(ll,"ylab")<-"Density" else attr(ll,"ylab")<-paste(deparse(formula[[2]]),collapse="") class(ll)<-"svysmooth" ll } svylocpoly<-function(formula, design, ngrid=401, xlim=NULL, ylim=NULL, bandwidth=NULL,...){ mf<-model.frame(formula,model.frame(design)) mm<-model.matrix(terms(formula),mf) if(attr(terms(formula),"intercept")) mm<-mm[,-1,drop=FALSE] naa<-attr(mf,"na.action") if (length(formula)==3){ Y<-model.response(mf) density<-FALSE } else density<-TRUE if (is.null(xlim)){ xlim<-apply(mm,2,range) } if (!is.matrix(xlim)) xlim<-matrix(xlim,nrow=2) if (is.null(bandwidth)){ bandwidth<-numeric(ncol(mm)) for(i in 1:ncol(mm)){ bandwidth[i]<-if(density) KernSmooth::dpik(mm[,i],gridsize=ngrid) else KernSmooth::dpill(mm[,i],Y,gridsize=ngrid) } } else { bandwidth<-rep(bandwidth, length=ncol(mm)) } w<-weights(design,type="sampling") if (length(naa)) w<-w[-naa] ll<-vector("list", ncol(mm)) for(i in 1:NCOL(mm)){ gx<-seq(min(xlim[,i]), max(xlim[,i]), length=ngrid) nx<-rowsum(c(rep(0,ngrid),w), c(1:ngrid, findInterval(mm[,i],gx))) if (density){ ll[[i]]<-KernSmooth::locpoly(rep(1,ngrid),nx*ngrid/(diff(xlim[,i])*sum(w)), binned=TRUE, bandwidth=bandwidth[i], range.x=xlim[,i]) }else{ ny<-rowsum(c(rep(0,ngrid), Y*w), c(1:ngrid, findInterval(mm[,i],gx))) ll[[i]]<-KernSmooth::locpoly(nx, ny, binned=TRUE, bandwidth=bandwidth[i], range.x=xlim[,i]) } names(ll)<-attr(terms(formula),"term.labels") } attr(ll,"call")<-sys.call(-2) attr(ll,"density")<-density if(density) attr(ll,"ylab")<-"Density" else attr(ll,"ylab")<-paste(deparse(formula[[2]]),collapse="") class(ll)<-"svysmooth" ll } print.svysmooth<-function(x,...){ if(attr(x,"density")) cat("Density estimate: :") else cat("Scatterplot smoother :") print(attr(x,"call")) invisible(x) } plot.svysmooth<-function(x, which=NULL,type="l",xlabs=NULL,ylab=NULL,...){ if (is.null(which)) which<-seq(length=length(x)) if (is.character(which)) which<-match(which,names(x)) if(is.null(xlabs)) xlabs<-names(x)[which] if(is.null(ylab)) ylab<-attr(x,"ylab") for(i in seq(length=length(which))) plot(x[[which[i]]], type=type, xlab=xlabs[i], ylab=ylab, ...) invisible(NULL) } lines.svysmooth<-function(x,which=NULL,...){ for(i in names(x)) lines(x[[i]],...) } survey/R/svyhist.R0000644000176200001440000000223313517345031013646 0ustar liggesuserssvyhist<-function(formula, design, breaks = "Sturges", include.lowest = TRUE, right = TRUE, xlab=NULL, main=NULL, probability=TRUE, freq=!probability,...){ if (inherits(design,"DBIsvydesign") || inherits(design,"ODBCsvydesign")){ design$variables<-getvars(formula, design$db$connection, design$db$tablename, updates = design$updates) class(design)<-"survey.design2" } mf<-model.frame(formula,model.frame(design), na.action=na.pass) if (ncol(mf)>1) stop("Only one variable allowed.") variable<-mf[,1] varname<-names(mf) h <- hist(variable, plot=FALSE, breaks=breaks,right=right) props <- coef(svymean(~cut(variable, h$breaks,right=right, include.lowest=include.lowest), design, na.rm=TRUE)) h$density<-props/diff(h$breaks) h$counts <- props*sum(weights(design,"sampling")) if (is.null(xlab)) xlab<-varname if (is.null(main)) main<-paste("Histogram of",varname) plot(h, ..., freq=freq,xlab=xlab,main=main) if (freq){ h$count_scale <- mean(diff(h$breaks))*sum(weights(design, "sampling")) } invisible(h) } survey/R/surveyrep.R0000755000176200001440000017467414556563036014242 0ustar liggesusers hadamard.doubler<-function(H){ rbind(cbind(H,H),cbind(H,1-H)) } hadamard<- function(n){ m<-n-(n %% 4) ## hadamard.list, hadamard.sizes in sysdata.rda precooked<- which(m < hadamard.sizes & m+4 >=hadamard.sizes) if (length(precooked)) return(hadamard.list[[min(precooked)]]) if (all(m4) H<-paley(n,sizes[bestfit]) if (is.null(H)){ ndoubles<-ceiling(log(sizes/hadamard.sizes, 2))[bestfit] H<-hadamard.list[[bestfit]] for(i in seq(length=ndoubles)) H<-hadamard.doubler(H) } H } jk1weights<-function(psu, fpc=NULL, fpctype=c("population","fraction","correction"), compress=TRUE){ fpctype<-match.arg(fpctype) unq<-unique(psu) n<-length(unq) if (is.null(fpc)) fpc<-1 else { fpc<-unique(fpc) if (length(fpc)>1) stop("More than one fpc value given") if (fpc<0) stop("Negative finite population correction") if (fpctype=="population" && fpcnstrat) stop("More distinct fpc values than strata") fpc<-sapply(sunq, function(ss) fpc[match(ss,strata)]) names(fpc)<-as.character(sunq) } else if (length(fpc)==1) { fpc<-rep(fpc,nstrat) names(fpc)<-as.character(sunq) } else if (length(fpc)==nstrat){ nn<-names(fpc) if (is.null(nn)) names(fpc)<-as.character(sunq) if (!all(names(fpc) %in% as.character(sunq))) stop("fpc has names that do not match the stratum identifiers") } if (compress){ repweights<-matrix(1,ncol=length(unq),nrow=length(unq)) } else { repweights<-matrix(1,ncol=length(unq), nrow=length(psu)) } counter<-0 rscales<-numeric(length(unq)) for(ss in as.character(sunq)){ thisfpc<-fpc[match(ss,names(fpc))] theseweights<-jk1weights(psu[strata %in% ss], fpc=thisfpc, fpctype=fpctype,compress=compress) nc<-if (compress) NCOL(theseweights$repweights$weights) else NCOL(theseweights$repweights) if (nc==1 && thisfpc!=0){ ## lonely PSUs if (lonely.psu=="fail") stop("Stratum",ss,"has only one PSU") if (lonely.psu=="remove") next if (lonely.psu=="certainty") next if (lonely.psu=="average") next if (lonely.psu=="adjust"){ nc<-1 if (compress) repweights[, counter+nc]<-ifelse(strata[!duplicated(psu)] %in% ss, 0, nstrat/(nstrat-1)) else repweights[ counter+nc]<-ifelse(strata %in% ss, 0, nstrat/(nstrat-1)) rscales[counter+nc]<-(nstrat-1)/nstrat counter<-counter+nc next } } if (compress) repweights[strata[!duplicated(psu)] %in% ss,counter+seq(length=nc)]<-theseweights$repweights$weights else repweights[strata %in% ss, counter+seq(length=nc)]<-theseweights$repweights rscales[counter+seq(length=nc)]<-theseweights$scale counter<-counter+nc } if (counter==0) stop("All strata were exhaustively sampled: you have the whole population") scale<-1 if (counter2) && large=="fail") stop("Some strata have more than 2 PSUs") unq<-which(!duplicated(psu)) sunq<-strata[unq] psunq<-psu[unq] weights<-matrix(ncol=2,nrow=length(unq)) weightstrata<-numeric(length(unq)) if (length(match)==length(strata)) match<-match[unq] if (is.null(match)) match<-unq ## default is to match by dataset order oo<-order(sunq,match) upto <- 0 if(any(ssize==1)){ smallstrata<-names(ssize)[ssize==1] if(small=="split"){ weights[sunq %in% smallstrata,1]<- 0.5 weights[sunq %in% smallstrata,2]<- 0.5 weightstrata[sunq %in% smallstrata]<-1:length(smallstrata) upto<-length(smallstrata) } else { ##small=="merge" if (length(smallstrata) > 1){ weights[oo,][sunq[oo] %in% smallstrata, 1]<-rep(0:1,length.out=length(smallstrata)) weights[oo,][sunq[oo] %in% smallstrata, 2]<-rep(1:0,length.out=length(smallstrata)) if(length(smallstrata) %% 2==0) weightstrata[oo][sunq[oo] %in% smallstrata]<-rep(1:(length(smallstrata) %/%2), 2) else weightstrata[oo][sunq[oo] %in% smallstrata]<-c(1,rep(1:(length(smallstrata) %/%2), 2)) upto<-length(smallstrata) %/% 2 } else stop("Can't merge with a single small stratum") } } if (any(ssize>2)){ largestrata<-names(ssize)[ssize>2] if (large=="split"){ if (any(ssize[largestrata] %%2 ==1)) stop("Can't split with odd numbers of PSUs in a stratum") ## make substrata of size 2 for(ss in largestrata){ weights[oo,][sunq[oo] %in% ss, 1]<-rep(0:1,length.out=ssize[ss]) weights[oo,][sunq[oo] %in% ss, 2]<-rep(1:0,length.out=ssize[ss]) weightstrata[oo][sunq[oo] %in% ss]<-upto+rep(1:(ssize[ss] %/%2),each=2) upto<-upto+(ssize[ss] %/% 2) } } else { ## make two substrata. halfsize<-ssize[largestrata] %/%2 otherhalfsize<-ssize[largestrata] - halfsize reps<-as.vector(rbind(halfsize,otherhalfsize)) nlarge<-length(halfsize) weights[oo,][sunq[oo] %in% largestrata, 1]<-rep(rep(0:1,nlarge),reps) weights[oo,][sunq[oo] %in% largestrata, 2]<-rep(rep(1:0,nlarge),reps) weightstrata[oo][sunq[oo] %in% largestrata]<-upto+rep(1:length(largestrata),ssize[largestrata]) upto<-upto+length(largestrata) } } if(any(ssize==2)){ goodstrata<-names(ssize)[ssize==2] weights[oo,][sunq[oo] %in% goodstrata, 1]<-rep(0:1,length(goodstrata)) weights[oo,][sunq[oo] %in% goodstrata, 2]<-rep(1:0,length(goodstrata)) weightstrata[oo][sunq[oo] %in% goodstrata]<-upto+rep(1:length(goodstrata),each=2) upto<-upto+length(goodstrata) } if (is.null(hadamard.matrix)){ H<-hadamard(upto) } else { ## the user supplied hadamard.matrix ## Check that it is a binary matrix and satifies the ## Hadamard determinant property if (!is.matrix(hadamard.matrix) || nrow(hadamard.matrix)1 && type!="mrbbootstrap"){ fpc<-fpc[,1] warning("Finite population corrections after first stage have been dropped") } if (getOption("survey.drop.replicates")){ selfrep<-design$fpc$popsize[,1]==design$fpc$sampsize[,1] } } else{ fpc<-design$fpc[,2] names(fpc)<-design$fpc[,1] } } } else { if (type %in% c("Fay","BRR","subbootstrap")) stop(paste("fpc information cannot be used for type=",type)) if (is.null(fpctype)) stop("fpctype must be supplied if fpc is supplied") } if (type=="JK1"){ ##JK1 r<-jk1weights(design$cluster[,1], fpc=fpc,fpctype=fpctype, compress=compress) repweights<-r$repweights scale<-drop(r$scale) if (inherits(repweights,"repweights_compressed")) rscales<-rep(1, NCOL(repweights$weights)) else rscales<-rep(1, NCOL(repweights)) type<-"JK1" pweights<-1/design$prob } else if (type %in% c("BRR","Fay")){ ##BRR if (inherits(design,"survey.design2")) repweights<-brrweights(design$strata[,1], design$cluster[,1],...,fay.rho=fay.rho, compress=compress,only.weights=TRUE) else repweights<-brrweights(design$strata, design$cluster[,1],...,fay.rho=fay.rho, compress=compress,only.weights=TRUE) pweights<-1/design$prob if (length(pweights)==1) pweights<-rep(pweights, NROW(design$variables)) if (fay.rho==0) type<-"BRR" else type<-"Fay" rscales<-rep(1,ncol(repweights)) scale<-1/(ncol(repweights)*(1-fay.rho)^2) } else if (type=="JKn"){ ##JKn if (inherits(design,"survey.design2")) r<-jknweights(design$strata[,1],design$cluster[,1], fpc=fpc, fpctype=fpctype, compress=compress) else r<-jknweights(design$strata,design$cluster[,1], fpc=fpc, fpctype=fpctype, compress=compress) pweights<-1/design$prob repweights<-r$repweights scale<-r$scale rscales<-r$rscales } else if (type=="bootstrap"){ ##bootstrap if (inherits(design,"survey.design2")) r<-bootweights(design$strata[,1],design$cluster[,1], fpc=fpc, fpctype=fpctype, compress=compress,...) else r<-bootweights(design$strata,design$cluster[,1], fpc=fpc, fpctype=fpctype, compress=compress,...) pweights<-1/design$prob repweights<-r$repweights scale<-r$scale rscales<-r$rscales }else if (type=="subbootstrap"){ ##bootstrap if (inherits(design,"survey.design2")) r<-subbootweights(design$strata[,1],design$cluster[,1],compress=compress,...) else r<-subbootweights(design$strata,design$cluster[,1],compress=compress,...) pweights<-1/design$prob repweights<-r$repweights scale<-r$scale rscales<-r$rscales } else if (type=="mrbbootstrap"){ if (inherits(design,"survey.design2")) r<-mrbweights(design$cluster,design$strata,design$fpc,...) else stop("MRB bootstrap not available for obsolete svydesign objects") pweights<-1/design$prob repweights<-r$repweights scale<-r$scale rscales<-r$rscales } else stop("Can't happen") rval<-list(repweights=repweights, pweights=pweights, type=type, rho=fay.rho,scale=scale, rscales=rscales, call=sys.call(), combined.weights=FALSE, selfrep=selfrep,mse=mse) rval$variables <- design$variables class(rval)<-"svyrep.design" rval$degf<-degf(rval) rval } svrepdesign<-function(variables, repweights, weights,data=NULL,degf=NULL,...) UseMethod("svrepdesign",data) svrepdesign.default<-function(variables=NULL,repweights=NULL, weights=NULL, data=NULL, degf=NULL,type=c("BRR","Fay","JK1", "JKn","bootstrap", "ACS","successive-difference","JK2","other"), combined.weights=TRUE, rho=NULL, bootstrap.average=NULL, scale=NULL,rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"), mse=getOption("survey.replicates.mse"),...) { type<-match.arg(type) if(type=="Fay" && is.null(rho)) stop("With type='Fay' you must supply the correct rho") if (type %in% c("JK1","JKn","ACS","successive-difference","JK2") && !is.null(rho)) warning("rho not relevant to JK1 design: ignored.") if (type %in% c("other") && !is.null(rho)) warning("rho ignored.") if(is.null(variables)) variables<-data if(inherits(variables,"formula")){ mf<-substitute(model.frame(variables, data=data,na.action=na.pass)) variables<-eval.parent(mf) } variables<-detibble(variables) if(inherits(repweights,"formula")){ mf<-substitute(model.frame(repweights, data=data)) repweights<-eval.parent(mf) } if(is.character(repweights)){##regular expression wtcols<-grep(repweights,names(data)) repweights<-data[,wtcols] } if (is.null(repweights)) stop("You must provide replication weights") if (anyNA(repweights)) stop("Missing values not allowed in 'repweights'") repweights<-detibble(repweights) if(inherits(weights,"formula")){ mf<-substitute(model.frame(weights, data=data)) weights<-eval.parent(mf) if (anyNA(weights)) stop("Missing values not allowed in 'weights'") weights<-drop(as.matrix(weights)) } if (is.null(weights)){ warning("No sampling weights provided: equal probability assumed") weights<-rep(1,NROW(repweights)) } if (!is.null(degf)){ if (!is.numeric(degf)) stop("degf must be NULL or numeric") if (degf>ncol(repweights)) warning(paste0("degf (", degf,") is larger than number of replicates (",ncol(repweights),")")) if (degf<=1) warning("degf is <=1") } repwtmn<-mean(apply(repweights,2,mean)) wtmn<-mean(weights) probably.combined.weights<-(repwtmn>5) & (wtmn/repwtmn<5) probably.not.combined.weights<-(repwtmn<5) & (wtmn/repwtmn>5) if (combined.weights & probably.not.combined.weights) warning(paste("Data do not look like combined weights: mean replication weight is", repwtmn," and mean sampling weight is",wtmn)) if (!combined.weights & probably.combined.weights) warning(paste("Data look like combined weights: mean replication weight is", repwtmn," and mean sampling weight is",wtmn)) if (!is.null(rscales) && !(length(rscales) %in% c(1, ncol(repweights)))){ stop(paste("rscales has length ",length(rscales),", should be ncol(repweights)",sep="")) } if (type %in% c("ACS", "successive-difference")){ if(!is.null(scale) | !is.null(rscales)) warning(paste("with type",type,"scale= and rscales= are not needed and will be ignored")) } if (type == "BRR"){ ## the default, so check it hasn't been accidentally defaulted to if (!is.null(scale)) warning("type='BRR' does not use 'scale=' argument") if (!is.null(rho)) warning("type='BRR' does not use 'rho=' argument, you may want type='Fay'") scale<-1/ncol(repweights) } if (type=="Fay") scale <-1/(ncol(repweights)*(1-rho)^2) if (type=="bootstrap"){ if(is.null(bootstrap.average)) bootstrap.average<-1 if (is.null(scale)) scale<-bootstrap.average/(ncol(repweights)-1) if (is.null(rscales)) rscales<-rep(1,ncol(repweights)) } if (type=="JK1" && is.null(scale)) { if(!combined.weights){ warning("scale (n-1)/n not provided: guessing from weights") scale<-1/max(repweights[,1]) } else { probably.n = ncol(repweights) scale<- (probably.n-1)/probably.n warning("scale (n-1)/n not provided: guessing n=number of replicates") } } if (type =="JKn" && is.null(rscales)) if (!combined.weights) { warning("rscales (n-1)/n not provided:guessing from weights") rscales<-1/apply(repweights,2,max) } else stop("Must provide rscales for combined JKn weights") if (type %in% c("ACS","successive-difference")){ rscales<-rep(1, ncol(repweights)) scale<-4/ncol(repweights) } if (type =="JK2"){ warning(paste("with type",type,"scale= and rscales= are not needed and will be ignored")) rscales<-rep(1, ncol(repweights)) scale<-1 } if (type=="other" && (is.null(rscales) || is.null(scale))){ if (is.null(rscales)) rscales<-rep(1,NCOL(repweights)) if (is.null(scale)) scale<-1 warning("scale or rscales not specified, set to 1") } if (is.null(rscales)) rscales<-rep(1,NCOL(repweights)) if (!is.null(fpc)){ if (missing(fpctype)) stop("Must specify fpctype") fpctype<-match.arg(fpctype) if (type %in% c("BRR","Fay","JK2","ACS","successive-difference")) stop("fpc not available for this type") if (type %in% "bootstrap") stop("Separate fpc not needed for bootstrap") if (length(fpc)!=length(rscales)) stop("fpc is wrong length") if (any(fpc>1) || any(fpc<0)) stop("Illegal fpc value") fpc<-switch(fpctype,correction=fpc,fraction=1-fpc) rscales<-rscales*fpc } if (is.null(scale)) scale<-1 rval<-list(type=type, scale=scale, rscales=rscales, rho=rho,call=sys.call(), combined.weights=combined.weights) rval$variables<-variables rval$pweights<-weights if (!inherits(repweights,"repweights")) class(rval)<-"repweights" rval$repweights<-repweights class(rval)<-"svyrep.design" if (!is.null(degf)) rval$degf<-degf else rval$degf<-degf(rval) rval$mse<-mse rval } print.svyrep.design<-function(x,...){ cat("Call: ") print(x$call) if (x$type=="Fay") cat("Fay's variance method (rho=",x$rho,") ") if (x$type=="BRR") cat("Balanced Repeated Replicates ") if (x$type=="JK1") cat("Unstratified cluster jacknife (JK1) ") if (x$type=="JKn") cat("Stratified cluster jackknife (JKn) ") if (x$type=="bootstrap") cat("Survey bootstrap ") if (x$type=="mrbbootstrap") cat("Multistage rescaled bootstrap ") if (x$type=="subbootstrap") cat("(n-1) bootstrap ") nweights<-ncol(x$repweights) cat("with", nweights,"replicates") if (!is.null(x$mse) && x$mse) cat(" and MSE variances") cat(".\n") invisible(x) } summary.svyrep.design<-function(object,...){ class(object)<-c("summary.svyrep.design", class(object)) object } print.summary.svyrep.design<-function(x,...){ class(x)<-class(x)[-1] print(x) cat("Variables: \n") print(colnames(x)) } image.svyrep.design<-function(x, ..., col=grey(seq(.5,1,length=30)), type.=c("rep","total")){ type<-match.arg(type.) m<-as.matrix(x$repweights) if (type=="total"){ m<-m*x$pweights } image(1:NCOL(m), 1:NROW(m), t(m), col=col, xlab="Replicate", ylab="Observation",...) invisible(NULL) } "[.svyrep.design"<-function(x, i, j, drop=FALSE){ if (!missing(i)){ pwt<-x$pweights if (is.data.frame(pwt)) pwt<-pwt[[1]] x$pweights<-pwt[i] x$repweights<-x$repweights[i,,drop=FALSE] if(!is.null(x$selfrep)) x$selfrep<-x$selfrep[i] if (!missing(j)) x$variables<-x$variables[i,j, drop=FALSE] else x$variables<-x$variables[i,,drop=FALSE] x$degf<-NULL x$degf<-degf(x) } else { x$variables<-x$variables[,j,drop=FALSE] } x } subset.svyrep.design<-function(x,subset,...){ e <- substitute(subset) r <- eval(e, x$variables, parent.frame()) r <- r & !is.na(r) x<-x[r,] x$call<-sys.call(-1) x } update.svyrep.design<-function(object,...){ dots<-substitute(list(...))[-1] newnames<-names(dots) for(j in seq(along=dots)){ object$variables[,newnames[j]]<-eval(dots[[j]],object$variables, parent.frame()) } object$call<-sys.call(-1) object } weights.svyrep.design<-function(object,type=c("replication","sampling","analysis"),...){ type<-match.arg(type) switch(type, replication= as.matrix(object$repweights), sampling=object$pweights, analysis=if(object$combined.weights) as.matrix(object$repweights) else as.matrix(object$repweights)*object$pweights) } weights.survey.design<-function(object,...){ return(1/object$prob) } svrVar<-function(thetas, scale, rscales,na.action=getOption("na.action"),mse=getOption("survey.replicates.mse"),coef){ thetas<-get(na.action)(thetas) naa<-attr(thetas,"na.action") if (!is.null(naa)){ rscales<-rscales[-naa] if (length(rscales)) warning(length(naa), " replicates gave NA results and were discarded.") else stop("All replicates contained NAs") } if (is.null(mse)) mse<-FALSE if (length(dim(thetas))==2){ if (mse) { meantheta<-coef } else { meantheta<-colMeans(thetas[rscales>0,,drop=FALSE]) } v<-crossprod( sweep(thetas,2, meantheta,"-")*sqrt(rscales))*scale } else { if (mse){ meantheta<-coef } else { meantheta<-mean(thetas[rscales>0]) } v<- sum( (thetas-meantheta)^2*rscales)*scale } attr(v,"na.replicates")<-naa attr(v,"means")<-meantheta return(v) } svyvar.svyrep.design<-svrepvar<-function(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE,...,estimate.only=FALSE){ if (!exists(".Generic",inherits=FALSE)) .Deprecated("svyvar") if (inherits(x,"formula")) x<-model.frame(x,design$variables,na.action=na.pass) else if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) wts<-design$repweights scale<-design$scale rscales<-design$rscales if (!is.null(rho)) .NotYetUsed("rho") x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) if(any(nas>0)){ design<-design[nas==0,] x<-x[nas==0,,drop=FALSE] wts<-wts[nas==0,,drop=FALSE] } } if (design$combined.weights) pw<-1 else pw<-design$pweights n<-NROW(x) p<-NCOL(x) v<-function(w){ xbar<-colSums(as.vector(w)*pw*x)/sum(as.vector(w)*pw) xdev<-sweep(x,2,xbar,"-") x1<-matrix(rep(xdev,p),ncol=p*p) x2<-xdev[,rep(1:p,each=p),drop=FALSE] (n/(n-1))*colSums(x1*x2*as.vector(w)*pw)/sum(as.vector(w)*pw) } if (design$combined.weights) rval<-v(design$pweights) else rval<-v(rep(1,length(design$pweights))) rval<-matrix(rval, ncol=p) dimnames(rval)<-list(colnames(x),colnames(x)) if (estimate.only) return(rval) repvars<-apply(wts,2, v) repvars<-drop(t(repvars)) attr(rval,"var")<-svrVar(repvars, scale, rscales,mse=design$mse, coef=rval) attr(rval, "statistic")<-"variance" if (return.replicates){ attr(repvars,"scale")<-design$scale attr(repvars,"rscales")<-design$rscales attr(repvars,"mse")<-design$mse rval<-list(variance=rval, replicates=repvars) } class(rval)<-c("svrepvar","svrepstat") rval } print.svrepvar<-function (x, covariance=FALSE, ...) { if (is.list(x)) x<-x[[1]] vv <- as.matrix(attr(x, "var")) if (covariance){ nms<-outer(rownames(x),colnames(x),paste,sep=":") m<-cbind(as.vector(x), sqrt(diag(vv))) rownames(m)<-nms } else{ ii <- which(diag(sqrt(length(x)))>0) m <- cbind(x[ii], sqrt(diag(vv))[ii]) if(length(ii)==1) rownames(m)<-rownames(x) } colnames(m) <- c(attr(x, "statistic"), "SE") printCoefmat(m) } as.matrix.svrepvar<-function(x,...) if (is.list(x)) unclass(x[[1]]) else unclass(x) svymean.svyrep.design<-svrepmean<-function(x,design, na.rm=FALSE, rho=NULL, return.replicates=FALSE,deff=FALSE,...) { if (!exists(".Generic",inherits=FALSE)) .Deprecated("svymean") if (!inherits(design,"svyrep.design")) stop("design is not a replicate survey design") if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$variables,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] x<-x[nas==0,,drop=FALSE] } wts<-design$repweights scale<-design$scale rscales<-design$rscales if (!is.null(rho)) .NotYetUsed("rho") if (!design$combined.weights) pw<-design$pweights else pw<-1 rval<-colSums(design$pweights*x)/sum(design$pweights) if (getOption("survey.drop.replicates") && !is.null(design$selfrep) && all(design$selfrep)){ v<-matrix(0,length(rval),length(rval)) repmeans<-NULL } else { if (inherits(wts, "repweights_compressed")){ repmeans<-matrix(ncol=NCOL(x), nrow=ncol(wts$weights)) for(i in 1:ncol(wts$weights)){ wi<-wts$weights[wts$index,i] repmeans[i,]<-t(colSums(wi*x*pw)/sum(pw*wi)) } } else { repmeans<-matrix(ncol=NCOL(x), nrow=ncol(wts)) for(i in 1:ncol(wts)){ repmeans[i,]<-t(colSums(wts[,i]*x*pw)/sum(pw*wts[,i])) } } repmeans<-drop(repmeans) v <- svrVar(repmeans, scale, rscales,mse=design$mse, coef=rval) } attr(rval,"var") <-v attr(rval, "statistic")<-"mean" if (return.replicates){ attr(repmeans,"scale")<-design$scale attr(repmeans,"rscales")<-design$rscales attr(repmeans,"mse")<-design$mse rval<-list(mean=rval, replicates=repmeans) } if (is.character(deff) || deff){ nobs<-length(design$pweights) npop<-sum(design$pweights) vsrs<-unclass(svyvar(x,design,na.rm=na.rm, return.replicates=FALSE,estimate.only=TRUE))/length(design$pweights) if (deff!="replace") vsrs<-vsrs*(npop-nobs)/npop attr(rval,"deff") <- v/vsrs } class(rval)<-"svrepstat" rval } svytotal.svyrep.design<-svreptotal<-function(x,design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE,...) { if (!exists(".Generic",inherits=FALSE)) .Deprecated("svytotal") if (!inherits(design,"svyrep.design")) stop("design is not a replicate survey design") if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,design$variables,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else{ if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] x<-x[nas==0,,drop=FALSE] } wts<-design$repweights scale<-design$scale rscales<-design$rscales if (!is.null(rho)) .NotYetUsed("rho") if (!design$combined.weights) pw<-design$pweights else pw<-1 rval<-colSums(design$pweights*x) if (is.character(deff) || deff){ nobs<-length(design$pweights) npop<-sum(design$pweights) vsrs<-unclass(svyvar(x,design,na.rm=na.rm, return.replicates=FALSE,estimate.only=TRUE))*sum(design$pweights)^2/nobs if (deff!="replace") vsrs<-vsrs*(npop-nobs)/npop } if (getOption("survey.drop.replicates") && !is.null(design$selfrep) && all(design$selfrep)){ v<-matrix(0,nrow=NROW(rval),ncol=NROW(rval)) repmeans<-NULL } else { if (inherits(wts, "repweights_compressed")){ if (getOption("survey.drop.replicates") && !is.null(design$selfrep)){ wts$index<-wts$index[!design$selfrep] x<-x[!design$selfrep,,drop=FALSE] pw<-pw[!design$selfrep] } repmeans<-matrix(ncol=NCOL(x), nrow=ncol(wts$weights)) for(i in 1:ncol(wts$weights)){ wi<-wts$weights[wts$index,i] repmeans[i,]<-t(colSums(wi*x*pw)) } } else { if (getOption("survey.drop.replicates") && !is.null(design$selfrep)){ wts<-wts[!design$selfrep,,drop=FALSE] x<-x[!design$selfrep,,drop=FALSE] pw<-pw[!design$selfrep] } repmeans<-matrix(ncol=NCOL(x), nrow=ncol(wts)) for(i in 1:ncol(wts)){ repmeans[i,]<-t(colSums(wts[,i]*x*pw)) } } repmeans<-drop(repmeans) v <- svrVar(repmeans, scale, rscales,mse=design$mse,coef=rval) } attr(rval,"var") <- v attr(rval, "statistic")<-"total" if (return.replicates){ attr(repmeans,"scale")<-design$scale attr(repmeans,"rscales")<-design$rscales attr(repmeans,"mse")<-design$mse rval<-list(mean=rval, replicates=repmeans) } if (is.character(deff) || deff) attr(rval,"deff") <- v/vsrs class(rval)<-"svrepstat" rval } svycoxph.svyrep.design<-function(formula, design, subset=NULL,rescale=NULL,...,return.replicates=FALSE,na.action, multicore=getOption("survey.multicore")){ subset<-substitute(subset) subset<-eval(subset, design$variables, parent.frame()) if (!is.null(subset)) design<-design[subset,] if (multicore && !requireNamespace(parallel,quietly=TRUE)) multicore<-FALSE data<-design$variables g<-match.call() g$design<-NULL g$return.replicates<-NULL g$weights<-quote(.survey.prob.weights) g[[1]]<-quote(coxph) g$x<-TRUE scale<-design$scale rscales<-design$rscales if (is.null(rescale)) pwts<-design$pweights/mean(design$pweights) else if (rescale) pwts<-design$pweights/sum(design$pweights) if (is.data.frame(pwts)) pwts<-pwts[[1]] if (!all(all.vars(formula) %in% names(data))) stop("all variables must be in design= argument") .survey.prob.weights<-pwts full<-with(data,eval(g)) if (inherits(full, "coxph.penal")) warning("svycoxph does not support penalised terms") nas<-attr(full$model, "na.action") betas<-matrix(ncol=length(coef(full)),nrow=ncol(design$repweights)) wts<-design$repweights if (!design$combined.weights){ pw1<-pwts rwt<-pw1/mean(pw1) } else{ rwt<-1/mean(as.vector(wts[,1])) pw1<-rwt } if (length(nas)) wts<-wts[-nas,] beta0<-coef(full) ## coxph doesn't allow zero weights EPSILON<-1e-10 if(full$method %in% c("efron","breslow")){ if (attr(full$y,"type")=="right") fitter<-coxph.fit else if(attr(full$y,"type")=="counting") fitter<-survival::agreg.fit else stop("invalid survival type") } else fitter<-survival::agexact.fit ## g$init<-beta0 ## for(i in 1:ncol(wts)){ ## .survey.prob.weights<-as.vector(wts[,i])*pw1+EPSILON ## betas[i,]<-with(data,coef(eval(g))) ## } if (multicore){ betas<-do.call(rbind, parallel::mclapply(1:ncol(wts), function(i){ fitter(full$x, full$y, full$strata, full$offset, coef(full), coxph.control(), as.vector(wts[,i])*pw1+EPSILON, full$method, names(full$resid))$coef })) }else{ for(i in 1:ncol(wts)){ betas[i,]<-fitter(full$x, full$y, full$strata, full$offset, coef(full), coxph.control(), as.vector(wts[,i])*pw1+EPSILON, full$method, names(full$resid))$coef } } if (length(nas)) design<-design[-nas,] v<-svrVar(betas,scale, rscales, mse=design$mse, coef=beta0) full$var<-v if (return.replicates){ attr(betas,"scale")<-design$scale attr(betas,"rscales")<-design$rscales attr(betas,"mse")<-design$mse full$replicates<-betas } full$naive.var<-NULL full$wald.test<-coef(full)%*%solve(full$var,coef(full)) full$loglik<-c(NA,NA) full$rscore<-NULL full$score<-NA full$degf.residual<-degf(design)+1-length(coef(full)[!is.na(coef(full))]) class(full)<-c("svrepcoxph","svycoxph",class(full)) full$call<-match.call() full$printcall<-sys.call(-1) full$survey.design<-design full } svrepglm<-svyglm.svyrep.design<-function(formula, design, subset=NULL,family=stats::gaussian(),start=NULL, rescale=NULL, ..., rho=NULL, return.replicates=FALSE, na.action, multicore=getOption("survey.multicore")){ if (!exists(".Generic",inherits=FALSE)) .Deprecated("svyglm") subset<-substitute(subset) subset<-eval(subset, design$variables, parent.frame()) if (any(is.na(subset))) stop("subset must not contain NA values") if (!is.null(subset)) design<-design[subset,] if(multicore && !requireNamespace("parallel",quietly=TRUE)) multicore<-FALSE data<-design$variables g<-match.call() formula<-eval.parent(formula) environment(formula)<-environment() g$formula<-formula g$data<-quote(data) g$design<-NULL g$var<-g$rho<-g$return.replicates<-g$multicore<-NULL g$weights<-quote(.survey.prob.weights) g[[1]]<-quote(glm) g$model<-TRUE g$x<-TRUE g$y<-TRUE g$rescale<-NULL scale<-design$scale rscales<-design$rscales if (!is.null(rho)) .NotYetUsed(rho) if (is.null(rescale)) pwts<-design$pweights/mean(design$pweights) else if (rescale) ## old behaviour pwts<-design$pweights/sum(design$pweights) else pwts<-design$pweights ## no rescaling if (is.data.frame(pwts)) pwts<-pwts[[1]] if (!all(all.vars(formula) %in% names(data))) stop("all variables must be in design= argument") .survey.prob.weights<-pwts full<-with(data,eval(g)) full$naive.cov<-summary(full)$cov.unscaled nas<-attr(full$model, "na.action") if(getOption("survey.drop.replicates") && !is.null(design$selfrep) && all(design$selfrep)){ v<-matrix(0,ncol=length(coef(full)),nrow=length(coef(full))) betas<-NULL } else { betas<-matrix(ncol=length(coef(full)), nrow=ncol(design$repweights)) if (!design$combined.weights) pw1<-pwts else pw1<-rep(1,length(pwts)) wts<-design$repweights if (length(nas)){ wts<-wts[-nas,] pw1<-pw1[-nas] } XX<-full$x YY<-full$y beta0<-coef(full) if (!all(is.finite(beta0))) stop(paste("Infinite/NA values in estimate (",paste(beta0,collapse=","),")")) if(is.null(full$offset)) offs<-rep(0,nrow(XX)) else offs<-full$offset incpt<-as.logical(attr(terms(full),"intercept")) fam<-full$family contrl<-full$control if (multicore){ betas<-do.call(rbind,parallel::mclapply(1:ncol(wts), function(i){ wi<-as.vector(wts[,i])*pw1 glm.fit(XX, YY, weights = wi/sum(wi), start =beta0, offset = offs, family = fam, control = contrl, intercept = incpt)$coefficients })) } else { for(i in 1:ncol(wts)){ wi<-as.vector(wts[,i])*pw1 betas[i,]<-glm.fit(XX, YY, weights = wi/sum(wi), start =beta0, offset = offs, family = fam, control = contrl, intercept = incpt)$coefficients } } v<-svrVar(betas,scale, rscales,mse=design$mse,coef=beta0) } full$x<-NULL full$df.residual<-degf(design)+1-length(coef(full)[!is.na(coef(full))]) if (length(nas)) design<-design[-nas,] full$cov.unscaled<-v if (return.replicates){ attr(betas,"scale")<-design$scale attr(betas,"rscales")<-design$rscales attr(betas,"mse")<-design$mse full$replicates<-betas } class(full)<-c("svrepglm", "svyglm", class(full)) full$call<-sys.call(-1) if(!("formula" %in% names(full$call))) { if (is.null(names(full$call))) i<-1 else i<-min(which(names(full$call)[-1]=="")) names(full$call)[i+1]<-"formula" } full$survey.design<-design full } print.summary.svyglm<-function (x, digits = max(3, getOption("digits") - 3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...) { ##if (!exists("printCoefmat")) printCoefmat<-print.coefmat cat("\nCall:\n") cat(paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "") cat("Survey design:\n") print(x$survey.design$call) if (!is.null(df <- x$df) && (nsingular <- df[3] - df[1])) cat("\nCoefficients: (", nsingular, " not defined because of singularities)\n", sep = "") else cat("\nCoefficients:\n") coefs <- x$coefficients if (!is.null(aliased <- is.na(x$coefficients[,1])) && any(aliased)) { cn <- names(aliased) coefs <- matrix(NA, length(aliased), 4, dimnames = list(cn, colnames(coefs))) coefs[!aliased, ] <- x$coefficients } printCoefmat(coefs, digits = digits, signif.stars = signif.stars, na.print = "NA", ...) if (x$df.resid<=0) cat("\nZero or negative residual df; p-values not defined\n") cat("\n(Dispersion parameter for ", x$family$family, " family taken to be ", format(x$dispersion), ")\n\n", "Number of Fisher Scoring iterations: ", x$iter, "\n", sep = "") correl <- x$correlation if (!is.null(correl)) { p <- NCOL(correl) if (p > 1) { cat("\nCorrelation of Coefficients:\n") if (is.logical(symbolic.cor) && symbolic.cor) { print(symnum(correl, abbr.colnames = NULL)) } else { correl <- format(round(correl, 2), nsmall = 2, digits = digits) correl[!lower.tri(correl)] <- "" print(correl[-1, -p, drop = FALSE], quote = FALSE) } } } cat("\n") invisible(x) } predict.svrepglm <- function(object, newdata=NULL, total=NULL, type = c("link", "response","terms"), se.fit=(type!="terms"), vcov=FALSE, return.replicates=!is.null(object$replicates),...){ if(is.null(newdata)) newdata<-model.frame(object$survey.design) type<-match.arg(type) if (type=="terms") return(predterms(object,se=se.fit,...)) tt<-delete.response(terms(formula(object))) mf<-model.frame(tt,data=newdata) mm<-model.matrix(tt,mf) if (!is.null(total) && attr(tt,"intercept")){ mm[,attr(tt,"intercept")]<-mm[,attr(tt,"intercept")]*total } eta<-drop(mm %*% coef(object)) d<-drop(object$family$mu.eta(eta)) eta<-switch(type, link=eta, response=object$family$linkinv(eta)) if(se.fit){ if(vcov){ vv<-mm %*% vcov(object) %*% t(mm) attr(eta,"var")<-switch(type, link=vv, response=d*(t(vv*d))) } else { ## FIXME make this more efficient vv<-drop(rowSums((mm %*% vcov(object)) * mm)) attr(eta,"var")<-switch(type, link=vv, response=drop(d*(t(vv*d)))) } } attr(eta,"statistic")<-type if (return.replicates){ if (is.null(object$replicates)) { warning("replicates are not present in the fit") } else{ pred.replicates<-t(apply(object$replicates,1, function(beta){ etai<-drop(mm %*% beta) switch(type, link=etai, response=object$family$linkinv(etai)) })) attr(pred.replicates,"scale")<-attr(object$replicates,"scale") attr(pred.replicates,"rscales")<-attr(object$replicates,"rscales") attr(pred.replicates,"mse")<-attr(object$replicates,"mse") eta<-list(eta,replicates=pred.replicates) } } class(eta)<-"svrepstat" eta } svyratio.svyrep.design<-svrepratio<-function(numerator=formula,denominator, design, na.rm=FALSE,formula,covmat=FALSE, return.replicates=FALSE,deff=FALSE,...){ if (!exists(".Generic")) .Deprecated("svyratio") if (!inherits(design, "svyrep.design")) stop("design must be a svyrepdesign object") if (inherits(numerator,"formula")) numerator<-model.frame(numerator,design$variables, na.action=na.pass) else if(typeof(numerator) %in% c("expression","symbol")) numerator<-eval(numerator, design$variables) if (inherits(denominator,"formula")) denominator<-model.frame(denominator,design$variables, na.action=na.pass) else if(typeof(denominator) %in% c("expression","symbol")) denominator<-eval(denominator, design$variables) nn<-NCOL(numerator) nd<-NCOL(denominator) all<-cbind(numerator,denominator) nas<-!complete.cases(all) if (na.rm==TRUE){ design<-design[!nas,] all<-all[!nas,,drop=FALSE] numerator<-numerator[!nas,,drop=FALSE] denominator<-denominator[!nas,,drop=FALSE] } allstats<-svymean(all, design, return.replicates=TRUE) rval<-list(ratio=outer(allstats$mean[1:nn], allstats$mean[nn+1:nd], "/")) if (is.null(allstats$replicates)){ ##only self-representing strata. vars<-matrix(0,nrow=nn,ncol=nd) }else { vars<-matrix(nrow=nn,ncol=nd) if (deff) deffs<-matrix(nrow=nn,ncol=nd) for(i in 1:nn){ for(j in 1:nd){ vars[i,j]<-svrVar(allstats$replicates[,i]/allstats$replicates[,nn+j], design$scale, design$rscales,mse=design$mse,coef=rval$ratio[i,j]) if (deff=="replace" || deff) deffs[i,j]<-deff(svytotal(numerator[,i]-rval$ratio[i,j]*denominator[,j],design,deff=deff)) } } } if (covmat){ if (is.null(allstats$replicates)) vcovmat<-matrix(0,nn*nd,nn*nd) else vcovmat<-as.matrix(svrVar(allstats$replicates[,rep(1:nn,nd)]/allstats$replicates[,nn+rep(1:nd,each=nn)], design$scale, design$rscales,mse=design$mse,coef=as.vector(rval$ratio))) rownames(vcovmat)<-names(numerator)[rep(1:nn,nd)] colnames(vcovmat)<-names(denominator)[rep(1:nd,each=nn)] rval$vcov<-vcovmat } if (return.replicates) { reps<-allstats$replicates[, rep(1:nn, nd)]/allstats$replicates[, nn + rep(1:nd, each = nn)] attr(reps,"scale")<-design$scale attr(reps,"rscales")<-design$rscales attr(reps,"mse")<-design$mse rval$replicates<-reps } rval$var<-vars attr(rval,"call")<-sys.call() if (deff=="replace" || deff) attr(rval,"deff")<-deffs class(rval)<-"svyratio" rval } vcov.svyratio <- function(object, ...){ covmat<-object$vcov if (is.null(covmat)){ covmat<-matrix(NaN,length(object$var),length(object$var)) diag(covmat)<-as.vector(object$var) } nms<-as.vector(outer(rownames(object$ratio),colnames(object$ratio),paste,sep="/")) dimnames(covmat)<-list(nms,nms) covmat } residuals.svrepglm<-function(object,type = c("deviance", "pearson", "working", "response", "partial"),...){ type<-match.arg(type) if (type=="pearson"){ y <- object$y mu <- object$fitted.values wts <- object$prior.weights r<-(y - mu) * sqrt(wts)/(sqrt(object$family$variance(mu))*sqrt(object$survey.design$pweights/mean(object$survey.design$pweights))) if (is.null(object$na.action)) r else naresid(object$na.action, r) } else NextMethod() } logLik.svrepglm<-function(object,...){ stop("svrepglm not fitted by maximum likelihood.") } vcov.svyrep.design<-function (object, replicates, centre, ...) { if (object$mse) svrVar(replicates, scale = object$scale, rscales = object$rscales, mse = object$mse, coef=centre) else svrVar(replicates, scale = object$scale, rscales = object$rscales, mse = object$mse) } withReplicates<-function(design, theta, ..., return.replicates=FALSE){ UseMethod("withReplicates",design) } withReplicates.svrepvar<-function(design, theta, ...,return.replicates=FALSE){ if (is.null(reps<-design$replicates)) stop("object does not contain replicate estimates") p<-sqrt(NCOL(reps)) if (is.function(theta)){ full<-theta(design[[1]],...) thetas<-drop(t(apply(reps,1, function(rr) theta(matrix(rr,p,p), ...)))) } else{ full<-eval(theta, list(.replicate=design[[1]])) thetas<-drop(t(apply(reps,1, function(rr) eval(theta, list(.replicate=matrix(rr,p,p)))))) } v<-svrVar(thetas, attr(reps,"scale"), attr(reps,"rscales"), mse=attr(reps,"mse"), coef=full) attr(full,"var")<-v attr(full,"statistic")<-"theta" if (return.replicates){ attr(thetas,"scale")<-attr(reps,"scale") attr(thetas,"rscales")<-attr(reps,"rscales") attr(thetas,"mse")<-attr(reps,"mse") rval<-list(theta=full, replicates=thetas) } else { rval<-full } class(rval)<-"svrepstat" rval } withReplicates.svrepstat<-function(design, theta, ..., return.replicates=FALSE){ if (is.null(reps<-design$replicates)) stop("object does not contain replicate estimates") reps<-as.matrix(reps) if (is.function(theta)){ full<-theta(design[[1]],...) thetas<-drop(t(apply(reps,1,theta, ...))) } else{ full<-eval(theta, list(.replicate=design[[1]])) thetas<-drop(t(apply(reps,1, function(rr) eval(theta, list(.replicate=rr))))) } v<-svrVar(thetas, attr(reps,"scale"), attr(reps,"rscales"), mse=attr(reps,"mse"), coef=full) attr(full,"var")<-v attr(full,"statistic")<-"theta" if (return.replicates){ attr(thetas,"scale")<-attr(reps,"scale") attr(thetas,"rscales")<-attr(reps,"rscales") attr(thetas,"mse")<-attr(reps,"mse") rval<-list(theta=full, replicates=thetas) } else { rval<-full } class(rval)<-"svrepstat" rval } withReplicates.svyrep.design<-function(design, theta, rho=NULL,..., scale.weights=FALSE, return.replicates=FALSE){ wts<-design$repweights scale<-design$scale rscales<-design$rscales if (!is.null(rho)) .NotYetUsed("rho") if (scale.weights) pwts<-design$pweights/sum(design$pweights) else pwts<-design$pweights if (inherits(wts,"repweights_compressed")){ if (scale.weights) wts$weights<-sweep(wts$weights,2,drop(colSums(wts$weights)),"/") } else { if (scale.weights) wts<-sweep(wts,2, drop(colSums(wts)),"/") } rpwts<-if (design$combined.weights) 1 else pwts data<-design$variables if (is.function(theta)){ full<-theta(pwts,data,...) thetas<-drop(t(apply(wts,2, function(ww) theta(as.vector(ww)*rpwts, data, ...)))) } else{ .weights<-pwts full<-with(data, eval(theta)) thetas<-drop(t(apply(wts,2, function(.weights) {.weights<-as.vector(.weights)*rpwts with(data, eval(theta))}))) } v<-svrVar(thetas, scale, rscales,mse=design$mse, coef=full) attr(full,"var")<-v attr(full,"statistic")<-"theta" if (return.replicates) rval<-list(theta=full, replicates=thetas) else rval<-full class(rval)<-"svrepstat" rval } coef.svrepstat<-function(object,...){ if (is.list(object)) object<-object[[1]] attr(object,"statistic")<-NULL attr(object,"deff")<-NULL attr(object,"var")<-NULL unclass(object) } vcov.svrepstat<-function (object, ...) { nms <- names(coef(object)) if (is.list(object)) object <- object[[1]] v <- as.matrix(attr(object, "var")) if (length(object) == NCOL(v)) { dimnames(v) <- list(nms, nms) v } else if (length(object) == length(v)) { dnms <- dimnames(coef(object)) vmat <- matrix(NA, nrow = length(object), ncol = length(object)) diag(vmat) <- as.vector(v) nms <- as.vector(outer(dnms[[1]], dnms[[2]], paste, sep = ":")) dimnames(vmat) <- list(nms, nms) vmat } } as.data.frame.svrepstat<-function(x,...){ if (is.list(x)) { x<-x[[1]] class(x)<-"svrepstat" } rval<-data.frame(statistic=coef(x),SE=SE(x)) names(rval)[1]<-attr(x,"statistic") if (!is.null(attr(x,"deff"))) rval<-cbind(rval,deff=deff(x)) rval } SE<-function(object,...){ UseMethod("SE") } SE.default<-function(object,...){ sqrt(diag(vcov(object,...))) } SE.svrepstat<-function(object,...){ if (is.list(object)){ object<-object[[1]] } vv<-as.matrix(attr(object,"var")) if (!is.null(dim(object)) && length(object)==length(vv)) sqrt(vv) else sqrt(diag(vv)) } print.svrepstat<-function(x,...){ if (is.list(x)){ x<-x[[1]] } vv<-attr(x,"var") deff<-attr(x, "deff") if (!is.null(dim(x)) && length(x)==length(vv)){ cat("Statistic:\n") prmatrix(x) cat("SE:\n") print(sqrt(vv)) if (!is.null(deff)){ cat("Design Effect:\n") printCoefmat() } } else if(length(x)==NCOL(vv)){ m<-cbind(x,sqrt(diag(as.matrix(vv)))) if (is.null(deff)) colnames(m)<-c(attr(x,"statistic"),"SE") else { m<-cbind(m,deff(x)) colnames(m)<-c(attr(x,"statistic"),"SE","DEff") } printCoefmat(m) } else if (length(x)==length(vv)){ m<-cbind(x,sqrt((vv))) if (is.null(deff)) colnames(m)<-c(attr(x,"statistic"),"SE") else { m<-cbind(m,deff(x)) colnames(m)<-c(attr(x,"statistic"),"SE","DEff") } printCoefmat(m) } else { stop("incorrect structure of svrepstat object") } naa<-attr(vv,"na.replicates") if (!is.null(naa)) cat("Note: NA results discarded for",length(naa),"replicates (",naa,")\n") } summary.svrepglm<-function (object, correlation = FALSE, df.resid=NULL,...) { Qr <- object$qr est.disp <- TRUE if (is.null(df.resid)) df.r <- object$df.residual else df.r<-df.resid presid<-resid(object,"pearson") dispersion<- sum( object$survey.design$pweights*presid^2,na.rm=TRUE)/sum(object$survey.design$pweights) coef.p <- coef(object) covmat<-vcov(object) dimnames(covmat) <- list(names(coef.p), names(coef.p)) var.cf <- diag(covmat) s.err <- sqrt(var.cf) tvalue <- coef.p/s.err dn <- c("Estimate", "Std. Error") if (!est.disp) { pvalue <- 2 * pnorm(-abs(tvalue)) coef.table <- cbind(coef.p, s.err, tvalue, pvalue) dimnames(coef.table) <- list(names(coef.p), c(dn, "z value", "Pr(>|z|)")) } else if (df.r > 0) { pvalue <- 2 * pt(-abs(tvalue), df.r) coef.table <- cbind(coef.p, s.err, tvalue, pvalue) dimnames(coef.table) <- list(names(coef.p), c(dn, "t value", "Pr(>|t|)")) } else { coef.table <- cbind(coef.p, Inf) dimnames(coef.table) <- list(names(coef.p), dn) } ans <- c(object[c("call", "terms", "family", "deviance", "aic", "contrasts", "df.residual", "null.deviance", "df.null", "iter")], list(deviance.resid = residuals(object, type = "deviance"), aic = object$aic, coefficients = coef.table, dispersion = dispersion, df = c(object$rank, df.r,NCOL(Qr$qr)), cov.unscaled = covmat, cov.scaled = covmat)) if (correlation) { dd <- sqrt(diag(covmat)) ans$correlation <- covmat/outer(dd, dd) } ans$aliased<-is.na(ans$coef) ans$survey.design<-list(call=object$survey.design$call, type=object$survey.design$type) class(ans) <- c("summary.svyglm","summary.glm") return(ans) } svytable.svyrep.design<-svreptable<-function(formula, design, Ntotal=sum(weights(design, "sampling")), round=FALSE,...){ if (!exists(".Generic",inherits=FALSE)) .Deprecated("svytable") weights<-design$pweights if (is.data.frame(weights)) weights<-weights[[1]] ## unstratified or unadjusted. if (is.null(Ntotal) || length(Ntotal)==1){ ff<-eval(substitute(lhs~rhs,list(lhs=quote(weights), rhs=formula[[2]]))) tbl<-xtabs(ff, data=design$variables,...) if (!is.null(Ntotal)) { tbl<-tbl*(sum(as.numeric(Ntotal))/sum(tbl)) ## avoid integer overflow when Ntotal >sqrt(maxint) } if (round) tbl<-round(tbl) class(tbl) <- c("svytable", class(tbl)) attr(tbl, "call")<-match.call() return(tbl) } ## adjusted and stratified ff<-eval(substitute(lhs~strata+rhs,list(lhs=quote(weights), rhs=formula[[2]], strata=quote(design$strata)))) tbl<-xtabs(ff, data=design$variables,...) ss<-match(sort(unique(design$strata)), Ntotal[,1]) dm<-dim(tbl) layer<-prod(dm[-1]) tbl<-sweep(tbl,1,Ntotal[ss, 2]/apply(tbl,1,sum),"*") tbl<-apply(tbl, 2:length(dm), sum) if (round) tbl<-round(tbl) class(tbl)<-c("svytable", "xtabs","table") attr(tbl, "call")<-match.call() tbl } postStratify<-function(design,strata, population, partial=FALSE,...){ UseMethod("postStratify") } postStratify.svyrep.design<-function(design, strata, population, partial=FALSE,compress=NULL,...){ if(inherits(strata,"formula")){ mf<-substitute(model.frame(strata, data=design$variables,na.action=na.fail)) strata<-eval.parent(mf) } strata<-as.data.frame(strata) if (is.null(compress)) compress<-inherits(design$repweights, "repweights_compressed") sampletable<-xtabs(design$pweights~.,data=strata) sampletable<-as.data.frame(sampletable) if (inherits(population,"table")) population<-as.data.frame(population) else if (is.data.frame(population)) population$Freq <- as.vector(population$Freq) else stop("population must be a table or dataframe") if (!all(names(strata) %in% names(population))) stop("Stratifying variables don't match") nn<- names(population) %in% names(strata) if (sum(!nn)!=1) stop("stratifying variables don't match") names(population)[which(!nn)]<-"Pop.Freq" both<-merge(sampletable, population, by=names(strata), all=TRUE) samplezero <- both$Freq %in% c(0, NA) popzero <- both$Pop.Freq %in% c(0, NA) both<-both[!(samplezero & popzero),] if (any(onlysample<- popzero & !samplezero)){ print(both[onlysample,]) stop("Strata in sample absent from population. This Can't Happen") } if (any(onlypop <- samplezero & !popzero)){ if (partial){ both<-both[!onlypop,] warning("Some strata absent from sample: ignored") } else { print(both[onlypop,]) stop("Some strata absent from sample: use partial=TRUE to ignore them.") } } reweight<-both$Pop.Freq/both$Freq both$label <- do.call("interaction", list(both[,names(strata)])) designlabel <- do.call("interaction", strata) index<-match(designlabel, both$label) oldpw<-design$pweights design$pweights<-design$pweights*reweight[index] if (design$combined.weights){ replicateFreq<- rowsum(as.matrix(design$repweights), match(designlabel, both$label), reorder=TRUE) repreweight<- both$Pop.Freq/replicateFreq design$repweights <- as.matrix(design$repweights)*repreweight[index,] } else { replicateFreq<- rowsum(as.matrix(design$repweights)*oldpw, match(designlabel, both$label), reorder=TRUE) repreweight<- both$Pop.Freq/replicateFreq design$repweights <- as.matrix(design$repweights)* (repreweight/reweight)[index,] } if (compress) design$repweights<-compressWeights(design$repweights) design$call<-sys.call(-1) if(!is.null(design$degf)){ design$degf<-NULL design$degf<-degf(design) } design } rake<-function(design, sample.margins, population.margins, control=list(maxit=10, epsilon=1, verbose=FALSE), compress=NULL){ if (!missing(control)){ control.defaults<-formals(rake)$control for(n in names(control.defaults)) if(!(n %in% names(control))) control[[n]]<-control.defaults[[n]] } is.rep<-inherits(design,"svyrep.design") if (is.rep && is.null(compress)) compress<-inherits(design$repweights,"repweights_compressed") if (is.rep) design$degf<-NULL if (length(sample.margins)!=length(population.margins)) stop("sample.margins and population.margins do not match.") nmar<-length(sample.margins) if (control$epsilon<1) epsilon<-control$epsilon*sum(weights(design,"sampling")) else epsilon<-control$epsilon strata<-lapply(sample.margins, function(margin) if(inherits(margin,"formula")){ mf<-model.frame(margin, data=design$variables,na.action=na.fail) } ) allterms<-unlist(lapply(sample.margins,all.vars)) ff<-formula(paste("~", paste(allterms,collapse="+"),sep="")) oldtable<-svytable(ff, design) if (control$verbose) print(oldtable) oldpoststrata<-design$postStrata iter<-0 converged<-FALSE while(iter < control$maxit){ ## we don't want to accumulate more poststrata with each iteration design$postStrata<-NULL for(i in 1:nmar){ design<-postStratify(design, strata[[i]], population.margins[[i]], compress=FALSE) } newtable<-svytable(ff, design) if (control$verbose) print(newtable) delta<-max(abs(oldtable-newtable)) if (delta1 && method!="brewer") stop("Multistage PPS sampling not supported with this method") rval<-svydesign(ids=ids,strata=strata,weights=NULL, probs=probs,fpc=fpc,data=data,pps="other") deltacheck<-make_pps_covmat(rval, method) rval$dcheck=deltacheck rval$variance<-variance rval$call<-call class(rval) <- c("pps","survey.design") rval } ppsmat<-function(jointprob, tolerance=0.0001){ if ((!is.matrix(jointprob)) || !(NROW(jointprob)==NCOL(jointprob))) stop("jointprob must be a square matrix") rval<-list(pij=jointprob, tolerance=tolerance,call=sys.call()) class(rval)<-"ppsmat" rval } ppscov<-function(probcov, weighted=FALSE){ if ((!is.matrix(probcov)) && (!is(probcov,"Matrix")) || !(NROW(probcov)==NCOL(probcov))) stop("jointprob must be a square matrix") if (weighted){ rval<-list(dcheck=probcov,call=sys.call()) class(rval)<-"ppsdcheck" } else { rval<-list(delta=probcov,call=sys.call()) class(rval)<-"ppsdelta" } rval } print.ppsdcheck<-function(x,...) { cat("PPS: Weighted covariance matrix: ") print(x$call) invisible(x) } print.ppsdelta<-function(x,...) { cat("PPS: Sampling covariance matrix: ") print(x$call) invisible(x) } print.ppsmat<-function(x,...) { cat("PPS: Joint probability matrix: ") print(x$call) invisible(x) } pps_design.ppsmat<-function(method,ids,strata=NULL, probs=NULL, fpc=NULL,variables=NULL, subset, data,call=sys.call(),variance="HT",...){ if (length(ids[[2]])>1) stop("Multistage PPS sampling not supported") rval<-svydesign(ids=ids,strata=strata,weights=NULL,variables=variables, probs=probs,fpc=fpc,data=data,pps="other") deltacheck<-pi2Dcheck(method$pij,method$tolerance) rval$variance<-variance rval$dcheck<-list(list(id=1:nrow(method$pij), dcheck=deltacheck)) rval$call<-call class(rval) <- c("pps","survey.design") rval } pps_design.ppsdcheck<-function(method,ids,strata=NULL, probs=NULL, fpc=NULL,variables=NULL, subset, data,call=sys.call(),variance="HT",...){ if (length(ids[[2]])>1) stop("Multistage PPS sampling not supported") rval<-svydesign(ids=ids,strata=strata,weights=NULL,variables=variables, probs=probs,fpc=fpc,data=data,pps="other") deltacheck<-method$dcheck rval$variance<-variance rval$dcheck<-list(list(id=1:nrow(deltacheck), dcheck=deltacheck)) rval$call<-call class(rval) <- c("pps","survey.design") rval } pps_design.ppsdelta<-function(method,ids,strata=NULL, probs=NULL, fpc=NULL,variables=NULL, subset, data,call=sys.call(),variance="HT",...){ if (length(ids[[2]])>1) stop("Multistage PPS sampling not supported") rval<-svydesign(ids=ids,strata=strata,weights=NULL,variables=variables, probs=probs,fpc=fpc,data=data,pps="other") w<-weights(rval) deltacheck<-method$delta*tcrossprod(w) diag(deltacheck)<-diag(method$delta)*w method$deltacheck<-deltacheck rval$variance<-variance rval$dcheck<-list(list(id=1:nrow(method$deltacheck), dcheck=deltacheck)) rval$call<-call class(rval) <- c("pps","survey.design") rval } poisson_sampling<-function(p){ ppscov(Diagonal(x=1-p),weighted=TRUE) } HR<-function(psum=NULL, strata=NULL){ if (is.null(psum)) { ## estimate rval<-list(pbar=NULL,call=sys.call()) } else if (is.data.frame(strata) || is.matrix(strata)){ #redundant pbar<-lapply(1:NCOL(strata), function(i){ psum[!duplicated(strata[,i]),i]}) strata<-lapply(1:NCOL(strata), function(i){ strata[!duplicated(strata[,i]),i]}) rval<-list(pbar=pbar, strata=strata,call=sys.call()) } else if (is.null(strata) && is.numeric(psum) && length(psum)==1){ ## single number rval<-list(pbar=list(psum),strata=list(1), call=sys.call()) } else{ ## non-redundant list rval<-list(pbar=psum, strata=strata,call=sys.call()) } class(rval)<-"HR" rval } print.HR<-function(x,...) { cat("PPS: Hartley-Rao correction: ") print(x$call) invisible(x) } pps_design.HR<-function(method,ids,strata=NULL, probs=NULL, fpc=NULL,variables=NULL, subset, data,call=sys.call(),variance="HT",...){ if (length(ids[[2]])>1) stop("Multistage PPS sampling not supported with this method") rval<-svydesign(ids=ids,strata=strata,weights=NULL, probs=probs,fpc=fpc,data=data,pps="other") if (is.null(method$pbar)) ## sample estimate of sum(p^2/n) deltacheck<-multi.HR1Dcheck(rval$cluster,rval$strata,rval$allprob) else deltacheck<-multi.HRDcheck(rval$cluster,rval$strata,rval$allprob, method$pbar) rval$dcheck=deltacheck rval$variance<-variance rval$call<-call class(rval) <- c("pps","survey.design") rval } print.pps<-function(x,...){ cat("Sparse-matrix design object:\n ") print(x$call) } summary.pps<-function(object,...){ class(object)<-"summary.pps" object } print.summary.pps<-function(x,...,varnames=TRUE){ cat("Two-phase sparse-matrix design:\n ") print(x$call) cat("Sampling probabilities:\n") print(summary(x$prob)) if (varnames){ cat("Data variables:\n") print(names(x$variables)) } invisible(x) } ppsvar<-function(x,design){ postStrata<-design$postStrata est<-design$variance ##Yates-Grundy or Horvitz-Thompson if (!is.null(postStrata)){ for (psvar in postStrata){ if (inherits(psvar, "greg_calibration")) { if (psvar$stage==0){ ## G-calibration at population level x<-qr.resid(psvar$qr,x/psvar$w)*psvar$w } else { ## G-calibration within clusters stop("calibration within clusters not yet available for PPS designs") } } else { ## ordinary post-stratification psw<-attr(psvar, "weights") postStrata<-as.factor(psvar) psmeans<-rowsum(x/psw,psvar,reorder=TRUE)/as.vector(table(factor(psvar))) x<- x-psmeans[match(psvar,sort(unique(psvar))),]*psw } } } dcheck<-design$dcheck if (length(dcheck)!=1) stop("Multistage not implemented yet") rval<-switch(est,HT=htvar.matrix(rowsum(x,dcheck[[1]]$id,reorder=FALSE),dcheck[[1]]$dcheck), YG=ygvar.matrix(rowsum(x,dcheck[[1]]$id,reorder=FALSE),dcheck[[1]]$dcheck), stop("can't happen")) rval } svytotal.pps<-function(x,design, na.rm=FALSE, deff=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,model.frame(design), na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if(length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } N<-sum(1/design$prob) total <- colSums(x/as.vector(design$prob),na.rm=na.rm) class(total)<-"svystat" attr(total, "var")<-v<-ppsvar(x/design$prob,design) attr(total,"statistic")<-"total" if (is.character(deff) || deff){ nobs<-NROW(design$cluster) if (deff=="replace") vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design))^2*(N-nobs)/N else vsrs<-svyvar(x,design,na.rm=na.rm)*sum(weights(design))^2 attr(total, "deff")<-v/vsrs } return(total) } svymean.pps<-function(x,design, na.rm=FALSE,deff=FALSE,...){ if (inherits(x,"formula")){ ## do the right thing with factors mf<-model.frame(x,model.frame(design) ,na.action=na.pass) xx<-lapply(attr(terms(x),"variables")[-1], function(tt) model.matrix(eval(bquote(~0+.(tt))),mf)) cols<-sapply(xx,NCOL) x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) scols<-c(0,cumsum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-do.call("c",lapply(xx,colnames)) } else { if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) else { if(is.data.frame(x) && any(sapply(x,is.factor))){ xx<-lapply(x, function(xi) {if (is.factor(xi)) 0+(outer(xi,levels(xi),"==")) else xi}) cols<-sapply(xx,NCOL) scols<-c(0,cumsum(cols)) cn<-character(sum(cols)) for(i in 1:length(xx)) cn[scols[i]+1:cols[i]]<-paste(names(x)[i],levels(x[[i]]),sep="") x<-matrix(nrow=NROW(xx[[1]]),ncol=sum(cols)) for(i in 1:length(xx)){ x[,scols[i]+1:cols[i]]<-xx[[i]] } colnames(x)<-cn } } } x<-as.matrix(x) if (na.rm){ nas<-rowSums(is.na(x)) if (any(nas>0)) design<-design[nas==0,] x[nas>0,]<-0 } pweights<-1/design$prob psum<-sum(pweights) average<-colSums(x*pweights/psum) x<-sweep(x,2,average) v<-ppsvar(x*pweights/psum,design) attr(average,"var")<-v attr(average,"statistic")<-"mean" class(average)<-"svystat" if (is.character(deff) || deff){ nobs<-nrow(design) if(deff=="replace"){ vsrs<-svyvar(x,design,na.rm=na.rm)/(nobs) } else { if(psumlength(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } w<-weights(design) epsilon<-0.1*min(w[w>0])/sum(w) if (is.null(df)){ qcrit<-function(p, lower.tail=TRUE) qt(p, df=degf(design), lower.tail=lower.tail) } else if(df==Inf){ qcrit <- function(p,lower.tail=TRUE) qnorm(p,lower.tail=lower.tail) } else { qcrit <- function(p,lower.tail=TRUE) qt(p,df=df,lower.tail=lower.tail) } computeQuantiles<-function(xx,p=quantiles){ if (any(is.na(x))) return(NA*p) oo<-order(xx) cum.w<-cumsum(w[oo])/sum(w) cdf<-approxfun(cum.w,xx[oo],method=method,f=f, yleft=min(xx),yright=max(xx),ties=min) cdf(p) } computeQuantilesRounded<-function(xx,p=quantiles){ if (any(is.na(xx))) return(NA*p) ww<-rowsum(w,xx,reorder=TRUE) xx<-sort(unique(xx)) cum.w <- cumsum(ww)/sum(ww) cdf <- approxfun(cum.w, xx, method = method, f = f, yleft = min(xx), yright = max(xx),ties=min) cdf(p) } computeScoreCI<-function(xx,p){ if (any(is.na(xx))) return(c(NA,NA)) U<-function(theta){ ((xx>theta)-(1-p))} scoretest<-function(theta,qlimit){ umean<-svymean(U(theta),design) umean/sqrt(attr(umean,"var"))-qlimit } iqr<-IQR(xx) lower<-min(xx)+iqr/100 upper<-max(xx)-iqr/100 tol<-1/(100*sqrt(nrow(design))) c(uniroot(scoretest,interval=c(lower,upper), qlimit=qcrit(alpha/2,lower.tail=FALSE),tol=tol)$root, uniroot(scoretest,interval=c(lower,upper), qlimit=qcrit(alpha/2,lower.tail=TRUE),tol=tol)$root) } computePCI<-function(se,alpha,p){ if (interval.type=="Wald"){ p.up<-p+qcrit(alpha/2,lower.tail=FALSE)*se p.low<-p+qcrit(alpha/2,lower.tail=TRUE)*se c(p.low,p.up) } else if (interval.type=="betaWald"){ n.eff <- (p*(1-p))/(se^2) n.eff <- n.eff * ( qt(alpha/2, nrow(design)-1)/qt(alpha/2, degf(design)) )^2 p.up<-qbeta(1-alpha/2, n.eff*p+1, n.eff*(1-p)) p.low<-qbeta(alpha/2, n.eff*p, n.eff*(1-p)+1) c(p.low,p.up) } } computeWaldCI<-function(xx,p){ if (any(is.na(xx))) return(c(NA,NA)) theta0<-computeQuantiles(xx,p) U<- ((xx>theta0)-(1-p)) wtest<-svymean(U,design) p.ci<-computePCI(SE(wtest),alpha,p) p.low<-p.ci[1] p.up<-p.ci[2] oo<-order(xx) cum.w<-cumsum(w[oo])/sum(w) approx(cum.w,xx[oo],xout=c(p.low,p.up), method=method,f=f, yleft=min(xx),yright=max(xx),ties=min)$y } computeWaldCIRounded<-function(xx,p){ if(any(is.na(xx))) return(c(NA,NA)) theta0<-computeQuantilesRounded(xx,p) U<- ((xx>theta0)-(1-p)) ww<-rowsum(w,xx, reorder=TRUE) uxx <- sort(unique(xx)) wtest<-svymean(U,design) p.ci<-computePCI(SE(wtest),alpha,p) p.low<-p.ci[1] p.up<-p.ci[2] oo<-order(xx) cum.w<-cumsum(ww)/sum(ww) approx(cum.w,uxx,xout=c(p.low,p.up), method=method,f=f, yleft=min(xx),yright=max(xx),ties=min)$y } ties<-match.arg(ties) computeQ<-switch(ties, discrete=computeQuantiles,rounded=computeQuantilesRounded) if (!is.null(dim(x))) rval<-t(matrix(apply(x,2,computeQ),nrow=length(quantiles), dimnames=list(as.character(round(quantiles,2)),colnames(x)))) else rval<-computeQ(x) if (!ci & !se) return(rval) interval.type<-match.arg(interval.type) computeCI<-switch(paste(interval.type,ties,sep="."), score.discrete=computeScoreCI, score.rounded=stop("ties=\"rounded\" not available with interval.type=\"score\""), Wald.rounded=computeWaldCIRounded, betaWald.rounded=computeWaldCIRounded, Wald.discrete=computeWaldCI, betaWald.discrete=computeWaldCI) if (!is.null(dim(x))) cis<-array(apply(x,2,function(xx) sapply(quantiles,function(qq) computeCI(xx,qq))), dim=c(2,length(quantiles),ncol(x)), dimnames=list(c("(lower","upper)"), as.character(round(quantiles,2)), colnames(x))) else cis<-sapply(quantiles, function(qq) computeCI(x,qq)) if (ci) rval<-list(quantiles=rval,CIs=cis) else rval<-list(quantiles=rval) if (is.null(dim(x))) ses<-(cis[2,]-cis[1,])/(2*qcrit(alpha/2,lower.tail=FALSE)) else ses<-(cis[2,,]-cis[1,,])/(2*qcrit(alpha/2,lower.tail=FALSE)) attr(rval,"SE")<-ses class(rval)<-c("oldsvyquantile","svyquantile") rval } oldsvyquantile.svyrep.design<-function(x,design,quantiles,method="linear", interval.type=c("probability","quantile"),f=1, return.replicates=FALSE, ties=c("discrete","rounded"),na.rm=FALSE, alpha=0.05,df=NULL,...){ if (!exists(".Generic",inherits=FALSE)) .Deprecated("svyquantile") ties<-match.arg(ties) interval<-match.arg(interval.type) if (design$type %in% c("JK1","JKn") && interval=="quantile") warning("Jackknife replicate weights may not give valid standard errors for quantiles") if (design$type %in% "other" && interval=="quantile") warning("Not all replicate weight designs give valid standard errors for quantiles.") if (inherits(x,"formula")) x<-model.frame(x,design$variables,na.action=if(na.rm) na.pass else na.fail) else if(typeof(x) %in% c("expression","symbol")) x<-eval(x, design$variables) if (na.rm){ nas<-rowSums(is.na(x)) design<-design[nas==0,] if (length(nas)>length(design$prob)) x<-x[nas==0,,drop=FALSE] else x[nas>0,]<-0 } if (NROW(x)<=1){ rval<-matrix(rep(as.matrix(x),length(quantiles)),ncol=NCOL(x),nrow=length(quantiles),byrow=TRUE) dimnames(rval)<-list(paste("q",round(quantiles,2),sep=""), names(x)) if (getOption("survey.drop.replicates") && !is.null(design$selfrep) && all(design$selfrep)) vv<-matrix(0,ncol=NCOL(x),nrow=length(quantiles)) else vv<-matrix(NA,ncol=NCOL(x),nrow=length(quantiles)) dimnames(vv)<-list(paste("q",round(quantiles,2),sep=""), names(x)) attr(rval,"var")<-vv attr(rval,"statistic")<-quantiles if (return.replicates) rval<-list(mean=rval,replicates=NULL) class(rval)<-"svrepstat" return(rval) } if (is.null(df)) df<-degf(design) if (df==Inf) qcrit<-qnorm else qcrit <-function(...) qt(...,df=df) w<-weights(design,"analysis") if (interval=="quantile"){ ## interval on quantile scale if (ties=="discrete") computeQuantiles<-function(xx){ oo<-order(xx) ws<-weights(design,"sampling") epsilon<-0.1*min(ws[ws>0])/sum(ws) cum.ws<-cumsum(ws[oo])/sum(ws) rval<-approx(cum.ws,xx[oo],method=method,f=f, yleft=min(xx),yright=max(xx), xout=quantiles,ties=min)$y cum.w<-apply(w,2,function(wi) cumsum(wi[oo])/sum(wi)) qq<-apply(cum.w, 2, function(cum.wi){ approx(cum.wi,xx[oo],method=method,f=f, yleft=min(xx),yright=max(xx), xout=quantiles,ties=min)$y } ) if (length(quantiles)>1) qq<-t(qq) else qq<-as.matrix(qq) ##rval<-colMeans(qq) rval<-list(quantiles=rval, variances=diag(as.matrix(svrVar(qq,design$scale,design$rscales,mse=design$mse,coef=rval)))) if (return.replicates) rval<-c(rval, list(replicates=qq)) rval } else { ##ties="rounded" computeQuantiles<-function(xx){ ws<-weights(design,"sampling") epsilon<-0.1*min(ws[ws>0])/sum(ws) wws<-rowsum(ws,xx,reorder=TRUE) uxx<-sort(unique(xx)) cum.wws<-cumsum(wws)/sum(wws) rval<-approx(cum.wws,uxx,method=method,f=f, yleft=min(xx),yright=max(xx), xout=quantiles,ties=min)$y cum.w<-apply(rowsum(w,xx,reorder=TRUE),2,function(wi) cumsum(wi)/sum(wi)) qq<-apply(cum.w, 2, function(cum.wi){ approx(cum.wi,uxx,method=method,f=f, yleft=min(xx),yright=max(xx), xout=quantiles,ties=min)$y } ) if (length(quantiles)>1) qq<-t(qq) else qq<-as.matrix(qq) ##rval<-colMeans(qq) rval<-list(quantiles=rval, variances=diag(as.matrix(svrVar(qq,design$scale,design$rscales,mse=design$mse,coef=rval)))) if (return.replicates) rval<-c(rval, list(replicates=qq)) rval } } } else { ## interval on probability scale, backtransformed. if (ties=="discrete"){ computeQuantiles<-function(xx){ oo<-order(xx) w<-weights(design,"sampling") epsilon<-0.1*min(w[w>0])/sum(w) cum.w<- cumsum(w[oo])/sum(w) Qf<-approxfun(cum.w,xx[oo],method=method,f=f, yleft=min(xx),yright=max(xx), ties=min) point.estimates<-Qf(quantiles) if(length(quantiles)==1) estfun<-as.numeric(xx0])/sum(w) ww<-rowsum(w,xx,reorder=TRUE) uxx<-sort(unique(xx)) cum.w<- cumsum(ww)/sum(ww) Qf<-approxfun(cum.w,uxx,method=method,f=f, yleft=min(xx),yright=max(xx), ties=min) point.estimates<-Qf(quantiles) if(length(quantiles)==1) estfun<-as.numeric(xxnrweight | counts[expand]totalweight) i<-which(bad & !duplicated(as.vector(object$index))) if (length(i)==0) return(NULL) d<-dim(object$weights) nd<-length(d) dd<-cumprod(d) dn<-dimnames(object$weights) ii<-t(t(outer(i-1,dd,"%%")) %/% c(1,dd[-nd])) +1 keep<-!duplicated(i) ii<-ii[keep,,drop=FALSE] i<-i[keep] mm<-matrix("",ncol=NCOL(ii),nrow=NROW(ii)) colnames(mm)<-names(dimnames(object$index)) rownames(mm)<-i for(j in seq(length=length(d))){ mm[,j]<-dn[[j]][ii[,j]] } rval<-list(index=i, names=mm, totals=totals[expand][i],ratios=ratios[expand][i], counts=counts[expand][i],call=sys.call()) class(rval)<-"nonresponseSubset" rval } neighbours<-function(index,object){ d<-dim(object$index) if (length(index)==1) i<-object$index[index] else i<-sum(c(index-1,0)*cumprod(c(1,d)))+1 nd<-length(d) ## all the joins of that index ii<-which(object$index==object$index[i],arr.ind=TRUE) nii<-nrow(ii) diffs<-t(rbind(diag(rep(1,nd)),diag(rep(-1,nd)))) nbours<-matrix(nrow=2*nd*nii,ncol=nd) counter<-0 for(j in 1:nii){ nbours[counter+1:(2*nd),]<-t(ii[j,]+diffs) counter<-counter+2*nd } keep<-apply(nbours,1, function(x) all(x>0 & x<=d)) keep<- keep & !duplicated(nbours) nbours<-nbours[keep,,drop=FALSE] nbour.index<-apply(nbours,1, function(x) sum(c(x-1,0)*cumprod(c(1,d)))+1) nbour.index<-nbour.index[!(object$index[nbour.index] %in% object$index[index])] object[nbour.index] } weights.nonresponse<-function(object,...){ w<-rowsum(as.vector(object$population),as.vector(object$index))/rowsum(as.vector(object$weights),as.vector(object$index)) expand<-as.integer(as.factor(object$index)) array(w[expand],dim=dim(object$index),dimnames=dimnames(object$index)) } joinCells<-function(object,a,...){ if (!is.list(a)) a<-list(a,...) d<-dim(object$index) nd<-length(d) if (length(a[[1]])>1) a<-sapply(a,function(ai) sum(c(ai-1,0)*cumprod(c(1,d)))+1) else a<-do.call("c",a) nd<-length(d) if(length(a)<2){ warning("Can't join a single cell") return(invisible(object)) } indices<-object$index[a] if (length(unique(indices))<2){ warning("These cells are already joined") return(invisible(object)) } object$index[object$index %in% object$index[a]]<-min(object$index[a]) object$joins<-c(object$joins,list(which(object$index %in% object$index[a]))) object } survey/R/recalibrate.R0000644000176200001440000000024214227413523014411 0ustar liggesusers recalibrate<-function(design, formula, ...){ m<-model.matrix(formula, model.frame(design)) calibrate(design,formula, colSums(m*weights(design))) } survey/R/bootstrap.R0000644000176200001440000000567413517345031014166 0ustar liggesusers subbootstratum<-function(psu,replicates){ upsu<-sample(unique(psu)) n<-length(upsu) replicate(replicates, table(factor(sample(upsu, length(upsu)-1,replace=TRUE), levels=unique(psu))))*n/(n-1) } bootstratum<-function(psu, popsize, replicates){ upsu<-sample(unique(psu)) if (is.null(popsize)){ replicate(replicates, table(factor(sample(upsu,length(upsu),replace=TRUE), levels=unique(psu)))) } else { replicate(replicates, table(factor(sample(rep(upsu,length=popsize), length(upsu)), levels=unique(psu)))) } } bootweights<-function(strata, psu, replicates=50, fpc=NULL, fpctype=c("population","fraction","correction"), compress=TRUE){ fpctype<-match.arg(fpctype) index<-match(psu,psu[!duplicated(psu)]) upsu<-unique(psu) strata<-as.character(strata) weights<-matrix(nrow=length(upsu),ncol=replicates) ustrata<-strata[!duplicated(psu)] ufpc<-fpc[!duplicated(psu)] for(s in unique(ustrata)){ this.stratum<-ustrata==s npsu<-length(unique(upsu[this.stratum])) if (is.null(fpc)) weights[this.stratum,]<-bootstratum(upsu[this.stratum],NULL,replicates) else { this.fpc<-ufpc[this.stratum] if (length(unique(this.fpc))>1) stop("More than one fpc in stratum",s) this.fpc<-this.fpc[1] if (fpctype=="population" && this.fpc 100*npsu) warning("Sampling fraction <1% in stratum",s," treated as zero.") weights[this.stratum,]<-bootstratum(upsu[this.stratum], popsize=this.fpc,replicates=replicates) } } ## harmonic mean of stratum sizes psu.per.strata<-1/mean(1/table(ustrata)) if (compress){ rw<-list(weights=weights,index=index) class(rw)<-"repweights_compressed" } else { rw<-weights[index,] } list(repweights=rw, scale=psu.per.strata/((psu.per.strata-1)*(replicates-1)), rscales=rep(1,replicates)) } subbootweights<-function(strata, psu, replicates=50, compress=TRUE){ index<-match(psu,psu[!duplicated(psu)]) upsu<-unique(psu) strata<-as.character(strata) weights<-matrix(nrow=length(upsu),ncol=replicates) ustrata<-strata[!duplicated(psu)] for(s in unique(ustrata)){ this.stratum<-ustrata==s npsu<-length(unique(upsu[this.stratum])) weights[this.stratum,]<-subbootstratum(upsu[this.stratum],replicates) } if (compress){ rw<-list(weights=weights,index=index) class(rw)<-"repweights_compressed" } else { rw<-weights[index,] } list(repweights=rw, scale=1/(replicates-1), rscales=rep(1,replicates)) } survey/R/rsquared.R0000644000176200001440000000207213517345031013764 0ustar liggesusers psrsq<-function(object, method=c("Cox-Snell","Nagelkerke"),...){ UseMethod("psrsq",object) } psrsq.glm<-function(object, method=c("Cox-Snell","Nagelkerke"),...){ nullmodel<-update(object,.~1) method<-match.arg(method) ell0<-as.vector(logLik(nullmodel)) ell1<-as.vector(logLik(object)) n<-object$df.null+1 mutualinf<- -2*(ell1-ell0)/n r2cs<-1-exp(mutualinf) if (method == "Cox-Snell") return(r2cs) scaling<-1-exp(2*ell0/n) r2cs/scaling } psrsq.svyglm<-function(object, method=c("Cox-Snell", "Nagelkerke"),...){ method<-match.arg(method) if (!(object$family$family %in% c("binomial","quasibinomial","poisson","quasipoisson"))) stop("Only implemented for discrete data") w<-weights(object$survey.design,"sampling") N<-sum(w) n<-sum(object$prior.weights) minus2ell0<-object$null.deviance*(N/n) minus2ell1<-object$deviance*(N/n) mutualinf<-(minus2ell1-minus2ell0)/N r2cs<-1-exp(mutualinf) if (method =="Cox-Snell") return(r2cs) if (any(w<1)) warning("Weights appear to be scaled: rsquared may be wrong") scaling<-1-exp(-minus2ell0/N) r2cs/scaling }survey/R/surveyby.R0000755000176200001440000003776014221522101014031 0ustar liggesusers## ## tables of statistics. ## svyby<-function(formula, by, design,...) UseMethod("svyby",design) unwrap<-function(x,nvartype) UseMethod("unwrap",x) unwrap2<-function(x) UseMethod("unwrap2",x) unwrap2.default <- function(x){ if(!is.null(attr(x, "deff"))) c(statistic = unclass(x), DEff = deff(x)) else c(statistic = unclass(x)) } unwrap2.newsvyquantile <- function(x){ rval<-do.call(rbind,x) rownames(rval)<-names(x) rval } unwrap.default <-function(x,nvartype){ rval<-c(coef(x)) nvar<-length(rval) rval<-c(rval,c(se=SE(x), ci_l=confint(x)[,1], ci_u=confint(x)[,2], cv=cv(x,warn=FALSE), `cv%`=cv(x,warn=FALSE)*100, var=SE(x)^2)[rep((nvartype-1)*(nvar),each=nvar)+(1:nvar)]) if(!is.null(attr(x,"deff"))) rval<-c(rval,DEff=deff(x)) rval } strings_to_factors<-function(formula, design){ allv<-intersect(all.vars(formula), colnames(design)) vclass<-sapply(model.frame(design)[,allv,drop=FALSE], class) if (!any(vclass=="character")) return(design) vfix<-names(vclass)[vclass=="character"] l<-as.list(vfix) names(l)<-vfix fl<-lapply(l, function(li) bquote(factor(.(as.name(li))))) expr<-bquote(update(design, ..(fl)), splice=TRUE) eval(expr) } svyby.default<-function(formula, by, design, FUN,..., deff=FALSE, keep.var=TRUE, keep.names=TRUE, verbose=FALSE, vartype=c("se","ci","ci","cv","cvpct","var"), drop.empty.groups=TRUE, covmat=FALSE, return.replicates=FALSE, na.rm.by=FALSE, na.rm.all=FALSE, stringsAsFactors=TRUE, multicore=getOption("survey.multicore")){ if (inherits(by, "formula")) byfactors<-model.frame(by, model.frame(design), na.action=na.pass) else byfactors<-as.data.frame(by) if(return.replicates){ if (!inherits(design,"svyrep.design")) stop("return.replicates=TRUE not implemented for this design type") } if(stringsAsFactors){ design<-strings_to_factors(formula,design) } if (multicore && !requireNamespace("parallel",quietly=TRUE)) multicore<-FALSE ## some people insist on using vectors rather than formulas ## so I suppose we should be nice to them if (!inherits(formula, "formula")){ if (NROW(formula)!=NROW(byfactors)) stop("'formula' is the wrong length") if (!(is.data.frame(formula) || is.matrix(formula) || is.vector(formula))){ stop("invalid type for 'formula'") } } hasdeff<- is.character(deff) || deff ## all combinations that actually occur in this design byfactor<-do.call("interaction", byfactors) dropped<- weights(design,"sampling")==0 if (na.rm.by) dropped<-dropped | apply(byfactors, 1, function(x) any(is.na(x))) if (na.rm.all){ if (inherits(formula,"formula")) allx<-model.frame(formula,model.frame(design),na.action=na.pass) else allx<-formula dropped <- dropped | (!complete.cases(allx)) } uniquelevels<-sort(unique(byfactor[!dropped])) uniques <- match(uniquelevels, byfactor) if(missing(vartype)) vartype<-"se" vartype<-match.arg(vartype,several.ok=TRUE) nvartype<-base::which(eval(formals(sys.function())$vartype) %in% vartype) if(any(is.na(nvartype))) stop("invalid vartype") if (keep.var){ ## In dire need of refactoring (or rewriting) ## but it seems to work. results<-(if (multicore) parallel::mclapply else lapply)(uniques, function(i){ if(verbose && !multicore) print(as.character(byfactor[i])) if (inherits(formula,"formula")) data<-formula else data<-subset(formula, byfactor %in% byfactor[i]) if (covmat || return.replicates) { FUN(data, design[byfactor %in% byfactor[i],], deff=deff,...,return.replicates=TRUE) } else { FUN(data, design[byfactor %in% byfactor[i],], deff=deff,...) } }) rval<-t(sapply(results, unwrap,nvartype=nvartype)) if ((covmat && inherits(design, "svyrep.design")) || return.replicates) { replicates<-do.call(cbind,lapply(results,"[[","replicates")) attr(replicates,"scale")<-design$scale attr(replicates, "rscales")<-design$rscales attr(replicates, "mse")<-design$mse colnames(replicates)<-rep(as.character(uniquelevels), each=NCOL(replicates)/length(uniquelevels)) covmat.mat<-svrVar(replicates,design$scale,design$rscales, mse=design$mse,coef=as.vector(sapply(results,coef))) } else{ covmats<-lapply(results,vcov) ncovmat<-sum(sapply(covmats,ncol)) covmat.mat<-matrix(0,ncol=ncovmat,nrow=ncovmat) j<-0 for(i in 1:length(covmats)){ ni<-nrow(covmats[[i]]) covmat.mat[j+(1:ni),j+(1:ni)]<-covmats[[i]] j<-j+ni } } } else { rval<-sapply(uniques, function(i) { if(verbose) print(as.character(byfactor[i])) if (inherits(formula,"formula")) data<-formula else data<-subset(formula, byfactor %in% byfactor[i]) unwrap2(FUN(data, design[byfactor %in% byfactor[i],], deff=deff,...))} ) if (is.matrix(rval)) rval<-t(rval) } nr<-NCOL(rval) nstats<-nr/(1+ keep.var*(length(vartype)+ ("ci" %in% vartype)) + hasdeff) if (nr>1) rval<-cbind(byfactors[uniques,,drop=FALSE], rval) else rval <-cbind(byfactors[uniques,,drop=FALSE], statistic=rval) expand.index<-function(index,reps,x=FALSE){ ns<-max(index) if (x){ i<-matrix(1:(ns*reps),ncol=reps) rval<-t(i[index,]) } else{ i<-matrix(1:(ns*reps), ncol=reps, nrow=ns, byrow=TRUE) rval<- i[index,] } as.vector(rval) } if(drop.empty.groups){ if (keep.names) rownames(rval)<-paste(byfactor[uniques]) rval<-rval[order(byfactor[uniques]),] i<-expand.index(order(byfactor[uniques]),nstats) if (keep.var) covmat.mat<-covmat.mat[i,i] } else { a<-do.call("expand.grid", lapply(byfactors,function(f) levels(as.factor(f)))) a<-cbind(a,matrix(NA, ncol=nr, nrow=nrow(a))) names(a)<-names(rval) a[match(byfactor[uniques], levels(byfactor)),]<-rval rval<-a if (keep.names) rownames(rval)<-levels(byfactor) if (keep.var){ tmp<-matrix(ncol=nrow(a)*nstats,nrow=nrow(a)*nstats) i<-expand.index(match(byfactor[uniques], levels(byfactor)),nstats,TRUE) tmp[i,i]<-covmat.mat covmat.mat<-tmp } } attr(rval,"svyby")<-list(margins=1:NCOL(byfactors),nstats=nstats, vars=if(keep.var) length(vartype) else 0, deffs=deff, statistic=deparse(substitute(FUN)), variables= names(rval)[-(1:NCOL(byfactors))][1:nstats], vartype=vartype ) if (!keep.names) rownames(rval)<-1:NROW(rval) if(covmat) attr(rval,"var")<-covmat.mat if (return.replicates) attr(rval,"replicates")<-replicates attr(rval,"call")<-sys.call() class(rval)<-c("svyby","data.frame") rval } SE.svyby <-function(object,...){ aa<-attr(object,"svyby") if (!aa$vars) stop("Object does not contain variances") vartype<-attr(object,"svyby")$vartype vartype <- c("se","ci","ci","cv","cvpct","var")[c("se","ci","ci","cv","cvpct","var") %in% vartype] if (pos<-match("se",vartype,0)) object[,max(aa$margins)+aa$nstats*pos+(1:aa$nstats)] else if (pos<-match("var",vartype,0)) sqrt(object[,max(aa$margins)+aa$nstats*pos+(1:aa$nstats)]) else if (pos<-match("cv",vartype,0)) object[,max(aa$margins)+aa$nstats*pos+(1:aa$nstats)]*coef(object) else if (pos<-match("cvpct",vartype,0)) object[,max(aa$margins)+aa$nstats*pos+(1:aa$nstats)]*coef(object)/100 else stop("This can't happen") } coef.svyby<-function (object, ...) { aa <- attr(object, "svyby") rval <- object[, max(aa$margins) + (1:aa$nstats)] if (is.null(dim(rval))){ names(rval) <- row.names(object) } else { rval<-as.vector(as.matrix(rval)) names(rval)<-outer(rownames(object), gsub("statistics\\.","",aa$variables), paste, sep=":") } rval } deff.svyby<-function(object,...){ aa<-attr(object,"svyby") if (!aa$deffs) stop("object does not have design effect information") object[,max(aa$margins)+aa$nstats*(1+aa$vars)+(1:aa$nstats)] } vcov.svyby<-function(object,...){ rval<-attr(object,"var") if(is.null(rval)){ warning("Only diagonal elements of vcov() available") se<-SE(object) if (is.data.frame(se)) se<-as.vector(as.matrix(se)) if(length(se)>1) rval<-diag(se^2) else rval<-as.matrix(se^2) } nms<-names(coef(object)) dimnames(rval)<-list(nms,nms) rval } ## allows for influence functions. Does not allow for replicates svyby.survey.design2<-function(formula, by, design, FUN,..., deff=FALSE, keep.var=TRUE, keep.names=TRUE, verbose=FALSE, vartype=c("se","ci","ci","cv","cvpct","var"), drop.empty.groups=TRUE, covmat=FALSE, influence=covmat, na.rm.by=FALSE, na.rm.all=FALSE, stringsAsFactors=TRUE, multicore=getOption("survey.multicore")){ if (inherits(by, "formula")) byfactors<-model.frame(by, model.frame(design), na.action=na.pass) else byfactors<-as.data.frame(by) if (stringsAsFactors){ design<-strings_to_factors(formula, design) } if (multicore && !requireNamespace("parallel",quietly=TRUE)) multicore<-FALSE ## some people insist on using vectors rather than formulas ## so I suppose we should be nice to them if (!inherits(formula, "formula")){ if (NROW(formula)!=NROW(byfactors)) stop("'formula' is the wrong length") if (!(is.data.frame(formula) || is.matrix(formula) || is.vector(formula))){ stop("invalid type for 'formula'") } } hasdeff<- is.character(deff) || deff ## all combinations that actually occur in this design byfactor<-do.call("interaction", byfactors) dropped<- weights(design,"sampling")==0 if (na.rm.by) dropped<-dropped | apply(byfactors, 1, function(x) any(is.na(x))) if (na.rm.all){ if (inherits(formula,"formula")) allx<-model.frame(formula,model.frame(design),na.action=na.pass) else allx<-formula dropped <- dropped | (!complete.cases(allx)) } uniquelevels<-sort(unique(byfactor[!dropped])) uniques <- match(uniquelevels, byfactor) if(missing(vartype)) vartype<-"se" vartype<-match.arg(vartype,several.ok=TRUE) nvartype<-base::which(eval(formals(sys.function())$vartype) %in% vartype) if(any(is.na(nvartype))) stop("invalid vartype") if (keep.var){ ## In dire need of refactoring (or rewriting) ## but it seems to work. results<-(if (multicore) parallel::mclapply else lapply)(uniques, function(i){ idx<- byfactor %in% byfactor[i] ##save this for assembling influence functions if(verbose && !multicore) print(as.character(byfactor[i])) if (inherits(formula,"formula")) data<-formula else data<-subset(formula, byfactor %in% byfactor[i]) if (covmat||influence ) { r<-FUN(data, design[byfactor %in% byfactor[i],], deff=deff,...,influence=influence) } else { r<-FUN(data, design[byfactor %in% byfactor[i],], deff=deff,...) } attr(r,"index")<-idx r }) rval<-t(sapply(results, unwrap,nvartype=nvartype)) if (covmat || influence) { ## do the influence function thing here infs<-lapply(results,attr, "influence") idxs<-lapply(results,attr, "index") if (all(sapply(infs,is.null))) stop("FUN does not return influence functions") inflmats<-vector("list",length(infs)) for(i in seq_along(infs)){ inflmats[[i]]<-matrix(0, ncol=NCOL(infs[[i]]),nrow=length(idxs[[i]])) inflmats[[i]][idxs[[i]],]<-infs[[i]] } inflmat<-do.call(cbind,inflmats) covmat.mat<-svyrecvar(inflmat,design$cluster, design$strata, design$fpc, postStrata=design$postStrata) } else{ covmats<-lapply(results,vcov) ncovmat<-sum(sapply(covmats,ncol)) covmat.mat<-matrix(0,ncol=ncovmat,nrow=ncovmat) j<-0 for(i in 1:length(covmats)){ ni<-nrow(covmats[[i]]) covmat.mat[j+(1:ni),j+(1:ni)]<-covmats[[i]] j<-j+ni } } } else { rval<-sapply(uniques, function(i) { if(verbose) print(as.character(byfactor[i])) if (inherits(formula,"formula")) data<-formula else data<-subset(formula, byfactor %in% byfactor[i]) unwrap2(FUN(data, design[byfactor %in% byfactor[i],], deff=deff,...))} ) if (is.matrix(rval)) rval<-t(rval) } nr<-NCOL(rval) nstats<-nr/(1+ keep.var*(length(vartype)+ ("ci" %in% vartype)) + hasdeff) if (nr>1) rval<-cbind(byfactors[uniques,,drop=FALSE], rval) else rval <-cbind(byfactors[uniques,,drop=FALSE], statistic=rval) expand.index<-function(index,reps,x=FALSE){ ns<-max(index) if (x){ i<-matrix(1:(ns*reps),ncol=reps) rval<-t(i[index,]) } else{ i<-matrix(1:(ns*reps), ncol=reps, nrow=ns, byrow=TRUE) rval<- i[index,] } as.vector(rval) } if(drop.empty.groups){ if (keep.names) rownames(rval)<-paste(byfactor[uniques]) rval<-rval[order(byfactor[uniques]),] i<-expand.index(order(byfactor[uniques]),nstats) if (keep.var) covmat.mat<-covmat.mat[i,i] } else { a<-do.call("expand.grid", lapply(byfactors,function(f) levels(as.factor(f)))) a<-cbind(a,matrix(NA, ncol=nr, nrow=nrow(a))) names(a)<-names(rval) a[match(byfactor[uniques], levels(byfactor)),]<-rval rval<-a if (keep.names) rownames(rval)<-levels(byfactor) if (keep.var){ tmp<-matrix(ncol=nrow(a)*nstats,nrow=nrow(a)*nstats) i<-expand.index(match(byfactor[uniques], levels(byfactor)),nstats,TRUE) tmp[i,i]<-covmat.mat covmat.mat<-tmp } } attr(rval,"svyby")<-list(margins=1:NCOL(byfactors),nstats=nstats, vars=if(keep.var) length(vartype) else 0, deffs=deff, statistic=deparse(substitute(FUN)), variables= names(rval)[-(1:NCOL(byfactors))][1:nstats], vartype=vartype ) if (!keep.names) rownames(rval)<-1:NROW(rval) if(covmat) attr(rval,"var")<-covmat.mat if (influence) attr(rval,"influence")<-inflmat attr(rval,"call")<-sys.call() 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r<-regTermTest(fit, ftest, method="LRT") deltabar<-mean(r$lambda) } else { r<-list(lambda=0) deltabar<-NaN } d<-fit$deviance c(eff.p=sum(r$lambda), AIC=d+k*sum(r$lambda),deltabar=deltabar) } extractAIC.svrepglm<-extractAIC.svyglm BIC.svyglm<-function(object,...,maximal){ if (length(list(...))){ do.call(rbind,lapply(list(object,...),dBIC,modelM=maximal)) } else { dBIC(object,modelM=maximal) } } dBIC<-function(modela, modelM){ pm<-modela$rank pM<-modelM$rank if (any(!(names(coef(modela))%in% names(coef(modelM))))){ stop("coefficients in model but not in maximal model") } index<-!(names(coef(modelM))%in% names(coef(modela))) n<-1+modela$df.null if(any(index)){ wald<-coef(modelM)[index]%*%solve(vcov(modelM)[index,index],coef(modelM)[index]) detDelta<-det(solve(modelM$naive.cov[index,index,drop=FALSE],modelM$cov.unscaled[index,index,drop=FALSE])) dbar<-detDelta^(1/(pM-pm)) nstar<-n/dbar }else { wald<-0 detDelta<-1 dbar<-1 nstar=NaN } c(p=pm, 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0ustar liggesuserssvydesign Specify a survey design twophase Specify two-phase designs svrepdesign Specify replication-weight designs as.svrepdesign Compute replication weights for a design svydesign.imputationList Designs for multiply-imputed data. subset.survey.design Subset of survey update.survey.design Add variables to a survey design postStratify Post-stratify a survey design rake Rake a survey design calibrate Calibration and generalised raking estWeight Estimated weights for two-phase designs trimWeights Trim sampling weights. ----- svyplot Survey-weighted graphics svycoplot Conditioning plots for survey data svyqqplot Quantile-quantile plots for survey data svyhist Survey-weighted histogram svysmooth Smoothers and density estimates svycdf Cumulative distribution functions svykm Survival curves svyboxplot Survey-weighted boxplots svyby Tables of statistics ftable.svystat Formatted tables of statistics ftable.svrepstat ftable.svyby cv extract coefficient of variation SE extract standard error deff extract design effect confint extract confidence intervals svycontrast linear combinations of estimates ----- svycoxph Survey-weighted Cox models svyglm Survey-weighted generalised linear models. svyolr Ordinal logistic regression svysurvreg Accelerated failure models regTermTest Test sets of terms in a regression model svyloglin Loglinear models svyquantile Summary statistics for sample surveys svytotal svymean svyvar svyratio svytable Contingency tables svychisq Tests for two-way tables svyttest Design-based t-test svyranktest Design-based two-sample rank tests svylogrank Design-based logrank tests svystandardize Direct standardisation svykappa Cohen's kappa for inter-rater agreement svycralpha Cronbach's alpha svyivreg Two-stage least-squares for instrumental variables svymle Maximum pseudolikelihood estimation in complex surveys svynls Design-weighted nonlinear least squares svyprcomp Principal component analysis and biplots svyfactanal Weighted maximum likelihood factor analysis ----- withReplicates Replicate variances for arbitrary statistics ---- hadamard Hadamard matrices is.hadamard paley Paley-type Hadamard matrices ---- stratsample Take a stratified sample ---- Utility functions: make.calfun Define calibration metrics cal.linear cal.raking cal.logit *svy.varcoef Sandwich variance estimator for glms *svyCprod Computations for survey variances *svyrecvar Computations for multistage samples *svrVar Computations for replication variances jk1weights create replication weights jknweights create replication weights bootweights create replication weights brrweights create replication weights mrbweights create replication weights grake Computations for generalised raking *regcalibrate Computations for regression calibration degf Degrees of freedom for replication weights *twophasevar Variances in two-phase designs as.svydesign2 Convert a old svydesign object to the new format. * not exported from NAMESPACE ----- Data: election US 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FlGTؙ|hySNPTr6ϮF Ɠo3FZb;an#ƛmtg&܎e EDSy(_3N&`r :q#@sxp+s)lt"=*~9r?Ky?M\y3ZIB*?>?|v,lX6Рb*~s^۸9$T7 ӓ_+]lBT GRHjCJŽ r@}y"_|VR^A0^0ag]nS )?˿i۠[㓃=$ mHlj5$<>AmӴ=h+(d_@Pn_ߏ`2¨<J*C [Y0 s5aRo۰XccAE!VFͬo_3ѵÕ޽8,&'se4IՊƑE e/j6G}쀯gq*v)1>/5u b_Ŧl*" z_~ίf28<W &龯5tomlW2m`/mԱ]uR:݋vG:e_FmMQ&vq)Feܘ5mtmmBF-SQǭcWs3*SO K].v{]Î*k>ls;yŦ5]tum\8eԚ.~{]dm|M]޼bh0= pa|kpq8K_rZTMjW5kaa$6*p!ٯQ;*Ư%xo {?xׅ/.-){}8 O.{,.{Aƹ endstream endobj 105 0 obj << /Type /XRef /Index [0 106] /Size 106 /W [1 3 1] /Root 103 0 R /Info 104 0 R /ID [ ] /Length 306 /Filter /FlateDecode >> stream x%ͻ/a~.uE/ԥVVKՖM 0q$b@b%0J `<'o+"@"~aH,Y Y2OIY` VKVH-GC;k'MͤivE: G; !3$L"(%} $q2D!U79?^XAV [j뵚"QI`j xJwV)3a5 'xb[y YMyU>ܗRV1Tڻ~lvo*pj:+ endstream endobj startxref 223758 %%EOF survey/inst/doc/pps.Rnw0000644000176200001440000001455113517345006014635 0ustar liggesusers\documentclass{article} \usepackage{url} \addtolength{\topmargin}{-0.5in} \addtolength{\textheight}{0.75in} \addtolength{\oddsidemargin}{-0.5in} \addtolength{\textwidth}{1in} %\VignetteIndexEntry{Analysing PPS designs} \usepackage{Sweave} \author{Thomas Lumley} \title{Describing PPS designs to R} \begin{document} \maketitle The survey package has always supported PPS (ie, arbitrary unequal probability) sampling with replacement, or using the with-replacement single-stage approximation to a multistage design. No special notation is required: just specify the correct sampling weights. Version 3.11 added an another approximation for PPS sampling without replacement, and version 3.16 added more support. There are two broad classes of estimators for PPS sampling without replacement: approximations to the Horvitz--Thompson and Yates--Grundy estimators based on approximating the pairwise sampling probabilities, and estimators of H\'ajek type that attempt to recover the extra precision of a without-replacement design by conditioning on the estimated population size. \subsection*{Direct approximations} Using the standard recursive algorithm for stratified multistage sampling when one or more stages are actually PPS gives an approximation due to Brewer. This is simple to compute, always non-negative, and appears to be fairly efficient. \subsection*{Approximating $\pi_{ij}$} Given the pairwise sampling probabilities $\pi_{ij}$ we can define the weighted covariance of sampling indicators $$\check{\Delta}_{ij} = 1-\frac{\pi_i\pi_j}{\pi_{ij}}$$ and the weighted observations $$\check{x}_i=\frac{1}{\pi_i}x_i.$$ Two unbiased estimators of the variance of the total of $x$ are the Horvitz--Thompson estimator $$\hat V_{HT}= \sum_{i,j=1}^n \check{\Delta}\check{x}_i\check{x}_j$$ and the Yates--Grundy(--Sen) estimator $$\hat V_{YG}= \frac{1}{2}\sum_{i,j=1}^n \check{\Delta}(\check{x}_i-\check{x}_j)^2$$ The Yates--Grundy estimator appears to be preferred in most comparisons. It is always non-negative (up to rounding error, at least). In principle, $\pi_{ij}$ might not be available and various approximations have been proposed. The (truncated) Hartley--Rao approximation is $$\check{\Delta}_{ij}=1-\frac{n-\pi_i-\pi_j+\sum_{k=1}^N\pi^2_k/n}{n-1}$$ which requires knowing $\pi_i$ for all units in the population. The population sum can be estimated from the sample, giving a further approximation $$\check{\Delta}_{ij}=1-\frac{n-\pi_i-\pi_j+\sum_{k=1}^n\pi_k/n}{n-1}.$$ that requires only the sample $\pi_i$. Overton's approximation is $$\check{\Delta}_{ij}=1-\frac{n-(\pi_i+\pi_j)/2}{n-1}$$ which also requires only the sample $\pi_i$. In practice, given modern computing power, $\pi_{ij}$ should be available either explicitly or by simulation, so the Hartley--Rao and Overton approximations are not particularly useful. \subsection{Using the PPS estimators} At the moment, only Brewer's approximation can be used as a component of multistage sampling, though for any sampling design it is possible to work out the joint sampling probabilities and use the other approaches. The other approaches can be used for cluster sampling or for sampling of individual units. This is likely to change in the future. To specify a PPS design, the sampling probabilities must be given in the \texttt{prob} argument of \texttt{svydesign}, or in the \texttt{fpc} argument, with \texttt{prob} and \texttt{weight} unspecified. In addition, it is necessary to specify which PPS computation should be used, with the \texttt{pps} argument. The optional \texttt{variance} argument specifies the Horvitz--Thompson (\texttt{variance="HT"}) or Yates--Grundy (\texttt{variance="YG"}) estimator, with the default being \texttt{"HT"}. Some estimators require information in addition to the sampling probabilities for units in the sample. This information is supplied to the \texttt{pps=} argument of \texttt{svydesign} using wrapper functions that create objects with appropriate classes. To specify the population sum $\sum pi_i^2/n$ needed for the Hartley--Rao approximation, use \texttt{HR()}, and to specify a matrix of pairwise sampling probabilities use \texttt{ppsmat()}. The function \texttt{HR()} without an argument will use the Hartley--Rao approximation and estimate the population sum from the sample. The data set \texttt{election} contains county-level voting data from the 2004 US presidential elections, with a PPS sample of size 40 taken using Till\'e's splitting method, from the \texttt{sampling} package. The sampling probabilities vary widely, with Los Angeles County having a probability of 0.9 and many small counties having probabilities less than 0.0005. <<>>= library(survey) data(election) summary(election$p) summary(election_pps$p) @ Some possible survey design specifications for these data are: <<>>= ## Hajek type dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") ## Horvitz-Thompson type dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) ## Yates-Grundy type dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") ## The with-replacement approximation dppswr <-svydesign(id=~1, probs=~p, data=election_pps) @ All the without-replacement design objects except for Brewer's method include a matrix $\check{\Delta}$. These can be visualized with the \texttt{image()} method. These plots use the \texttt{lattice} package and so need \texttt{show()} to display them inside a program: <>= show(image(dpps_ht)) @ <>= show(image(dpps_ov)) @ In this example there are more negative entries in $\check{\Delta}$ with the approximate methods than when the full pairwise sampling matrix is supplied. The estimated totals are the same with all the methods, but the standard errors are not. <<>>= svytotal(~Bush+Kerry+Nader, dpps_ht) svytotal(~Bush+Kerry+Nader, dpps_yg) svytotal(~Bush+Kerry+Nader, dpps_hr) svytotal(~Bush+Kerry+Nader, dpps_hryg) svytotal(~Bush+Kerry+Nader, dpps_hr1) svytotal(~Bush+Kerry+Nader, dpps_br) svytotal(~Bush+Kerry+Nader, dpps_ov) svytotal(~Bush+Kerry+Nader, dppswr) @ \end{document} survey/inst/doc/precalibrated.pdf0000644000176200001440000037670014576431432016653 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Length 250 >> stream concordance:precalibrated.tex:precalibrated.Rnw:1 14 1 1 2 1 0 2 1 3 0 1 2 2 1 1 2 6 0 1 1 5 0 2 1 7 0 1 2 3 1 1 2 1 0 1 1 7 0 1 2 1 1 1 2 8 0 1 2 1 1 1 3 2 0 1 1 1 2 7 0 1 2 2 1 1 2 7 0 1 1 6 0 1 1 6 0 1 1 6 0 1 2 6 0 1 1 6 0 1 1 6 0 1 1 7 0 1 2 3 1 endstream endobj 4 0 obj << /Length 1509 /Filter /FlateDecode >> stream xڝWA6 Wgɲ퀵h 0ʋZ6$Y.#E9b'SEO݋_ާrŋXLvXnW9,Vzq ""T41HPXԾ۔YHX:2,zv zgۥrd\N.Gl4TPt\دK'/6=AK4+2 T] aѠe*s! 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F`RޗS/~g> ilXc3t5n/.XjR˺^o3L9g)q:#eNC?lыѣc`83ֲ3#>w+>Cӊ݊͊T_|~+>Cg!>cB|/g)g{!>_|&~'a9K!B>,TGbPq> stream x}UMo0+J! ᫊"Rjn"B73W3njehܬ;WWU\8׺v=ߩonTtƓ7]ziTCA:fzg 4R㫧~R~J}6O7}w̭uזSIе(TPfw۷莛n˥?8ޛ`~?n8t/3NbV+պ};:5wɩƆ56}Χ]]₥+U9q~=W(_KdR$| 4hd52HHNsL FU*q8cMMh QEk%RWp gn~ȋCam `42W0A/c9^'-pʹ)pq[[i])9^W5js7 Gb_#xb~ ' ˏ1}cLu'No ޅ0&1Ie76Z cx-~`& y%Q?K'!/h],KV0d 1էM=Ռ3g1Y ~i?'?!>L9g)q:#eNC?lыѣc`83ֲ3#>w+>Cӊ݊͊T_|~+>Cg!>cB|/g)g{!>_|&~'a9K!B>,TGbPq> endobj 10 0 obj << /Type /ObjStm /N 27 /First 198 /Length 1776 /Filter /FlateDecode >> stream xY[o6~!xb@6[ek:yPm&X_-iS/S$'yx+2&ϘPL ɤLXf0-3J1)uIɄI^ T., l)2 O 6  Ǵ ϴ)dn`3ÌN 'hxcXά@ $Skf2G|q^\?M75P倿 uuƉ0){arK#Q G?QBCj= 5Ali)'‡].t;0̓``v; !186-<mg6=wg1C.fC: ,츮,vUkP1mVunh:?1o{TTNB:7 Q}1JfPktKsPK>Vȟ4v*T+3O4VzBD)]\csw;8TElA[k$qH|Rv[0g^Xp|ߞ Y;/"tkቢ!ikjM;N*7Injvt(eFuWI4J4N%&mt(u hL١WZ(r:?rh::WȥL L&dewzH{[ْjt\\ {SSTuMi>&>%cD5G7>0{;G!*ڌ Mru*#Svv5hN !r ![yϊ2ȕSE\"9?hy$#h)ٶ7c&uli>I쮒\-\{BIlV[{М7.F{6r&#x5촷Ԉ>xY.jߊ^8fx_+yu~ͧ|q1 s^?_|oy@|A,\5i$ |e5VMH3K^L!}o;?'+PMn^=;z}.?mvCfkZ~MּьShOUO|E_<;()d Ex A^(U?X(T我Et|Wj}414?}&l-}-ռ=q՟s(_l$yΨo%Tʢe(],~鴘Dgd$,q 1> -1ߞG67 7ys*ގ uXmCJgKf*~H`e_WgE 8B][cwϔ孂OjRίyyup }y#e{1J p6X#yʤ6YZ) RԎ&1/H'tc6KٖҲF] ~^g,pcyG,wo]w {atywGnOTE&^LH۩)a}GDu?zqO]wrY7qע}tQNiMn#ʷ 6N% >pBjEHcPߏp#Tb!^:]P z^~bBлBAݏPpo!tذPޏP"5}%5࿔Hibݪzx+XN `ۿ1f endstream endobj 46 0 obj << /Type /XRef /Index [0 47] /Size 47 /W [1 3 1] /Root 44 0 R /Info 45 0 R /ID [ ] /Length 160 /Filter /FlateDecode >> stream x%+@(*/$$\ 8@#pCp@@@`?& ?1< D D_Ő|v"*#D6G' |E36VZdU vv(4%7?s endstream endobj startxref 130085 %%EOF survey/inst/doc/survey.R0000644000176200001440000000437214576431435015033 0ustar liggesusers### R code from vignette source 'survey.Rnw' ################################################### ### code chunk number 1: survey.Rnw:26-29 ################################################### library(survey) data(api) dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) ################################################### ### code chunk number 2: survey.Rnw:33-34 ################################################### summary(dclus1) ################################################### ### code chunk number 3: survey.Rnw:43-48 ################################################### svymean(~api00, dclus1) svyquantile(~api00, dclus1, quantile=c(0.25,0.5,0.75), ci=TRUE) svytotal(~stype, dclus1) svytotal(~enroll, dclus1) svyratio(~api.stu,~enroll, dclus1) ################################################### ### code chunk number 4: survey.Rnw:55-56 ################################################### svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H")) ################################################### ### code chunk number 5: survey.Rnw:64-66 ################################################### vars<-names(apiclus1)[c(12:13,16:23,27:37)] svymean(make.formula(vars),dclus1,na.rm=TRUE) ################################################### ### code chunk number 6: survey.Rnw:73-74 ################################################### svyby(~ell+meals, ~stype, design=dclus1, svymean) ################################################### ### code chunk number 7: survey.Rnw:79-83 ################################################### regmodel <- svyglm(api00~ell+meals,design=dclus1) logitmodel <- svyglm(I(sch.wide=="Yes")~ell+meals, design=dclus1, family=quasibinomial()) summary(regmodel) summary(logitmodel) ################################################### ### code chunk number 8: survey.Rnw:87-88 ################################################### gclus1 <- calibrate(dclus1, formula=~api99, population=c(6194, 3914069)) ################################################### ### code chunk number 9: survey.Rnw:91-96 ################################################### svymean(~api00, gclus1) svyquantile(~api00, gclus1, quantile=c(0.25,0.5,0.75), ci=TRUE) svytotal(~stype, gclus1) svytotal(~enroll, gclus1) svyratio(~api.stu,~enroll, gclus1) survey/inst/doc/epi.pdf0000644000176200001440000061271314576431430014622 0ustar liggesusers%PDF-1.5 % 3 0 obj << /Length 2073 /Filter /FlateDecode >> stream xڅXKDWDW8(=~[$4 bbO~D4~;1v{<|ؼzQ ?q? 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Some of the examples were published by Breslow \& Chatterjee (1999). The data are relapse rates from the National Wilm's Tumor Study (NWTS). Wilm's Tumour is a rare cancer of the kidney in children. Intensive treatment cures the majority of cases, but prognosis is poor when the disease is advanced at diagnosis and for some histological subtypes. The histological characterisation of the tumour is difficult, and histological group as determined by the NWTS central pathologist predicts much better than determinations by local institution pathologists. In fact, local institution histology can be regarded statistically as a pure surrogate for the central lab histology. In these examples we will pretend that the (binary) local institution histology determination (\texttt{instit}) is avavailable for all children in the study and that the central lab histology (\texttt{histol}) is obtained for a probability sample of specimens in a two-phase design. We treat the initial sampling of the study as simple random sampling from an infinite superpopulation. We also have data on disease stage, a four-level variable; on relapse; and on time to relapse. \section*{Case--control designs} Breslow \& Chatterjee (1999) use the NWTS data to illustrate two-phase case--control designs. The data are available at \url{http://faculty.washington.edu/norm/software.html} in compressed form; we first expand to one record per patient. <<>>= library(survey) load(system.file("doc","nwts.rda",package="survey")) nwtsnb<-nwts nwtsnb$case<-nwts$case-nwtsb$case nwtsnb$control<-nwts$control-nwtsb$control a<-rbind(nwtsb,nwtsnb) a$in.ccs<-rep(c(TRUE,FALSE),each=16) b<-rbind(a,a) b$rel<-rep(c(1,0),each=32) b$n<-ifelse(b$rel,b$case,b$control) index<-rep(1:64,b$n) nwt.exp<-b[index,c(1:3,6,7)] nwt.exp$id<-1:4088 @ As we actually do know \texttt{histol} for all patients we can fit the logistic regression model with full sampling to compare with the two-phase analyses <<>>= glm(rel~factor(stage)*factor(histol), family=binomial, data=nwt.exp) @ The second phase sample consists of all patients with unfavorable histology as determined by local institution pathologists, all cases, and a 20\% sample of the remainder. Phase two is thus a stratified random sample without replacement, with strata defined by the interaction of \texttt{instit} and \texttt{rel}. <<>>= dccs2<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,rel)),data=nwt.exp) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs2)) @ Disease stage at the time of surgery is also recorded. It could be used to further stratify the sampling, or, as in this example, to post-stratify. We can analyze the data either pretending that the sampling was stratified or using \texttt{calibrate} to post-stratify the design. <<>>= dccs8<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,stage,rel)),data=nwt.exp) gccs8<-calibrate(dccs2,phase=2,formula=~interaction(instit,stage,rel)) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs8)) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=gccs8)) @ \section*{Case--cohort designs} In the case--cohort design for survival analysis, a $P$\% sample of a cohort is taken at recruitment for the second phase, and all participants who experience the event (cases) are later added to the phase-two sample. Viewing the sampling design as progressing through time in this way, as originally proposed, gives a double sampling design at phase two. It is simpler to view the process \emph{sub specie aeternitatis}, and to note that cases are sampled with probability 1, and controls with probability $P/100$. The subcohort will often be determined retrospectively rather than at recruitment, giving stratified random sampling without replacement, stratified on case status. If the subcohort is determined prospectively we can use the same analysis, post-stratifying rather than stratifying. There have been many analyses proposed for the case--cohort design (Therneau \& Li, 1999). We consider only those that can be expressed as a Horvitz--Thompson estimator for the Cox model. First we load the data and the necessary packages. The version of the NWTS data that includes survival times is not identical to the data set used for case--control analyses above. <<>>= library(survey) library(survival) data(nwtco) ntwco<-subset(nwtco, !is.na(edrel)) @ Again, we fit a model that uses \texttt{histol} for all patients, to compare with the two-phase design <<>>= coxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12),data=nwtco) @ We define a two-phase survey design using simple random superpopulation sampling for the first phase, and sampling without replacement stratified on \texttt{rel} for the second phase. The \texttt{subset} argument specifies that observations are in the phase-two sample if they are in the subcohort or are cases. As before, the data structure is rectangular, but variables measured at phase two may be \texttt{NA} for participants not included at phase two. We compare the result to that given by \texttt{survival::cch} for Lin \& Ying's (1993) approach to the case--cohort design. <<>>= (dcch<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco)) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcch) subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing") @ Barlow (1994) proposes an analysis that ignores the finite population correction at the second phase. This simplifies the standard error estimation, as the design can be expressed as one-phase stratified superpopulation sampling. The standard errors will be somewhat conservative. More data preparation is needed for this analysis as the weights change over time. <<>>= nwtco$eventrec<-rep(0,nrow(nwtco)) nwtco.extra<-subset(nwtco, rel==1) nwtco.extra$eventrec<-1 nwtco.expd<-rbind(subset(nwtco,in.subcohort==1),nwtco.extra) nwtco.expd$stop<-with(nwtco.expd, ifelse(rel & !eventrec, edrel-0.001,edrel)) nwtco.expd$start<-with(nwtco.expd, ifelse(rel & eventrec, edrel-0.001, 0)) nwtco.expd$event<-with(nwtco.expd, ifelse(rel & eventrec, 1, 0)) nwtco.expd$pwts<-ifelse(nwtco.expd$event, 1, 1/with(nwtco,mean(in.subcohort | rel))) @ The analysis corresponds to a cluster-sampled design in which individuals are sampled stratified by subcohort membership and then time periods are sampled stratified by event status. Having individual as the primary sampling unit is necessary for correct standard error calculation. <<>>= (dBarlow<-svydesign(id=~seqno+eventrec, strata=~in.subcohort+rel, data=nwtco.expd, weight=~pwts)) svycoxph(Surv(start,stop,event)~factor(stage)+factor(histol)+I(age/12), design=dBarlow) @ In fact, as the finite population correction is not being used the second stage of the cluster sampling could be ignored. We can also produce the stratified bootstrap standard errors of Wacholder et al (1989), using a replicate weights analysis <<>>= (dWacholder <- as.svrepdesign(dBarlow,type="bootstrap",replicates=500)) svycoxph(Surv(start,stop,event)~factor(stage)+factor(histol)+I(age/12), design=dWacholder) @ \subsection*{Exposure-stratified designs} Borgan et al (2000) propose designs stratified or post-stratified on phase-one variables. The examples at \url{http://faculty.washington.edu/norm/software.html} use a different subcohort sample for this stratified design, so we load the new \texttt{subcohort} variable <<>>= load(system.file("doc","nwtco-subcohort.rda",package="survey")) nwtco$subcohort<-subcohort d_BorganII <- twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(instit,rel)), data=nwtco, subset=~I(rel |subcohort)) (b2<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganII)) @ We can further post-stratify the design on disease stage and age with \texttt{calibrate} <<>>= d_BorganIIps <- calibrate(d_BorganII, phase=2, formula=~age+interaction(instit,rel,stage)) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganIIps) @ \section*{References} Barlow WE (1994). Robust variance estimation for the case-cohort design. \emph{Biometrics} 50: 1064-1072 Borgan \O, Langholz B, Samuelson SO, Goldstein L and Pogoda J (2000). Exposure stratified case-cohort designs, \emph{Lifetime Data Analysis} 6:39-58 Breslow NW and Chatterjee N. (1999) Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. \emph{Applied Statistics} 48:457-68. Lin DY, and Ying Z (1993). Cox regression with incomplete covariate measurements. \emph{Journal of the American Statistical Association} 88: 1341-1349. Therneau TM and Li H., Computing the Cox model for case-cohort designs. \emph{Lifetime Data Analysis} 5:99-112, 1999 Wacholder S, Gail MH, Pee D, and Brookmeyer R (1989) Alternate variance and efficiency calculations for the case-cohort design \emph{Biometrika}, 76, 117-123 \end{document} survey/inst/doc/phase1.pdf0000644000176200001440000036142613517345025015226 0ustar liggesusers%PDF-1.5 % 3 0 obj << /Length 3341 /Filter /FlateDecode >> stream xZI#ŭ<A'F`[ZZ3UTjlA*o#業+*n+heMS5T7k=n9S[=cvum;lxT˟{lxmf {? ~-lݜ/߼ԱZE ZնBL;tC#!Oz%p8^5m-ʶmsmTΩ|^O355,b9WS끗1o!/w{hBZe@Y DIoam ,J OTpd=QLyj56F'-wwGE ~nT4gRxGnla_;k& {Kʨ0M]2X76N@O-OV6ae5FxH/ *iN"@0jb'fE2Z>YT['wOC)upqHՊx?~BrχX:]zcpcl,E~95,iw`ydV}Tߘ.= ۝͖l*FGT~Uކ>B8g.%|3;nER\{ ଐ B\{bc//eeqAVd!QSxVTt z3\XRȻf3$4v{a qB=,԰wCa)1PD=" ~Mp2A}OA v06͛4%`*GAaq=No{͚XEDwCj{U!!]B[! fi"vXTRQ7`lIGзZ7ɻ,IZ8ݥ8p~8Ɛp:{l@3NPI&e S&#A$wef:@_HXP"ὓȳ`T7OuT6H2NRa-b`|S<551xZ VQ둬DS x6`V/u5J`1TtUE^,E~,UeJ>uQ'$A: "n˙r+ P˒CA#c/LxqVHVZ`Szd mo(QQ #H6N|eMm1|[6~;Hkaz72 ᓸPB`emsj$[=6Zm{;MϜɆr6y9aF!%%޻^x:5;2}o{ƺ#XUq94m0\X 5g"Fvs ՂNF,)o} jpu)שy,I'1d8 eE"\ '3:i@Z=E~sx7g,%@`YGоg~Z$@a͗}*4( c7>?L*O#3V9n&'"]є26xz` ݚ2nM?ïi0itIl^vx8f5VRQ<?>~Cm$Ib·`V0="9F !iJ8.H6`H?B>>XHgw h񩋄5**Ϟ||ښO8OߝyP2x)~LM8P*aj ЮF@ͱxH106Խcn`=sqʀwA?^5(sx )^pӣ~5>W꘿95_.A}VFCscL=ӰZ <>Vi4+*N>k3-A(D})}ҥxjt~ۀb#Y[h#\@tMSN 13q\{3#4ZXljU0~p}J ׾ ͷ#G54Cx1̭t)=Bm[eOƺhâty8ڔ^Nͳ}u;Cؘp.K@y7ǒ3ys^gysgN2:iz,S5P!PD]g2xczB'=..~}HP/Di|B YY%8[7rg:RG;]k{)QH<33z[,z+ .KN6V1v," *ں [t4 W};W|5NIAљ%SkQ>$ER{@r~r/%K*mS4&I2QgfǹChI!؋UfD1wIJ2CJutȷ飷\b#Ib]m r׏;.>S VuP>f"K=tPvC%Kn7R;Ѕ4 ,U}@B:؄hF Cj/Pw)[ȯS9bf{6YbW}U!9x2q47ZNoߛj4%BMM endstream endobj 18 0 obj << /Length 1324 /Filter /FlateDecode >> stream xڽYo6_a`g"R؆(Ccx~G$Kv~`Lw<: Ng;Y'K$N;q2:ûSOt00~;>0h hI?K`04ڄl ſ w'EdeJQ"|u$7MH3KS#bwa#hp .$pa.6pTw<1\`|,^/0>5<@rhB9ASO xvؖ?ZNV!87tء>|6 ܖʍCT'EL#1mɱCA<6MΉ:΅[DkӾmVm>Qݮ$ ZoR֔ gJ$AޓIwa(\wsT6nR iwer+v}`jjѬKC̡T)W4 ^juM{Cfsb>3UZn~)$:5,J2/GaJ!Pl@vn]m[[M~lYI$6[+E] etI] aA,c꾲g{أ|@[ 0#Ԅ;#&:kJ VAʿDeR_vWZP6WLۂKٶjKyQ-Sxfx2]eMmdjk2 4LQyk((ȉ%+G(:f):^+D4?3#tw1NZ4Hj`Pį@,Qqx]{Y C੡9iX{ 7fw$!0v}K&6_mc$|gv^G9%zA{=:ڔ4lðGf>rm^fg35Gbvd L)ƕýv·R]B8a9ra AWsUmjU/L tg€1{KKɿR_fc=Ya_ᅊo=j',::vSrX|u=OjlH|_; 1ȱ Y:]^-vN12ځhh'L勻<*5&w?Qwr)YJ4g-ݛ\P'}Ԋֈ+|WLN\e0-ƩXҐAq@|_ endstream endobj 31 0 obj << /Length1 1492 /Length2 6277 /Length3 0 /Length 7280 /Filter /FlateDecode >> stream xڍT6L (%H^w#! 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As with quantiles in unweighted data, this definition only pins down the quantile to an interval between two observations, and a rule is needed to interpolate. As the help for the base R function \texttt{quantile} explains, even before considering sampling weights there are many possible rules. Rules in the \texttt{svyquantile()} function can be divided into three classes \begin{itemize} \item Discrete rules, following types 1 to 3 in \texttt{quantile} \item Continuous rules, following types 4 to 9 in \texttt{quantile} \item A rule proposed by Shah \& Vaish (2006) and used in some versions of \textsf{SUDAAN} \end{itemize} \subsection*{Discrete rules} These are based on the discrete empirical CDF that puts weight proportional to the weight $w_k$ on values $x_k$. $$\hat F(x) = \frac{\sum_i \{x_i\leq x\}w_i}{\sum_i w_i}$$ \paragraph{The mathematical inverse} The mathematical inverse $\hat F^{-1}(p)$ of the CDF is the smallest $x$ such that $F(x)\geq p$. This is rule \texttt{hf1} and \texttt{math} and in equally-weighted data gives the same answer as \texttt{type=1} in \texttt{quantile} \paragraph{The primary-school median} The school definition of the median for an even number of observations is the average of the middle two observations. We extend this to say that the $p$th quantile is $q_{\mathrm{low}}=\hat F^{-1}(p)$ if $\hat F(q_{\mathrm{low}})=p$ and otherwise is the the average of $\hat F^{-1}(p)$ and the next higher observation. This is \texttt{school} and \texttt{hf2} and is the same as \texttt{type=2} in \texttt{quantile}. \paragraph{Nearest even order statistic} The $p$th quantile is whichever of $\hat F^{-1}(p)$ and the next higher observation is at an even-numbered position when the distinct data values are sorted. This is \texttt{hf3} and is the same as \texttt{type=3} in \texttt{quantile}. \subsection*{Continuous rules} These construct the empirical CDF as a piecewise-linear function and read off the quantile. They differ in the choice of points to interpolate. Hyndman \& Fan describe these as interpolating the points $(p_k,x_k)$ where $p_k$ is defined in terms of $k$ and $n$. For weighted use they have been redefined in terms of the cumulative weights $C_k=\sum_{i\leq k} w_i$, the total weight $C_n=\sum w_i$, and the weight $w_k$ on the $k$th observation. \begin{tabular}{lll} {\tt qrule} & Hyndman \& Fan & Weighted\\ \hline {\tt hf4} & $p_k= k/n$ & $p_k = C_k/C_n$\\ {\tt hf5} & $p_k= (k-0.5)/n$ & $p_k = (C_k-w_k)/C_n$\\ {\tt hf6} & $p_k= k/(n+1)$ & $p_k = C_k/(C_n+w_n)$\\ {\tt hf7} & $p_k= (k-1)/(n-1)$ & $p_k = C_{k-1}/C_{n-1}$\\ {\tt hf8} & $p_k= (k-1/3)/(n+2/3)$ & $p_k = (C_k-w_k/3)/(C_n+w_n/3)$\\ {\tt hf9} & $p_k= (k-3/8)/(n+1/4)$ & $p_k = (C_k-3w_k./8)/(C_n+w_n/4)$\\ \hline \end{tabular} \subsection*{Shah \& Vaish} This rule is related to {\tt hf6}, but it is discrete and more complicated. First, define $w^*_i=w_in/C_n$, so that $w^*_i$ sum to the sample size rather than the population size, and $C^*k$ as partial sums of $w^*_k$. Now define the estimated CDF by $$\hat F(x_k) =\frac{1}{n+1}\left(C^*_k+1/2-w_k/2 \right)$$ and take $\hat F^{-1}(p)$ as the $p$th quantile. \subsection*{Other options} It would be possible to redefine all the continuous estimators in terms of $w^*$, so that type 8, for example, would use $$p_k = (C^*_k-1/3)/(C^*_n+2/3)$$ Or a compromise, eg using $w^*_k$ in the numerator and numbers in the denominator, such as $$p_k = (C^*_k-w^*_k/3)/(C^*_n+2/3).$$ Comparing these would be \strike{a worthwhile}\dots\strike{an interesting}... a research question for simulation. \end{document} survey/inst/doc/nwtco-subcohort.rda0000644000176200001440000000166513517345025017176 0ustar liggesusersY9A,]x Xkx/@ƠVUfFDF{22./߿rbwϻo?>}~o~5z\պި}ݵe +sZ}=}#|vd߳+nh{WשЕv*v+}j|OٸE! 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################################################### ### code chunk number 3: precalibrated.Rnw:34-36 ################################################### cal_dclus1<-calibrate(dclus1, formula=~1, population=sum(weights(dclus1))) svytotal(~one,cal_dclus1) ################################################### ### code chunk number 4: precalibrated.Rnw:40-41 ################################################### summary(weights(cal_dclus1)/weights(dclus1)) ################################################### ### code chunk number 5: precalibrated.Rnw:45-50 ################################################### precal_dclus1<-svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc, calibrate.formula=~1) precal_dclus1<-update(precal_dclus1, one=rep(1,nrow(dclus1))) svytotal(~one,precal_dclus1) ################################################### ### code chunk number 6: precalibrated.Rnw:55-64 ################################################### (enroll_t<-svytotal(~enroll, dclus1)) (enroll_m<-svymean(~enroll, dclus1)) SE(enroll_m) SE(enroll_t)/6194 (cenroll_t<-svytotal(~enroll, precal_dclus1)) (cenroll_m<-svymean(~enroll, precal_dclus1)) SE(cenroll_m) SE(cenroll_t)/6194 survey/inst/doc/domain.R0000644000176200001440000000441614576431426014744 0ustar liggesusers### R code from vignette source 'domain.Rnw' ################################################### ### code chunk number 1: domain.Rnw:29-34 ################################################### library(survey) data(fpc) dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) dsub<-subset(dfpc,x>4) svymean(~x,design=dsub) ################################################### ### code chunk number 2: domain.Rnw:41-42 ################################################### svyby(~x,~I(x>4),design=dfpc, svymean) ################################################### ### code chunk number 3: domain.Rnw:49-50 ################################################### summary(svyglm(x~I(x>4)+0,design=dfpc)) ################################################### ### code chunk number 4: domain.Rnw:57-58 ################################################### svyratio(~I(x*(x>4)),~as.numeric(x>4), dfpc) ################################################### ### code chunk number 5: domain.Rnw:76-84 ################################################### data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) gclus1 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069)) svymean(~api00, subset(gclus1, comp.imp=="Yes")) svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), gclus1) summary(svyglm(api00~comp.imp-1, gclus1)) ################################################### ### code chunk number 6: domain.Rnw:88-94 ################################################### data(mu284) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) svymean(~y1, subset(dmu284,y1>40)) svyratio(~I(y1*(y1>40)),~as.numeric(y1>40),dmu284) summary(svyglm(y1~I(y1>40)+0,dmu284)) ################################################### ### code chunk number 7: domain.Rnw:100-108 ################################################### library("survival") data(nwtco) nwtco$incc2<-as.logical(with(nwtco, ifelse(rel | instit==2,1,rbinom(nrow(nwtco),1,.1)))) dccs8<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2) svymean(~rel, subset(dccs8,age>36)) svyratio(~I(rel*as.numeric(age>36)), ~as.numeric(age>36), dccs8) summary(svyglm(rel~I(age>36)+0, dccs8)) survey/inst/doc/phase1.R0000644000176200001440000000367313517345025014653 0ustar liggesusers### R code from vignette source 'phase1.Rnw' ################################################### ### code chunk number 1: phase1.Rnw:82-105 ################################################### rei<-read.table(textConnection( " id N n.a h n.ah n.h sub y 1 1 300 20 1 12 5 TRUE 1 2 2 300 20 1 12 5 TRUE 2 3 3 300 20 1 12 5 TRUE 3 4 4 300 20 1 12 5 TRUE 4 5 5 300 20 1 12 5 TRUE 5 6 6 300 20 1 12 5 FALSE NA 7 7 300 20 1 12 5 FALSE NA 8 8 300 20 1 12 5 FALSE NA 9 9 300 20 1 12 5 FALSE NA 10 10 300 20 1 12 5 FALSE NA 11 11 300 20 1 12 5 FALSE NA 12 12 300 20 1 12 5 FALSE NA 13 13 300 20 2 8 3 TRUE 6 14 14 300 20 2 8 3 TRUE 7 15 15 300 20 2 8 3 TRUE 8 16 16 300 20 2 8 3 FALSE NA 17 17 300 20 2 8 3 FALSE NA 18 18 300 20 2 8 3 FALSE NA 19 19 300 20 2 8 3 FALSE NA 20 20 300 20 2 8 3 FALSE NA "), header=TRUE) ################################################### ### code chunk number 2: phase1.Rnw:109-113 ################################################### library(survey) des.rei <- twophase(id=list(~id,~id), strata=list(NULL,~h), fpc=list(~N,NULL), subset=~sub, data=rei) tot<- svytotal(~y, des.rei) ################################################### ### code chunk number 3: phase1.Rnw:117-124 ################################################### rei$w.ah <- rei$n.ah / rei$n.a a.rei <- aggregate(rei, by=list(rei$h), mean, na.rm=TRUE) a.rei$S.ysh <- tapply(rei$y, rei$h, var, na.rm=TRUE) a.rei$y.u <- sum(a.rei$w.ah * a.rei$y) a.rei$f<-with(a.rei, n.a/N) a.rei$delta.h<-with(a.rei, (1/n.h)*(n.a-n.ah)/(n.a-1)) Vphase1<-with(a.rei, sum(N*N*((1-f)/n.a)*( w.ah*(1-delta.h)*S.ysh+ ((n.a)/(n.a-1))*w.ah*(y-y.u)^2))) ################################################### ### code chunk number 4: phase1.Rnw:128-130 ################################################### Vphase1 attr(vcov(tot),"phases")$phase1 survey/inst/doc/survey-sae.html.asis0000644000176200001440000000011314576426776017303 0ustar liggesusers%\VignetteIndexEntry{Small area estimation} %\VignetteEngine{R.rsp::asis} survey/inst/doc/survey.Rnw0000644000176200001440000000670613517345006015373 0ustar liggesusers\documentclass{article} \usepackage{url} %\VignetteIndexEntry{A survey analysis example} \usepackage{Sweave} \author{Thomas Lumley} \title{A survey analysis example} \begin{document} \maketitle This document provides a simple example analysis of a survey data set, a subsample from the California Academic Performance Index, an annual set of tests used to evaluate California schools. The API website, including the original data files are at \url{http://api.cde.ca.gov}. The subsample was generated as a teaching example by Academic Technology Services at UCLA and was obtained from \url{http://www.ats.ucla.edu/stat/stata/Library/svy_survey.htm}. We have a cluster sample in which 15 school districts were sampled and then all schools in each district. This is in the data frame \texttt{apiclus1}, loaded with \texttt{data(api)}. The two-stage sample is defined by the sampling unit (\texttt{dnum}) and the population size(\texttt{fpc}). Sampling weights are computed from the population sizes, but could be provided separately. <<>>= library(survey) data(api) dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) @ The \texttt{svydesign} function returns an object containing the survey data and metadata. <<>>= summary(dclus1) @ We can compute summary statistics to estimate the mean, median, and quartiles of the Academic Performance Index in the year 2000, the number of elementary, middle, and high schools in the state, the total number of students, and the proportion who took the test. Each function takes a formula object describing the variables and a survey design object containing the data. <<>>= svymean(~api00, dclus1) svyquantile(~api00, dclus1, quantile=c(0.25,0.5,0.75), ci=TRUE) svytotal(~stype, dclus1) svytotal(~enroll, dclus1) svyratio(~api.stu,~enroll, dclus1) @ The ordinary R subsetting functions \verb'[' and \texttt{subset} work correctly on these survey objects, carrying along the metadata needed for valid standard errors. Here we compute the proportion of high school students who took the test <<>>= svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H")) @ The warnings referred to in the output occured because several school districts have only one high school sampled, making the second stage standard error estimation unreliable. Specifying a large number of variables is made easier by the \texttt{make.formula} function <<>>= vars<-names(apiclus1)[c(12:13,16:23,27:37)] svymean(make.formula(vars),dclus1,na.rm=TRUE) @ Summary statistics for subsets can also be computed with \texttt{svyby}. Here we compute the average proportion of ``English language learners'' and of students eligible for subsidized school meals for elementary, middle, and high schools <<>>= svyby(~ell+meals, ~stype, design=dclus1, svymean) @ Regression models show that these socieconomic variables predict API score and whether the school achieved its API target <<>>= regmodel <- svyglm(api00~ell+meals,design=dclus1) logitmodel <- svyglm(I(sch.wide=="Yes")~ell+meals, design=dclus1, family=quasibinomial()) summary(regmodel) summary(logitmodel) @ We can calibrate the sampling using the statewide total for the previous year's API <<>>= gclus1 <- calibrate(dclus1, formula=~api99, population=c(6194, 3914069)) @ which improves estimation of some quantities <<>>= svymean(~api00, gclus1) svyquantile(~api00, gclus1, quantile=c(0.25,0.5,0.75), ci=TRUE) svytotal(~stype, gclus1) svytotal(~enroll, gclus1) svyratio(~api.stu,~enroll, gclus1) @ \end{document} survey/inst/doc/pps.R0000644000176200001440000000357214576431431014275 0ustar liggesusers### R code from vignette source 'pps.Rnw' ################################################### ### code chunk number 1: pps.Rnw:57-61 ################################################### library(survey) data(election) summary(election$p) summary(election_pps$p) ################################################### ### code chunk number 2: pps.Rnw:65-77 ################################################### ## Hajek type dpps_br<- svydesign(id=~1, fpc=~p, data=election_pps, pps="brewer") ## Horvitz-Thompson type dpps_ov<- svydesign(id=~1, fpc=~p, data=election_pps, pps="overton") dpps_hr<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40)) dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR()) dpps_ht<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob)) ## Yates-Grundy type dpps_yg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG") dpps_hryg<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG") ## The with-replacement approximation dppswr <-svydesign(id=~1, probs=~p, data=election_pps) ################################################### ### code chunk number 3: pps.Rnw:81-82 ################################################### show(image(dpps_ht)) ################################################### ### code chunk number 4: pps.Rnw:84-85 ################################################### show(image(dpps_ov)) ################################################### ### code chunk number 5: pps.Rnw:91-99 ################################################### svytotal(~Bush+Kerry+Nader, dpps_ht) svytotal(~Bush+Kerry+Nader, dpps_yg) svytotal(~Bush+Kerry+Nader, dpps_hr) svytotal(~Bush+Kerry+Nader, dpps_hryg) svytotal(~Bush+Kerry+Nader, dpps_hr1) svytotal(~Bush+Kerry+Nader, dpps_br) svytotal(~Bush+Kerry+Nader, dpps_ov) svytotal(~Bush+Kerry+Nader, dppswr) survey/inst/doc/survey-sae.html0000644000176200001440000121626014576431424016344 0ustar liggesusers Area level and unit level models for estimating small area means

Area level and unit level models for estimating small area means

In this vignette, we demonstrate the use of basic area level models for small area estimation, as originally implemented in SUMMER and adapted for use in survey. The models implemented in survey include common small area estimation models, as outlined below. A in-depth review of our approach to model-based small area estimation is provided by Wakefield, Okonek, and Pedersen (2020).

Preliminary

First, we load the necessary packages and data. The required package INLA is not available via a standard repository, so we include code for installation if it is not found. The survey package will be used to generate direct estimates, while dplyr and tidyr will be used for data manipulation.

library(survey)
if (!isTRUE(requireNamespace("INLA", quietly = TRUE))) {
  install.packages("INLA", repos=c(getOption("repos"), 
                                   INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
}

Area level models

Artificial poverty rate example

In their vignette for the sae package, Molina and Marhuenda (2015) generate an artificial dataset on income and other related variables to illustrate the use of area level models. In this example, the objective is to estimate prevalence of poverty in Spanish counties.

data("incomedata", package="sae")
data("sizeprov", package="sae")
data("sizeprovedu", package="sae")
povertyline <- 0.6 * median(incomedata$income) # 6557.143
incomedata$in_poverty <- as.integer(incomedata$income < povertyline)

The incomedata data frame contains information on 17199 observations of individuals in 52 Spanish provinces. Income values and sampling weights are provided for each individual along with covariate information including age group and education level. Molina and Marhuenda (2015) define the poverty line and calculate an indicator variable (which we name in_poverty) with value 1 if the corresponding income value is below the poverty line and 0 otherwise.

Direct estimation with sae

Before considering model-based methods for small area estimation, we compute direct weighted estimators for the desired small area means. The Horvitz-Thompson estimator of domain means is given by

(1/Ni)∑j ∈ Siwjyj

where Ni is the population size of domain i, Si is the set of sampled observations in domain i, wj is the sampling weight for unit j, and yj is the observation for unit j, for all j ∈ Si. The sae::direct function also estimates standard deviation and coefficient of variation for each domain. Note that Ni is assumed known and is provided in the data frame sizeprov. The domains of interest are identified via the provlab variable.

We can use the survey::svyby function to compute the Horvitz-Thompson estimates:

incomedata$pop <- sum(sizeprov$Nd[match(incomedata$provlab, sizeprov$provlab)])
design <- survey::svydesign(ids = ~1, weights = ~weight,
                            data = incomedata, fpc = ~pop)

# estimate area totals
svy.DIR <- survey::svyby(~in_poverty, ~provlab, design, svytotal)

# calculate corresponding area mean estimates
svy.DIR$prov_pop <- sizeprov$Nd[match(svy.DIR$provlab, sizeprov$provlab)]
svy.DIR$Domain <-svy.DIR$provlab 
svy.DIR$Direct = svy.DIR$in_poverty/svy.DIR$prov_pop
svy.DIR$SD= svy.DIR$se/svy.DIR$prov_pop

Basic area level model

The basic area level model, also called the Fay-Herriot model, treats direct estimates of small area quantities as response data and explicitly models differences between areas using covariate information and random effects (Fay and Herriot 1979). The Fay-Herriot model can be viewed as a two-stage model: in the first stage, a sampling model represents the sampling variability of a direct estimator and in the second stage, a linking model describes the between area differences in small area quantities.

Sampling model:

Let θ̂iDIR be a direct estimator of an area level mean or total θi. The sampling model treats θ̂iDIR as a noisy observation of the true finite population quantity θi:

θ̂iDIR = θi + ϵi;   ϵi  ∼ind  N(0,Vi),   i = 1, …, M

where Vi is the known sampling variance of the direct estimator θ̂iDIR.

Linking model:

θi = xiTβ + ui,   ui  ∼ind  N(0,σu2)   i = 1, …, M,

where σu2 (between-area residual variance) is estimated. In this basic Fay-Herriot model, the area-specific random effects ui are assumed to be independent and identically distributed (IID) between areas.

Below, we provide a quantile-quantile plot comparing the direct estimates to a Gaussian distribution. Here the observed quantiles align well with those from a Gaussian distribution, which lends some support to the basic IID model.

par(pty = "s")
mu.DIR <- mean(svy.DIR$Direct)
sd.DIR <- sd(svy.DIR$Direct)
qqnorm((svy.DIR$Direct - mu.DIR) / sd.DIR, main = "")
abline(0, 1, col = "red")

The svysmoothArea function adopts a Bayesian approach to inference using models such as the basic area level model, carrying out computation via the INLA package. The svysmoothArea function computes direct estimates and then produces smoothed estimates using a Bayesian Fay-Herriot model. The main arguments of interest are:

  • formula: Describing the response variable and any area-level covariates
  • domain A one-sided formula with the variable containing domain labels on the right. The domain labels variable should be contained in the dataset used to generate the design.
  • design: A survey.design object containing survey data and specifying the survey design.

In addition, other commonly used optional arguments include:

  • adj.mat: Optional adjacency matrix if a spatial smoothing model is desired.
  • transform: If "logit" is specified, a logit transform will be applied to the direct estimates and an appropriate transformation will be applied to the estimated sampling variances before smoothing.
  • direct.est: The direct estimates may be specified directly and smoothing will be applied directly to these user-provided estimates.
  • X.domain: Data frame of area level covariates.
  • domain.size: Data frame of domain sizes used for computing direct estimates if domain sizes are known.

Other optional arguments can be specified to change the priors and are described further in the documentation.

For the artificial poverty rate example, we fit the Fay-Herriot model and obtain the following smoothed estimates.

# specify known domain sizes
domain.size <- sizeprov[, c("provlab", "Nd")]
colnames(domain.size)[2] <- "size"

# fit model and obtain svysae object
svy.FH <- svysmoothArea(formula = in_poverty~1,
                     domain = ~provlab,
                     design = design,
                     domain.size = domain.size, 
                     return.samples = T)
svy.FH.table <- data.frame(
  Domain = svy.DIR$Domain,
  Median = svy.FH$iid.model.est$median,
  SE = sqrt(svy.FH$iid.model.est$var)
)
head(svy.FH.table)
##     Domain     Median         SE
## 1    Alava 0.23394447 0.03989036
## 2 Albacete 0.15516988 0.02843149
## 3 Alicante 0.20488000 0.02071508
## 4  Almeria 0.23808371 0.03515380
## 5    Avila 0.07854126 0.02392518
## 6  Badajoz 0.20805100 0.02200545

Using the plot() method, we can obtain a comparison of the direct estimates (with corresponding frequentist 95% confidence intervals) and smoothed estimates (with Bayesian 95% credible intervals).

# plot comparison of direct and smoothed estimates
plot(svy.FH)

The SUMMER package includes functions to generate additional diagnostic plots based on samples from the model posterior. The SUMMER::compareEstimates() function generates a heatmap of the posterior pairwise probabilities of one area’s mean exceeding another’s mean.

Spatial area level model

The svysmoothArea function also allows the use of a model with spatially correlated area effects, where the default implementation assumes a BYM2 model for u (Riebler et al. 2016).

In particular, under the BYM2 model, we assume u is composed of an unstructured term u1′ and a spatially structured term u2*′:

Here, u1′ ∼ N(0,I) is a vector of iid Gaussian random area effects and assume an intrinsic conditional autoregressive (ICAR) Gaussian prior for u2*′. Under this parameterization, σu2 represents the marginal variance of u and ϕ represents the proportion of variation assigned to the spatial term.

BRFSS diabetes rates

Below, we provide an example using data from the Behavioral Risk Factor Surveillance System (BRFSS).

data(BRFSS)
data(KingCounty)
BRFSS <- subset(BRFSS, !is.na(BRFSS$diab2))
BRFSS <- subset(BRFSS, !is.na(BRFSS$hracode))
head(BRFSS)
##   age pracex       educau zipcode    sex street1 street2      seqno year
## 1  30  White college grad   98001   male      NA      NA 2009000041 2009
## 2  26  White college grad   98107 female      NA      NA 2009000309 2009
## 3  33  Black college grad   98133   male      NA      NA 2009000404 2009
## 4  25  White some college   98058   male      NA      NA 2009000531 2009
## 5  23  White some college   98102   male      NA      NA 2009000675 2009
## 6  19  Asian some college   98106   male      NA      NA 2009000694 2009
##   hispanic mracex strata             hracode tract rwt_llcp genhlth2 fmd obese
## 1 non-Hisp  White  53019        Auburn-North    NA 2107.463        0   0     0
## 2 non-Hisp  White  53019             Ballard    NA 2197.322        0   1     0
## 3 non-Hisp  Black  53019          NW Seattle    NA 3086.511        0   0     0
## 4 non-Hisp  White  53019        Renton-South    NA 3184.740        1   1     1
## 5 non-Hisp  White  53019 Capitol Hill/E.lake    NA 3184.740        0   0     0
## 6 non-Hisp  Asian  53019      North Highline    NA 4391.304        0   0     0
##   smoker1 diab2 aceindx2 zipout streetx ethn age4 ctmiss
## 1       0     0       NA  98001       0    1    3      1
## 2       0     0       NA  98107       0    1    3      1
## 3       0     0       NA  98133       0    2    3      1
## 4       0     0       NA  98058       0    1    3      1
## 5       0     0       NA  98102       0    1    4      1
## 6       0     0       NA  98106       0    3    4      1
mat <- SUMMER::getAmat(KingCounty, KingCounty$HRA2010v2_)
design <- svydesign(ids = ~1, weights = ~rwt_llcp,
                    strata = ~strata, data = BRFSS)
direct <- svyby(~diab2, ~hracode, design, svymean)

Below, we fit two versions of the spatial area level model in SUMMER. For this example, we apply the area level model to the logit-transformed direct estimates (as specified via the transform = "logit" argument). In other words, we use the following model:

logit(θ̂iDIR) = logit(θi) + ϵi;   ϵi  ∼ind  N(0, V(logit(θi)),   i = 1, …, M where V(logit(θi)) ≈ Vi/θi2 is the sampling variance of θ̂iDIR. The linking model is then logit(θi) = xiTβ + ui,   ui  ∼ind  N(0,σu2)   i = 1, …, M,

If we change pc.u and pc.alpha from the default value u = 1, α = 0.01 to u = 0.1, α = 0.01, we assign more prior mass on smaller variance of the random effects, inducing more smoothing. Note that in this example, we include no covariates to avoid having to introduce additional data.

svy.brfss <- svysmoothArea(diab2~1, domain= ~hracode,
                        design = design,
                        transform = "logit",
                        adj.mat = mat, level = 0.95,
                        pc.u = 0.1, pc.alpha = 0.01)

The SUMMER::mapPlot() function can be used to visualize the posterior median estimates and uncertainty estimates.

Unit level models

The nested error model, introduced by Battese, Harter, and Fuller (1988), uses auxiliary data at the unit level.

Nested error model: yij = xijTβ + ui + ϵij,  ui  ∼ind  N(0,σu2),  ϵij  ∼ind  N(0,σϵ2)

Here ui are area random effects and ϵij are unit level errors.

This model assumes there is no sample selection bias.

Corn and Soy Production

The cornsoybean and cornsoybeanmeans datasets contain info on corn and soy beans production in 12 Iowa counties (Battese, Harter, and Fuller 1988). The objective here is use satellite imagery of the number of pixels assigned to corn and soy to estimate the hectares grown of corn.

  • SampSegments: sample size.
  • PopnSegments: population size.
  • MeanCornPixPerSeg: county mean of the number of corn pixels (satellite imagery).
  • MeanSoyBeansPixPerSeg county mean of the number of soy beans (satellite imagery) pixels.

The variables MeanCornPixPerSeg and MeanSoyBeansPixPerSeg provide the known county means of the auxiliary variables.

We load the sample data:

data("cornsoybean", package="sae")

Next, we load the population auxiliary information:

data("cornsoybeanmeans", package="sae")
Xmean <-
  data.frame(cornsoybeanmeans[, c("CountyIndex",
                                  "MeanCornPixPerSeg",
                                  "MeanSoyBeansPixPerSeg")])
Popn <-
  data.frame(cornsoybeanmeans[, c("CountyIndex",
                                  "PopnSegments")])

The smoothUnit function provides the ability to fit unit level models with unit level covariates for Gaussian and binomial response variables. Below we use the is.unit argument to specify a unit level model and then provide the column names of the unit level covariates in X.unit. Finally, the X argument provides the area level means of each covariate for use when generating predictions. Note that we specify a relatively flat prior on the variance of the area-specific random effect (the arguments pc.u = 100, pc.alpha = 0.01 specify a penalized complexity prior such that P(σu>100) = 0.01 where σu is the standard deviation of the area-specific random effects).

cornsoybean$id <- 1:dim(cornsoybean)[1]
Xsummer <- Xmean
colnames(Xsummer) = c("County", "CornPix", "SoyBeansPix")
des0 <- svydesign(ids = ~1, data = cornsoybean)
svy.bhf.unit <- smoothUnit(formula = CornHec ~ CornPix + SoyBeansPix,
                           family = "gaussian",
                           domain = ~County,
                           design = des0, X.pop = Xsummer,
                           pc.u = 1000, pc.alpha = 0.01, level = 0.95)
summary(svy.bhf.unit)
## Call:
## smoothUnit(formula = CornHec ~ CornPix + SoyBeansPix, domain = ~County, 
##     design = des0, family = "gaussian", X.pop = Xsummer, pc.u = 1000, 
##     pc.alpha = 0.01, level = 0.95)
## 
## Methods used: direct.est, iid.model.est
## 
## direct.est 
##    domain     mean   median        var     lower    upper method
## 1       1 165.7600 165.7600   0.000000 165.76000 165.7600 Direct
## 2       2  96.3200  96.3200   0.000000  96.32000  96.3200 Direct
## 3       3  76.0800  76.0800   0.000000  76.08000  76.0800 Direct
## 4       4 150.8900 150.8900 610.238739 102.47299 199.3070 Direct
## 5       5 158.6233 158.6233   7.430247 153.28077 163.9659 Direct
## 6       6 102.5233 102.5233 430.314438  61.86580 143.1809 Direct
## 7       7 112.7733 112.7733 213.115805  84.16083 141.3858 Direct
## 8       8 144.2967 144.2967 665.975547  93.71685 194.8765 Direct
## 9       9 117.5950 117.5950  87.414716  99.27015 135.9198 Direct
## 10     10 109.3820 109.3820  40.331533  96.93483 121.8292 Direct
## 11     11 110.2520 110.2520  24.148695 100.62048 119.8835 Direct
## 12     12 114.8100 114.8100 178.260890  88.64166 140.9783 Direct
## 
## iid.model.est 
##    domain     mean   median       var     lower    upper                method
## 1       1 123.7876 123.2013  86.06266 108.32013 142.2333 Unit level model: IID
## 2       2 124.2452 124.7868  97.86504 104.93144 142.9875 Unit level model: IID
## 3       3 110.3233 111.5757 102.53029  88.20383 126.9989 Unit level model: IID
## 4       4 113.6742 114.1331  71.81986  96.38425 127.9440 Unit level model: IID
## 5       5 140.1470 139.9905  75.00325 123.76649 155.1843 Unit level model: IID
## 6       6 110.0132 109.4203  57.25731  95.71681 124.7636 Unit level model: IID
## 7       7 115.9491 116.0076  57.80478 101.21077 129.8307 Unit level model: IID
## 8       8 122.6541 122.4973  55.39583 108.86131 138.6098 Unit level model: IID
## 9       9 112.6702 112.1757  48.62457  98.12156 127.7731 Unit level model: IID
## 10     10 123.8395 123.8251  36.20162 111.43605 135.8105 Unit level model: IID
## 11     11 111.2287 111.9254  44.52737  96.51444 123.5814 Unit level model: IID
## 12     12 131.3522 131.1526  35.26051 120.12848 142.9651 Unit level model: IID

References

Battese, George E., Rachel M. Harter, and Wayne A. Fuller. 1988. “An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data.” Journal of the American Statistical Association 83 (401): 28–36. https://doi.org/10.2307/2288915.
Fay, Robert E., and Roger A. Herriot. 1979. “Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data.” Journal of the American Statistical Association 74 (366a): 269–77. https://doi.org/10.1080/01621459.1979.10482505.
Molina, Isabel, and Yolanda Marhuenda. 2015. “Sae: An R Package for Small Area Estimation.” The R Journal 7 (1): 81–98. https://journal.r-project.org/archive/2015/RJ-2015-007/index.html.
Riebler, Andrea, Sigrunn H Sørbye, Daniel Simpson, and Håvard Rue. 2016. “An Intuitive Bayesian Spatial Model for Disease Mapping That Accounts for Scaling.” Statistical Methods in Medical Research 25 (4): 1145–65. https://doi.org/10.1177/0962280216660421.
Wakefield, Jonathan, Taylor Okonek, and Jon Pedersen. 2020. “Small Area Estimation for Disease Prevalence Mapping.” International Statistical Review 88 (2): 398–418. https://doi.org/10.1111/insr.12400.
survey/inst/doc/epi.R0000644000176200001440000001157314576431430014247 0ustar liggesusers### R code from vignette source 'epi.Rnw' ################################################### ### code chunk number 1: epi.Rnw:45-61 ################################################### library(survey) load(system.file("doc","nwts.rda",package="survey")) nwtsnb<-nwts nwtsnb$case<-nwts$case-nwtsb$case nwtsnb$control<-nwts$control-nwtsb$control a<-rbind(nwtsb,nwtsnb) a$in.ccs<-rep(c(TRUE,FALSE),each=16) b<-rbind(a,a) b$rel<-rep(c(1,0),each=32) b$n<-ifelse(b$rel,b$case,b$control) index<-rep(1:64,b$n) nwt.exp<-b[index,c(1:3,6,7)] nwt.exp$id<-1:4088 ################################################### ### code chunk number 2: epi.Rnw:65-66 ################################################### glm(rel~factor(stage)*factor(histol), family=binomial, data=nwt.exp) ################################################### ### code chunk number 3: epi.Rnw:75-79 ################################################### dccs2<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,rel)),data=nwt.exp) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs2)) ################################################### ### code chunk number 4: epi.Rnw:88-94 ################################################### dccs8<-twophase(id=list(~id,~id),subset=~in.ccs, strata=list(NULL,~interaction(instit,stage,rel)),data=nwt.exp) gccs8<-calibrate(dccs2,phase=2,formula=~interaction(instit,stage,rel)) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs8)) summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=gccs8)) ################################################### ### code chunk number 5: epi.Rnw:122-126 ################################################### library(survey) library(survival) data(nwtco) ntwco<-subset(nwtco, !is.na(edrel)) ################################################### ### code chunk number 6: epi.Rnw:130-131 ################################################### coxph(Surv(edrel, rel)~factor(stage)+factor(histol)+I(age/12),data=nwtco) ################################################### ### code chunk number 7: epi.Rnw:143-155 ################################################### (dcch<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel), subset=~I(in.subcohort | rel), data=nwtco)) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=dcch) subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] cch(Surv(edrel, rel) ~ factor(stage) + factor(histol) + I(age/12), data =ccoh.data, subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="LinYing") ################################################### ### code chunk number 8: epi.Rnw:165-176 ################################################### nwtco$eventrec<-rep(0,nrow(nwtco)) nwtco.extra<-subset(nwtco, rel==1) nwtco.extra$eventrec<-1 nwtco.expd<-rbind(subset(nwtco,in.subcohort==1),nwtco.extra) nwtco.expd$stop<-with(nwtco.expd, ifelse(rel & !eventrec, edrel-0.001,edrel)) nwtco.expd$start<-with(nwtco.expd, ifelse(rel & eventrec, edrel-0.001, 0)) nwtco.expd$event<-with(nwtco.expd, ifelse(rel & eventrec, 1, 0)) nwtco.expd$pwts<-ifelse(nwtco.expd$event, 1, 1/with(nwtco,mean(in.subcohort | rel))) ################################################### ### code chunk number 9: epi.Rnw:185-189 ################################################### (dBarlow<-svydesign(id=~seqno+eventrec, strata=~in.subcohort+rel, data=nwtco.expd, weight=~pwts)) svycoxph(Surv(start,stop,event)~factor(stage)+factor(histol)+I(age/12), design=dBarlow) ################################################### ### code chunk number 10: epi.Rnw:194-197 ################################################### (dWacholder <- as.svrepdesign(dBarlow,type="bootstrap",replicates=500)) svycoxph(Surv(start,stop,event)~factor(stage)+factor(histol)+I(age/12), design=dWacholder) ################################################### ### code chunk number 11: epi.Rnw:209-217 ################################################### load(system.file("doc","nwtco-subcohort.rda",package="survey")) nwtco$subcohort<-subcohort d_BorganII <- twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(instit,rel)), data=nwtco, subset=~I(rel |subcohort)) (b2<-svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganII)) ################################################### ### code chunk number 12: epi.Rnw:222-225 ################################################### d_BorganIIps <- calibrate(d_BorganII, phase=2, formula=~age+interaction(instit,rel,stage)) svycoxph(Surv(edrel,rel)~factor(stage)+factor(histol)+I(age/12), design=d_BorganIIps) survey/inst/doc/domain.pdf0000644000176200001440000045232514576431426015322 0ustar liggesusers%PDF-1.5 % 3 0 obj << /Length 1763 /Filter /FlateDecode >> stream xڍˎ6Q 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It is standard practice to ignore this fact and treat the weights as if they were sampling weights. An alternative approach is to calibrate the weights again in R. This might seem impossible: calibration needs the population totals. But we know the population totals, because the effect of calibration is precisely that the estimated population totals match the true population totals. From version 4.2, the \texttt{svydesign} function has an option \texttt{calibrate.formula} to specify (as a model formula) the variables that the weights are already calibrated on. The weights will be recalibrated using this formula, to population totals estimated from the sample. The weights will not change --- they were already calibrated --- but information will be added to the survey design object to describe the calibration constraints and standard errors of estimates will change. Consider, for example, the \texttt{apiclus1} example. This is a cluster sample of 15 school districts in California <<>>= library(survey) data(api) dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc) @ The sampling weights \texttt{pw} are already calibrated to sum to the known number of schools in the population, 6194, but R does not know this: <<>>= sum(weights(dclus1)) dim(apipop) dclus1<-update(dclus1, one=rep(1,nrow(dclus1))) svytotal(~one,dclus1) @ The standard error should be zero, because the calibration procedure ensures that the the estimated total is exactly 6194. We could use \texttt{calibrate()} on the design object <<>>= cal_dclus1<-calibrate(dclus1, formula=~1, population=sum(weights(dclus1))) svytotal(~one,cal_dclus1) @ The standard errors have changed, but the weights haven't <<>>= summary(weights(cal_dclus1)/weights(dclus1)) @ With the new option to \texttt{svydesign} we can do the same thing when the object is created <<>>= precal_dclus1<-svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc, calibrate.formula=~1) precal_dclus1<-update(precal_dclus1, one=rep(1,nrow(dclus1))) svytotal(~one,precal_dclus1) @ Calibrating to the population size simplifies the standard error relationship between mean and total <<>>= (enroll_t<-svytotal(~enroll, dclus1)) (enroll_m<-svymean(~enroll, dclus1)) SE(enroll_m) SE(enroll_t)/6194 (cenroll_t<-svytotal(~enroll, precal_dclus1)) (cenroll_m<-svymean(~enroll, precal_dclus1)) SE(cenroll_m) SE(cenroll_t)/6194 @ Because calibration in this way changes the standard errors but not the point estimates, it's critical that you {\em only use it when the weights are in fact already calibrated}. If not, the standard errors will be wrong. In particular, it is not valid to take a subset of a data set with calibrated weights and then pretend the subset was also calibrated on the same variables. \end{document} survey/inst/doc/domain.Rnw0000644000176200001440000000771613517345006015307 0ustar liggesusers\documentclass{article} \usepackage{url} %\VignetteIndexEntry{Estimates in subpopulations} \usepackage{Sweave} \author{Thomas Lumley} \title{Estimates in subpopulations.} \begin{document} \maketitle Estimating a mean or total in a subpopulation (domain) from a survey, eg the mean blood pressure in women, is not done simply by taking the subset of data in that subpopulation and pretending it is a new survey. This approach would give correct point estimates but incorrect standard errors. The standard way to derive domain means is as ratio estimators. I think it is easier to derive them as regression coefficients. These derivations are not important for R users, since subset operations on survey design objects automatically do the necessary adjustments, but they may be of interest. The various ways of constructing domain mean estimators are useful in quality control for the survey package, and some of the examples here are taken from \texttt{survey/tests/domain.R}. Suppose that in the artificial \texttt{fpc} data set we want to estimate the mean of \texttt{x} when \texttt{x>4}. <<>>= library(survey) data(fpc) dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) dsub<-subset(dfpc,x>4) svymean(~x,design=dsub) @ The \texttt{subset} function constructs a survey design object with information about this subpopulation and \texttt{svymean} computes the mean. The same operation can be done for a set of subpopulations with \texttt{svyby}. <<>>= svyby(~x,~I(x>4),design=dfpc, svymean) @ In a regression model with a binary covariate $Z$ and no intercept, there are two coefficients that estimate the mean of the outcome variable in the subpopulations with $Z=0$ and $Z=1$, so we can construct the domain mean estimator by regression. <<>>= summary(svyglm(x~I(x>4)+0,design=dfpc)) @ Finally, the classical derivation of the domain mean estimator is as a ratio where the numerator is $X$ for observations in the domain and 0 otherwise and the denominator is 1 for observations in the domain and 0 otherwise <<>>= svyratio(~I(x*(x>4)),~as.numeric(x>4), dfpc) @ The estimator is implemented by setting the sampling weight to zero for observations not in the domain. For most survey design objects this allows a reduction in memory use, since only the number of zero weights in each sampling unit needs to be kept. For more complicated survey designs, such as post-stratified designs, all the data are kept and there is no reduction in memory use. \subsection*{More complex examples} Verifying that \texttt{svymean} agrees with the ratio and regression derivations is particularly useful for more complicated designs where published examples are less readily available. This example shows calibration (GREG) estimators of domain means for the California Academic Performance Index (API). <<>>= data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) gclus1 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069)) svymean(~api00, subset(gclus1, comp.imp=="Yes")) svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), gclus1) summary(svyglm(api00~comp.imp-1, gclus1)) @ Two-stage samples with full finite-population corrections <<>>= data(mu284) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) svymean(~y1, subset(dmu284,y1>40)) svyratio(~I(y1*(y1>40)),~as.numeric(y1>40),dmu284) summary(svyglm(y1~I(y1>40)+0,dmu284)) @ Stratified two-phase sampling of children with Wilm's Tumor, estimating relapse probability for those older than 3 years (36 months) at diagnosis <<>>= library("survival") data(nwtco) nwtco$incc2<-as.logical(with(nwtco, ifelse(rel | instit==2,1,rbinom(nrow(nwtco),1,.1)))) dccs8<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~interaction(rel,stage,instit)), data=nwtco, subset=~incc2) svymean(~rel, subset(dccs8,age>36)) svyratio(~I(rel*as.numeric(age>36)), ~as.numeric(age>36), dccs8) summary(svyglm(rel~I(age>36)+0, dccs8)) @ \end{document} survey/inst/ucla-examples.pdf0000644000176200001440000027472013517345027016042 0ustar liggesusers%PDF-1.4 3 0 obj << /Length 1749 /Filter /FlateDecode >> stream xڵXIoFWT5 fA[E`+-Z!J_!hg{oqb&#](r].FTZlgt>6ET4I,U\gO]wx%O|.<{.GZ2#BSte~|#crby+t=䐪ȈÛؙ@1*cnȓnT2OQı։r.\Ϋpy<66]spj=Ȳ5цHZĻS^AW  G5)FKA+o&"w #+̨*Em79)eOReem|GsY.Tl3Pf#7[ةw"h[2 C'l8'Nn`-IPY'&$:Cg &.xy͏oOKvMjh{+ۣ/(dFOJD`K\3HmڡԤJ@Q<7L %#W-&5‡ o!$a XN}M4콏Y̝nh}-b{XRC.#t 67/O qxMj86y6Qrohw!bȧdS 0,fh+і@17L!8?eI)'hk4jqޫ>p$0sĿbU%猊񍪔Y(8yE` %% 66GVcT@+xrܳKNH߸HҲw! 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Lumley (2024) \"survey: analysis of complex survey samples\". R package version 4.4." ) bibentry(bibtype="article", year=2004, author="Thomas Lumley", title = "Analysis of Complex Survey Samples", journal="Journal of Statistical Software", volume="9",number=1,pages="1-19", note="R package verson 2.2", textVersion="T. Lumley (2004) Analysis of complex survey samples. Journal of Statistical Software 9(1): 1-19" ) bibentry(bibtype="book", year=2010, author="Thomas Lumley", title = "Complex Surveys: A Guide to Analysis Using R: A Guide to Analysis Using R", publisher="John Wiley and Sons", textVersion="T. Lumley (2010) Complex Surveys: A Guide to Analysis Using R. John Wiley and Sons." ) survey/inst/porting.to.S0000755000176200001440000000032013517345025015016 0ustar liggesusersVersion 3.6-12 is available for S-PLUS 8.0, ported by Patrick Aboyoun, who was then at what was then Insightful. Comparing this to the R version 3.6-12 should help if you want to port more recent versions. survey/inst/disclaimer0000644000176200001440000000070313704752740014634 0ustar liggesusersThis software comes with NO WARRANTY WHATSOEVER. This product has not been evaluated by the Food and Drug Administration and is not intended to diagnose, treat, cure, or prevent any disease. If it breaks you get to keep both pieces. Not tested on animals. Your mileage may vary. Keep out of reach of babies and small children. For external use only. Times are approximate. Batteries not included. Product of more than one country. May contain nuts. survey/inst/BUGS0000644000176200001440000000066513517345027013265 0ustar liggesusersKnown bug: 1. calibrate and postStratify do not recompute the finite population correction. This matters only when the sampling fraction is very close to one 2. svydesign should check that `weights` is the right length 3. options(survey.lonely.psu="adjust") does not do what it says in the case of svytotal(). Not yet clear whether it does the right thing, though. 4. Should drop unused factor levels from strata, but don't. survey/inst/NEWS0000755000176200001440000016305714576426452013321 0ustar liggesusers4.4-2 Invalid read in C++ code, found by Brian Ripley, fixed by Ben Schneider Updated small-area vignette (Peter Gao) 4.4-1 CRAN 4.4 Fixes to calibration for PPS sampling A PPS variance matrix can now be specified as phase two of a two-phase design. This includes poisson_sampling() as a model for non-response (for Pam Shaw, Jasper Yang) svysmoothUnit() and svysmoothArea() as an interface to the SUMMER package for small-area estimation (Peter Gao, Jon Wakefield, Richard Li) 4.3 Added Ben Schneider's C++ code for multistage variances. It is currently controlled by options(survey.use_rcpp), which defaults to TRUE error in scaling Pearson residuals for svyrepglm led to confint.svrepglm with likelihood profiling not finding the ends of the interval (Stephanie Zimmer) svyolr with rank deficiency and subsetting of cases in raked designs was overwriting the 'keep' variable (Justin Wishart) print.svyciprop() was printing fewer digits for the upper CI limit than the lower limit. C++ code issues fixed degrees of freedom for score F-test in loglinear models fixed (Thomas Loughin) correct the scale in the F-distributed score test (Keiran Shao) allow user to specify degf= in svrepdesign to avoid needing to compute it (for Ben Schneider) warn if svychisq() is used with a single variable (for Isabelle Michaud) fix svyglm(rescale=FALSE) for replicate weights 4.2-1 Fixed the penalty term in AIC for linear regression. Also stop scaling by sigma2 (Justin Wishart) null_has_intercept was being ignored for AIC in linear regression svycontrast() now works with the output of svyvar(), eg to compute correlations from covariances 4.2 Handling of influence functions has CHANGED. A function that supplies influence functions must supply one for every observation it was given in its input: use 0 for observations removed by subsetting. (for Guilherme Jacob) regTermTest(method="LRT") required the two models to use the same observations (of course), but didn't check, so IT WAS WRONG. It now subsets properly. (for Keiran Shao) This will probably be the *last* interpreted-R-only version of survey. Future versions will likely incorporate C++ code for faster variance computation and for small-area estimation. deffs in svyglm.svyrep.design (Ben Schneider) svynls() allows for prior (eg precision) weights (for Gary Nelson) improved names in svyquantile.svyrep.design (Ben Schneider) trimWeights() could get into an infinite recursion (Ingmar Sturm) data(myco) from Rao, Scott, and Skinner (originally from Clayton & Hills) as.svrepdesign now throws an error with post-stratified/raked/calibrated designs -- create replicates *first*, then calibrate (for Lauren Kennedy) trimWeights now works with replicate-weights designs score tests for svyglm (with Keiran Shao) svyby(), and thus svyboxplot(), didn't handle the new quantile functions correctly when standard error/ci weren't requested (Stephanie Zimmer, Raymond Pan) svycontrast() threw an error on the output of svyby() with return.replicates=TRUE (Alena Stern) Fix regTermTest for svycoxph for the methods lookup changes in R 4.0 (and consequential change to marginpred) We don't provide model.matrix.svycoxph any more;use the inherited survival:::model.matrix.coxph Recent survival::coxph() switches to robust variances with non-integer weights so check for model$naive.var before model$var Disable the pass-through from predict.svycoxph to predict.coxph for type="expected" (Bryan Shepherd) svyby( svyvar) would throw an error on domains with only one observation (for Dirk Schumacher) svyvar() computes sample size 'n' in the same way for na.rm=TRUE as for a domain (for Raymond Pan) xdesign() for crossed designs (which aren't strictly surveys, but are basically similar) removed redundant loop in saddlepoint approximation to pchisqsum (Qiaolan Deng) fix in summary(svyivreg) for variances (Chandler McClellan) svyolr() didn't run for subsets of raked/calibrated designs (Antony Damico) add predict.svyolr (for Vincent Arel-Bundock) add anova.svycoxph (for Bryan Shepherd) add stringsToFactors=TRUE to svyby to ensure factors have same levels in domains (for various people including Stephanie Zimmer) more complicated svyby example in ?svycontrast svyranktest() gave an error for multiple groups with replicate weights (Kasuki Yoshida) Additional chisquared test for tables with zeros (CrossValidated question #571328) svydesign() has a 'calibrate.formula' option to tell R that your weights have already been calibrated/raked/post-stratified (for Tobias Schoch) svycontrast() didn't work on svrepglm objects without replicates (Thomas Loughlin) error in svycoxph with rescale=FALSE (Jing Zhang) error in vcov.svyrep.design with mse=TRUE (shows up in svyVGAM) svyquantile() didn't pay attention to interval_type for replicate weights (David Jorquera Petersen) svyquantile() qrules 5 to 9 now return the single data value when there's just one (David Jorquera Petersen) regTermTest() on svyolr() objects created in a function now finds the design object more reliably. (for Pedro Baldoni) regTermTest() will now test against null models in svycoxph() Fixes to svyquantile: https://github.com/bschneidr/r-forge-survey-mirror/pull/7 (Ben Schneider) anova.svyloglin was broken by change to anova.glm(test=NULL) (Brian Ripley) One-sample svyttest() on logical variable now tests for P(TRUE==0) not P(FALSE==0) (Stephanie Zimmer) rename svykm.fit to svykm_fit because CRAN tests 4.1-1 CRAN 4.1 svyquantile() has been COMPLETELY REWRITTEN. The old version is available as oldsvyquantile() (for David Eduardo Jorquera Petersen) svycontrast()'s improvements for statistics with replicates are now also there with svyby(), for domain comparisons (Robert Baskin) svyttest() now gives an error message if the binary group variable isn't binary (for StackOverflow 60930323) confint.svyglm Wald-type intervals now correctly label the columns (eg 2.5%, 97.5%) (for Molly Petersen) svyolr() using linearisation had the wrong standard errors for intercepts other than the first, if extracted using vcov (it was correct in summary() output) svyglm() gave deffs that were too large by a factor of nrow(design). (Adrianne Bradford) svycoxph() now warns if you try to use frailty or other penalised terms, because they just come from calling coxph and I have no reason to believe they work correctly in complex samples (for Claudia Rivera) coef.svyglm() now has a complete= argument to match coef.default(). (for Thomas Leeper) summary.svyglm() now gives NA p-values and a warning, rather than Inf standard errors, when the residual df are zero or negative (for Dan Simpson and Lauren Kennedy) In the multigroup case, svyranktest() now documents which elements of the 'htest' object have which parts of the result, because it's a bit weird (for Justin Allen) svycontrast() gets a new argument add=TRUE to keep the old coefficients as well twophase() can now take strata= arguments that are character, not just factor or numeric. (for Pam Shaw) add reference to Chen & Lumley on tail probabilities for quadratic forms. add reference to Breslow et al for calibrate() add svyqqplot and svyqqmath for quantile-quantile plots SE.svyby would grab confidence interval limits instead of SEs if vartype=c("ci","se"). svylogrank(method="small") was wrong (though method="score" and method="large" are ok), because of problems in obtaining the at-risk matrix from coxph.detail. (for Zhiwen Yao) added as.svrepdesign.svyimputationList and withReplicates.svyimputationList (for Ángel Rodríguez Laso) logLik.svyglm used to return the deviance and now divides it by -2 svybys() to make multiple tables by separate variables rather than a joint table (for Hannah Evans) added predictat= option to svypredmeans for Steven Johnston. Fixed bug in postStratify.svyrep.design, was reweighting all reps the same (Steven Johnston) Fix date for Thomas & Rao (1987) (Neil Diamond) Add svygofchisq() for one-sample chisquared goodness of fit (for Natalie Gallagher) confint.svyglm(method="Wald") now uses t distribution with design df by default. (for Ehsan Karim) confint.svyglm() checks for zero/negative degrees of freedom confint.svyglm() checks for zero/negative degrees of freedom mrb bootstrap now doesn't throw an error when there's a single PSU in a stratum (Steve White) oldsvyquantile() bug with producing replicate-weight confidence intervals for multiple quantiles (Ben Schneider) regTermTest(,method="LRT") didn't work if the survey design object and model were defined in a function (for Keiran Shao) svyglm() has clearer error message when the subset= argument contains NAs (for Pam Shaw) and when the weights contain NAs (for Paige Johnson) regTermTest was dropping the first term for coxph() models (Adam Elder) (this was in R>=4.0) svydesign() is much faster for very large datasets with character ids or strata. svyglm() now works with na.action=na.exclude (for Terry Therneau) extractAIC.svylm does the design-based AIC for the two-parameter Gaussian model, so estimating the variance parameter as well as the regression parameters. (for Benmei Liu and Barry Graubard) svydesign(, pps=poisson_sampling()) for Poisson sampling, and ppscov() for specifying PPS design with weighted or unweighted covariance of sampling indicators (for Claudia Rivera Rodriguez) 4.0 Some (and eventually nearly all) functions now return influence functions when called with a survey.design2 object and the influence=TRUE option. These allow svyby() to estimate covariances between domains, which could previously only be done for replicate-weight designs, and so allow svycontrast() to do domain contrasts - svymean, svytotal, svyratio, svymle, svyglm, svykappa Nonlinear least squares with svynls() now available Document that predict.svyglm() doesn't use a rescaled residual mean square to estimate standard errors, and so disagrees with some textbooks. (for Trent Buskirk) 3.38 When given a statistic including replicates, svycontrast() now transforms the replicates and calculates the variance, rather than calculating the variance then using the delta method. Allows geometric means to exactly match SAS/SUDAAN (for Robert Baskin) vcov.svyrep.design to simplify computing variances from replicates (for William Pelham) svykm() no longer throws an error with single-observation domains (for Guy Cafri) Documentation for svyglm() specifies that it has always returned model-robust standard errors. (for various people wanting to fit relative risk regression models). 3.37 RODBC database connections are no longer supported. Use the DBI-compatible 'odbc' package set scale<-1 if it is still NULL after processing, inside svrepdesign() [https://stats.stackexchange.com/questions/409463] Added withPV for replicate-weight designs [for Tomasz Żółtak] svyquantile for replicate-weight designs now uses a supplied alpha to get confidence intervals and estimates SE by dividing confidence interval length by twice abs(qnorm(alpha/2)). [For Klaus Ignacio Lehmann Melendez] All the svyquantile methods now take account of design degrees of freedom and use t distributions for confidence intervals. Specify df=Inf to get a Normal. [For Klaus Ignacio Lehmann Melendez] svyivreg() for 2-stage least-squares (requires the AER package) warn when rho= is used with type="BRR" in svrepdesign [for Tomasz Żółtak] Add "ACS" and "successive-difference" to type= in svrepdesign(), for the American Community Survey weights Add "JK2" to type= in svrepdesign Warn when scale, rscales are supplied unnecessarily to svyrepdesign More explanation of 'symbolically nested' in anova.svyglm Link to blog post about design df with replicate weights. Chase 'Encyclopedia of Design Theory' link again. 3.36 Add withPV.survey.design for plausible-value analyses (needs mitools >=2.4) 3.35-3 Warning from svrepdesign() if type="BRR" and scale= is specified, to catch defaulting to BRR (Stas Kolenikov) More fixes to svymle() with linear predictors for multiple parameters (no change to results, but gets rid of warning) allow svyby() to have a vector as the first argument force character vectors to factor in id= argument of svydesign (fixes Stack Overflow 54239063) 3.35-2 The previous (3.34) patch to allow offsets in svymle() didn't work with non-trivial linear predictors for multiple parameters. (reported by Beat Hulliger) 3.35-1 svytable() could give an integer overflow with a replicate-weight design having integer weights, such as CHIS. (Elizabeth Purdom) empty factor levels in the strata= argument to svydesign() no longer create strata The twophase() function gave errors when the first phase of sampling had multiple stages (reported by Pedro Luis Baldoni) 3.35 The use of RODBC is DEPRECATED. If possible, I want to move to just supporting the R-DBI interface; you can use ODBC connections with the DBI-compatible 'odbc' package Some instances of deparse() needed to be paste(deparse(), collapse=""), notably in svyciprop() (Boris Fazio) Fix to svycontrast had broken the no-names case (Brian Miner) More helpful error message with missing values in replicate weights (Antony Damico) svyglm() now uses match.call() rather than sys.call() for storing the call (for Michael Laviolette) svystandardize() now takes over=~1 for the whole population (for Michael Laviolette) svycralpha() does Cronbach's alpha (for Franziska Kößler) many tests based on the printed output are moved to tests/testoutput because they differ trivially between platforms and so aren't CRAN-compliant. 3.34 removed duplicate definition of svycontrast.svystat fix match.names() for the case of all the same names in different order (Sebastien Lucie) make rescaling of weights optional in svyglm and svycoxph (Greg Ridgeway) USER VISIBLE CHANGE: default rescaling of weights in svyglm and svycoxph with replicate weights is now mean=1, as it always was with svydesign objects, rather than sum=1. This does not affect estimates or tests or comparisons, but the AIC and its effective degrees of freedom will look more plausible, as will the deviance. Fix handling of missing data for calibrated designs in svyranktest (for Brad Biggerstaff) handle empty subsets in onestage, because svyby(). (for Greg Freedman) allow offset() in svymle() (Patrick Brown) svyhist() returns the same as hist(), plus when freq=TRUE a component count_scale giving the scale factor between counts and density (for Ward Kingkade) example of geometric means added to ?svycontrast (for Irene van Woerden) move check for missing repweights later in the function to pick up more cases (Anthony Damico) fix printing of predicted values from predict.svrepglm (for Anthony Damico) added minqa::newuoa and minqa::bobyqa as options for svymle(), and made 'newuoa' the default when a gradient is available 3.33-2 confint method for svyttest (for Brian Guay) partial fix in start= argument for svyglm() [still doesn't work inside function] 3.33-1 two minor bug fixes for ABS contributions to calibrate() 3.33 Add explicit family= argument to svyglm() methods because of strange scoping problems (for Thomas Leeper) calibrate() now displays differences between sample and population names (for Stas Kolenikov) cal_names() displays what the auxiliary variable names will be for a formula regTermTest() now does Rao-Scott-type Wald tests, which I think are the same as SUDAAN's "Satterthwaite Adjusted Wald Tests" (but I don't have an example to verify). 3.32-2 Actually add the hyperbolic sine distance function Add AIC method for svycoxph 3.32-1 pseudo-rsquared paper is out: update reference Add cal.sinh for the hyperbolic sine distance function (used in CALMAR2) with code from Maciej Beręsewicz Coerce tbl_dfs to data frames in svydesign, svrepdesign, twophase, because they aren't actually a drop-in replacement. 3.32 Add diffs for calibration from Daniel Oehm at ABS - Sparse matrix support - Heteroscedasticity parameters - more flexible boundary constraints Update references to AIC/BIC paper, Statistical Science review paper More information on accuracy of pchisqsum methods svyglm() didn't work with missing values in database-backed designs (Anthony Damico) 3.31-8 Add svysurvreg() (for Pam Shaw and Eric Oh) Remove the tests that don't produce character-identical output on all platforms (for CRAN) 3.31-7 Add deff="replace" as an option for svyratio (for Chris Webb) Add psrsq() for pseudo-R^2 statistics (for Ward Kinkade) 3.31-6 Make database-backed svydesign work with *no* design variables (for Anthony Damico and Guilherme Jacob) 3.31-5 predict.svyglm() uses object$xlevels and object$contrasts and so should be able to guess the right factor levels when they aren't supplied in newdata= (for @thosjleeper) fix return() without parentheses in svykm.R 3.31-4 svyciprop(,method="like") and confint.svyglm(method="like") work even when the design effect is large. 3.31-3 svyciprop has method="xlogit" that reproduces what SUDAAN and SPSS give. (for Rex Dwyer) Added reference for svylogrank Added example from YRBS for software comparison Copied some names from NEWS into THANKS 3.31-2 explicitly dropping dimensions on a 1x1 matrix 3.31-1 Allow for incompatible change in output of CompQuadForm::farebrother() 3.31 update isIdValid() to dbIsValid() for DBI changes. explicitly :: or import ALL THE THINGS. mse option for svrepdesign.character and svyrepdesign.imputationList was ignored (Antony Damico) confint works on output of svycontrast (for Michael Laviolette) denominator df fixed in confint.svyglm (Joey Morris) svyboxplot rule for which lower-tail points are outliers was wrong (David Collins) calibrate() with variable-specific epsilons and zero sample totals didn't work (Alex Kowarik) document that regTermTest(method="LRT") can't handle models with a start= argument and document how to use anova.svyglm instead. (Brad Biggerstaff) update tests output for new formatting in current R. 3.30-4 svypredmeans() does the same things (together with svycontrast()) as as PREDMARG in SUDAAN (for Thomas Yokota and Anthony Damico) 3.30-4 confint.svystat was handling denominator degrees of freedom wrongly for the Wald method (Jared Smith) 3.30-3 vcov.svrepstat does more sensible things when covariances aren't estimated (eg for quantiles). This fixes issues with svyby dropped support for old version of hexbin 3.30-1 Fix example(svyplot) now that "hexbin" package no longer loads grid package 3.30 svyranktest() now allows k-sample tests (eg Kruskal-Wallis) svylogrank() does the generalised G-rho-gamma logrank tests [methods from Rader and Lipsitz (and probably al)] various CRAN fixes 3.29-9 AIC.svyglm, based on Rao-Scott approximation BIC.svyglm, based on multivariate Gaussian likelihood for coefficients svyglm.svyrep.design checks values are finite before replicating calibrate() using a list of margins now allows named vectors for 1-d margins 3.29-8 svyhist(freq=TRUE) works with replicate-weight designs (for Ward Kinkade) 3.29-7 svyranktest() works with replicate-weight designs (for Matthew Soldner) reference to the lavaan.survey package in ?svyfactanal 3.29-6 svyby() now always includes within-domain covariances 3.29-5 Change from multicore to parallel. Parallel processing is now only available with R >=2.14 ddf argument really works now in confint.svyglm (Anthony Damico) colour specification in plot.svykmlist now works (Mark Rosenstein) svyplot() documentation explains how to annotate a hexbin plot 3.29-4 add symmetric=TRUE to eigenvalue calculation in anova.svyloglin, to improve numerical stability subset.svyimputationList now allows the subsets to end up with different observations in them (for Anthony Damico) subset.svyDBimputationList now gives an error if the subsets have different observations, not just a warning that people ignored. svydesign gives an error if there is only one PSU, to catch omission of tilde in svydesign(id=~1,...) (Milan Bouchet-Valat) 3.29-2 confint.svyglm(method="Wald") wasn't using its ddf= argument, because confint.default() doesn't (Anthony Damico) 3.29 svystandardize() for direct standardization over domains withReplicates() has a method for svrepstat objects added predict.svrepglm(), which can return replicates saddlepoint approximation to sum of chisquares works further out into the tails fixed bug in rescaling in calibrate() when initial weights are very wrong (Takahiro Tsuchiya) documented df= argument in svyciprop(method="mean") (Anthony Damico) added df= argument to other svyciprop methods for Stata compatibility (Anthony Damico) 3.28-3 svykappa didn't work for larger than 2x2 tables. (Jeffery Hughes) svyby didn't allow deff="replace" (Francisco Fabuel) svrepdesign(,type="other") now warns if scale, rscales arguments are not given svystat, svrepstat objects now have a plot method (barplot, currently.) svyplot(,type="bubble") now uses the basecol= argument for colors. postStratify() now works when some input weights are zero 3.28-2 calibrate() prints out sample and population totals when the lengths disagree calibrate() is more stable when the initial weights are wrong by orders of magnitude (for Kirill Mueller) calibrate() can now take a list of margins as input, similar to rake() (for various people including Kirill Mueller) 3.28-1 SE now works with output of predict.svyglm (Kieran Healy) make.panel.svysmooth() sometimes had invalid bandwidth choices. as.svrepdesign() now allows for fpc information not present in the design object (Alistair Gray) regTermTest(,method="LRT") works for svyolr(), and method="Wald" now doesn't need user-specified df (for Zachary Grinspan) svrepdesign() checks the length of the rscales= argument (Ward Kinkade). Document the problem with in-line data-dependent variable construction in svyby() (Anthony Damico) Check for completely-missing groups in svyby 3.28 svyvar() for replicate-weight designs now returns whole matrix withReplicates() has method for svyvar() output, to simplify multivariate analyses. design effect estimate for svytotal with replicate weights was wrong (Daniel Fernandes) transform() is now a synonym for update(). lots of partial argument matching removed to keep CRAN happy. 3.27 added anova.svyglm() for Wald tests and Rao-Scott LRT. anova(model1, model2) works for nested models even if not symbolically nested. formula component of svyglm$call is now always named, so update() will work. svyboxplot(,all.outliers=TRUE) didn't work for single boxes (Takahiro Tsuchiya) 3.26-1 Better missing-value handling with replicated weights in svyquantile svyboxplot() has all.outliers= argument to plot all outliers 3.26 Added Preston's multistage rescaled bootstrap (for Alois Haslinger) The multistage bootstrap can use the multicore package if available. calibrate() can take a vector of tolerances (for Alois Haslinger) [this actually used to work by accident, but now it's documented] Clearer error messages when post-strata contain NAs. 3.25-1 The ... argument to svytable() is now passed to xtabs() Clearer documentation about graphing discrete variables. 3.25 svyhist() didn't work for two-phase designs. added svylogrank() for logrank test for survival data. added svyranktest() for two-sample rank tests. svrepdesign() and as.svrepdesign() now have mse= argument to request replicate-weight variances centered around the point estimate rather than the mean of the replicates. The default is controlled by options(survey.replicates.mse), which defaults to FALSE, consistent with previous versions. (For Anthony Damico, among others) 3.24-1 CHANGE: svychisq() statistic="lincom" and "saddlepoint" now use the linear combination of F statistics from pFsum(). 3.24 Rao-Scott test based on linear combinations of Fs is now also available in regTermTest Algorithms from CompQuadForm (AS155 and AS204) now used for method="integration" in pFsum and pchisqsum. These are more accurate and faster than the previous implementations. If you use CRAN binary packages you will need at least R 2.12. pFsum() saddlepoint and Satterthwaite methods are also much faster. The saddlepoint approximation now works for the whole range, not just the right tail. 3.23-3 Some vignettes didn't load the package (Brian Ripley) Added pFsum() for linear combination of F distributions with same denominator. better example (quantile regression) in withReplicates(). 3.23-2 svyhist() didn't handle include.lowest= correctly. (Chris Wild) svyby(, return.replicates=TRUE) now returns the replicates in the same order as the printed output, and labelled. (for Bob Fay) 3.23-1 svycdf() wasn't handling replicate weights correctly. Change in svyquantile() for replicate weights when using type="quantile". Point estimate used to be mean of replicates, now is ordinary weighted quantile. (for Bob Fay) Small changes in handling of zero weights in svyquantile(). 3.23 two-sample svyttest() didn't work with replicate weights. (Richard Valliant) 3.22-4 postStratify now allows 1-d matrix as well as vector in data frame of population counts. (for Jean Opsomer) print.summary.pps wasn't being exported (Gonzalo Perez) svyhist() ignored right= argument predict.svycoxph() was slightly overestimating standard errors for survival curves. [.pps and [.twophase2 crashed when no observations were removed (Gonzalo Perez) 3.22-3 bug in trimWeights (Richard Valliant), also add warning for attempts to trim past the mean weight. 3.22-2 bug in the na.rm.by= argument to svyby() (Trevor Thompson) regTermTest() now does F tests by default (for Chris Wild) 3.22-1 added df= argument to confint() methods for svystat, svyrepstat, svyby, svyratio (for Richard Valliant) added na.rm.by= argument to svyby(), to drop groups defined by missing values of by= variables. confint.svyby() uses SE(), not vcov(), so undefined values in replicates are handled on a per-group basis. svysmooth(,method="locpoly") now has automatic bandwidth choice, and make.panel.svysmooth() will use this choice by default. 3.22 added stratsample() to take stratified samples. fixed bug in design effects for subsets of calibrated or database-based surveys changed scaling in biplot.svyprcomp so area is proportional to weight, rather than height proportional to weight. 3.21-3 svyratio() can now estimate design effects (for Scott Kostyshak) 3.21-2 Rao & Wu bootstrap wasn't sampling n-1 PSUs (Richard Valliant) 3.21-1 bug in printing variances for three or more variables (Corey Sparks) svyquantile() reliably returns NA for NAs in data when na.rm=FALSE. svymle() was not using analytical gradients with nlm() (Christian Raschke) 3.21 added trimWeights() to trim weights, and trim= option to calibrate (for Richard Valliant) clearer documentation that svyquantile() needs ci=TRUE or keep.var=FALSE to work with svyby() added a simple random sample to data(api) as promised in book (Djalma Pessoa) 3.20 in svycoxph() modify the rescaling of weights to avoid very small weights because of convergence problem in coxph() with counting-process data (for Tapan Mehta) added some multivariate statistics: svyprcomp(): principal components, svyfactanal(): factor analysis. added heuristic check that combined.weights= has been specified correctly. confint.default wouldn't give CIs for multiple parameters with replicate weights, because the vcov matrix didn't have variable names. (Art Burke) More of the svyciprop() methods now work for replicate-weight designs. The book of the package is now available! (see http://faculty.washington.edu/tlumley/svybook) 3.19 svrepdesign() can specify replicate-weight columns with a regular expression svrepdesign() can produce database-backed designs svyquantile() has a df argument to use a t distribution in Woodruff's method (for Wade Davis) calibrate() doesn't require an intercept in the calibration model (for Richard Valliant) regTermTest() and model.frame() work with svyolr() (for Michael Donohue) better printing of svyvar() output (for Brad Fulton) twophase() documents more clearly that method="simple" is preferred for standard epi designs where it works. better error messages when a database-backed design has a closed connection 3.18-1 documented the need to use quasibinomial/quasipoisson in svyglm improved the description of confidence intervals and standard errors for svyquantile. 3.18 Changed the default to combined.weights=TRUE in svrepdesign() Fixed bug in multiple imputation analysis with multicore package. The check for PSUs properly nested in strata had some false negatives. 3.17 Under Linux, Mac OS, and most Unix systems, multiple processors can be used for the subgroups in svyby(), the imputed data sets in with.svyimputationList and with.DBsvyimputationList, and the replicate weights in svyglm.svyrep.design and svyolr.svyrep.design. This requires the 'multicore' package and the argument multicore=TRUE to the functions (in the absence of the multicore package, the multicore=TRUE option is just ignored). svyvar.svyrep.design handled NA values incorrectly (Arthur Burke) print.summary.twophase2 wasn't exported, so summary(twophase.object) gave Too Much Information (Norman Breslow) svytotal.svyrep.design labelled the statistic it computed as 'mean', although it really was the correct total. (Arthur Burke) detection of PSUs not nested in strata was incorrect in some cases. added xbins= option to svyplot for hexbin styles (for Bryan Shepherd) print() method now has strata in a more predictable order (for Norman Breslow) regTermTest(,method="LRT") now does Rao-Scott-type tests based on the estimated loglikelihood ratio, for generalized linear models and the Cox model. Similarly, confint.svyglm(,method="likelihood") does confidence intervals based on the Rao-Scott-type likelihood ratio test. Updated marginpred() to work with survival 2.35-7 Documentation fixes revealed by the new R pre-2.10 help parser Added unwtd.count() to count the raw number of non-missing observations. The new PPS designs now work with subset(). 3.16 PPS designs without replacement, based on the weighted covariance of sample indicators: Horvitz-Thompson and Yates-Grundy estimators, Overton's approximation, Hartley-Rao approximation, a modified Hartley-Rao approximation that depends only on sample data. 3.15-1 The new two-phase designs added in 3.15 are now exported properly. 3.15 Full multistage sampling now possible at both phases of a two-phase design, and the standard errors now exactly match Sarndal et al. The underlying algorithms use sparse matrices to store the weighted covariance of sampling indicators, and so require the Matrix package. Use method="approx" in twophase() to get the old methods, which use less memory. added marginpred() for predictive margins, ie, predictions after calibration/direct standardization on confounder distribution. standard errors for predict.svyglm(,type="response") were printing incorrectly. as.data.frame.svrepstat now works when the result has return.replicates=TRUE The separate package odfWeave.survey provides methods for odfWeave::odfTable for some survey objects. formula() now works correctly on svykmlist objects with standard errors. 3.14 predict.svycoxph() now does fitted survival curves with standard errors for the Cox model. (for Norman Breslow) standard errors for svykm use a bit less memory. quantile.svykm can do confidence intervals added some references on svykm standard errors. tidied up some help pages. 3.13 Add standard errors to svykm() (for Norman Breslow) fix typo in svyquantile(interval.type="betaWald") and add 'degrees of freedom' correction to the effective sample size. add 'degrees of freedom' correction to effective sample size in svyciprop, type="beta". SE, coef for svyratio objects now optionally convert to a vector and confint() now works on ratios. 3.12 Add svyttest() for t-tests, as a wrapper for svyglm Add svyciprop() for confidence intervals for proportions, especially near 0 or 1 confint() works with svycontrast(), svyquantile(), svyciprop() output. bug fix for updates to ODBCsvydesign objects. Add example of PPS sampling to example(svydesign), and link to help for variance estimation. Add Berger(2004) reference. svyby() now has vartype="ci" to report confidence intervals (for Ron Burns) update survival examples to work with new version of survival package. 3.11-2 Document that calibrate() to PSU totals requires at least as many observations as calibration variables pchisqsum(,type="saddlepoint") now works down to mean x 1.05 rather than mean x 1.2 The breaks= argument to svyhist() now works (Stas Kolenikov) svyhist() works on database-backed designs. 3.11-1 svyglm() [and svyratio()] gave an error for post-stratified designs with missing data (Shelby Chartkoff) svycoxph() gives a clearer error message for negative weights. svyquantile() now has a 'betaWald' option, as proposed by Korn & Graubard (1998), and has an option for handling ties that appears similar to (some versions of) SUDAAN (for Melanie Edwards) plot.svycdf() has an xlab argument to override the default labels 3.11 as.svrepdesign now has type="subbootweights" for Rao and Wu n-1 bootstrap An approximation for PPS without replacement due to Brewer is available in svydesign() svydesign() no longer warns if some fpc are exactly zero, but still warns if they are suspiciously large or small 3.10-1 svycoplot can now pass ... arguments to xyplot(), not just to panel. svycontrast() has a 'default' method that assumes only a coef() and vcov() method are available. Fixed example code for anova.svyloglin. Added predict(,type="terms"), termplot(), residuals(,type="partial") for svyglm. As a result, the default for se= in predict.svyglm has changed. make.panel.svysmooth() makes a weighted smoother as a slot-in replacement for panel.smooth(), eg in termplot(). print.summary.svyloglin was broken (Norm Breslow). confint() method for svyglm has both Wald-type and likelihood-type confidence intervals (based on Rao-Scott test) documented that svykappa() requires factor variables. svysmooth() doesn't fail when data are missing. documented that update.svyloglin is faster than fitting a new model. dotchart() methods for svyby, svystat, svrepstat svyloglin() handles missing data better. svymle() didn't work if constant parameters were in any position other than last. svyby() now has a return.replicates argument (for Phil Smith). logit and raking calibration could run into NaN problems with impossible bounds. Step-halving seems to fix the problem. 3.10 update() methods for database-backed designs. improvements in graphics for subsets of database-backed designs. barplot methods for svystat, svrepstat, svyby objects. svytable() for database-backed designs quantiles work with svyby(covmat=TRUE) for replicate-weight designs. fix printing of p-value in svychisq, type="lincom" better error messages for misspecified fpc in svydesign() database-backed analysis of multiple imputations. formatting changes to coef.svyquantile, SE.svyquantile, svyby svrepdesign works with multiple imputations (though not with databases) fix for missing factor levels in subsets of database-backed designs allow svychisq(statistic='lincom') with replicate weights. quantile regression smoothers in svysmooth() add svychisq.twophase() (for Norm Breslow) changed defaults in predict.svyglm so that plot.lm works (for Patricia Berglund) svyloglin() for loglinear models, with Wald and Rao-Scott tests. pchisqsum() (and svychisq, anova.svylogin) have a saddlepoint approximation. 3.9-1 improvments in svyby, degf, svyglm for subsets of calibrated designs or database-backed designs. svyboxplot() and svycdf() now work with database-backed designs. ODBC support for database-backed designs. modified the degrees of freedom calculation in svyglm. 3.9 Added database-backed design objects. The data= argument to svydesign can be the name of a database table in a relational database with a DBI-compatible interface. Only the meta-data is kept in R, other variables are loaded as necessary. 3.8-2 Added svycoplot() 3.8-1 Added subset.svyimputationList coef.svyolr returns intercepts as well (by default). svyolr() has a method for replicate-weight designs print methods for svykm, svykmlist weren't exported. 3.8 svyolr() for proportional odds and related models. license is now GPL 2|3 to accomodate code ripped from MASS package svykm() for survival curves (no standard errors yet) 3.7 Added style="transparent" to svyplot(). svyby() and svytable() work on twophase objects. svychisq() has statistic="lincom" for linear combination of chisquare, the exact asymptotic distribution. Added interface to mitools package for analyzing multiple imputations svykappa() for Cohen's kappa (for Tobias Verbeke) 3.6-13 Change in tolerances so that calibrate() works better with collinear calibration variables (Richard Valliant) calibrate() can be forced to return an answer even when the specified accuracy was not achieved. 3.6-12 svyhist() handles missing data better. Added svycdf() for cumulative distribution function estimate. 3.6-11 postStratify() for repweights was standardizing the replicates to slightly wrong population totals. (Alistair Gray) vcov() for two-phase designs gives the contributions from each phase for a wider range of statistics. (Norman Breslow) fixes for codetools warnings. 3.6-10 Added error message for missing sampling indicator in two-phase design (Lucia Hindorff) Added tests/kalton.R with reweighting examples. make.calfun() for creating user-specified calibration distances. NOTE: Calling grake() directly now requires a calfun object rather than a string: see help(make.calfun). 3.6-9 Bootstrap weights used last stratum size rather than harmonic mean for n/(n-1) factor (Djalma Pessoa) method= argument to svycoxph() didn't work (Lisa McShane) svyquantile did not treat missing values as a domain (Nicole Glazer) fix for change in pmax/pmin (Brian Ripley) Add pchisqsum for distribution of quadratic forms. 3.6-8 A fix in 3.6-6 had broken svycoxph when only a single predictor variable was used (Lisa McShane) 3.6-7 svycoxph() is much faster for replicate weights degf.svyrep.design uses a cached value rather than recomputing. 3.6-6 svyquantile was not passing method= argument to approxfun() (Jacques Ferrez) Documented that svyquantile(interval.type="score") may not be any more accurate Broken link due to typo in svyratio.Rd (Giuseppe Antonaci) postStratify could overestimate standard errors for post-strata cutting across existing sampling strata. (Ben French) svycoxph() would not run for subsets of calibrated designs. (Norman Breslow) 3.6-5 Add return.replicates option to svyratio() (for ine.pt) Add amount= option to svyplot Design effects for totals were wrong for PPS sampling. (Takahiro Tsuchiya) 3.6-4 rownames fix for svyratio with a single statistic. 3.6-3 raking by rake() now has slightly more accurate (smaller) standard errors. As a result, it can't be used on pre-2.9 svydesign objects. calibrate() does not warn about name mismatches when population argument has no names. svyCprod, svyrecvar, grake now exported. 3.6-2 covmat=TRUE option for svyratio. svycontrast() fix for svyby() with empty groups (ine.pt) 3.6-1 Allow averaged bootstrap weights (as StatCanada sometimes produces) in svrepdesign() Fix derivative to get faster convergence in logit calibration (Diego Zardetto) svycontrast() can take named vectors of just the non-zero coefficients. Nonlinear combinations of statistics with svycontrast() 3.6 Allow empty factor levels in calibration (for Diego Zardetto). Work around for strange S4 class/NAMESPACE issue with hexbin plots; actual fix requires more understanding. regTermTest handles MIresult objects. Add dimnames, colnames, rownames methods (for ine.pt) svysmooth for scatterplot smoothers and density estimation (needs KernSmooth) Give a warning when fpc varies within strata. svycontrast() for linear combinations of survey statistics covmat=TRUE option to svyby() for replicate-weight designs, so the output can be used in svycontrast(). 3.5 Add estWeights for Robins et al way of using auxiliary information (ie AIPW). Remove JSS article and survey-vanderbilt.pdf from inst/ since they are now seriously out of date. paley() now gives matrices of order 2^k(p+1), which are usually of minimal or near-minimal size. Drop 72x72 and 256x256 Hadamard matrices, which are easy to recreate, from precomputed set and replace 36x36 with the one from Plackett & Burman, which has full orthogonal balance Note that changes to svyby now require R 2.2.0 or later. predict.svyglm has option to return just variances (rather than entire variance-covariance matrix) drop.empty.groups now works when the grouping variables are not factors. Add a namespace Move precomputed Hadamard matrices from inst/hadamard.rda to R/sysdata.rda 3.4-5 Add svyboxplot (for Luke Peterson) Add drop.empty.groups option to svyby 3.4-4 Paley construction of Hadamard matrices now knows primes up to 7919, works for larger sizes if the user supplies a suitable prime. calibrate() now reorders elements of 'population' to match column names of model matrix if necessary. predict() method for svyglm (for Phil Smith, Andrew Robinson) svyratio() for two-phase designs. Added vignette on domain estimation. svyby() can report multiple vartypes. 3.4-3 make svyratio work with svyby (for Phil Smith) increase default number of iterations in calibrate() 3.4-2 Options for residual df for summary.svyglm, default based on degf Default denominator df for svyglm, svycoxph in regTermTest. survey.lonely.psu now applies to as.svrepdesign. keep up with changes in all.equal() for R 2.3.0 3.4-1 Speed optimizations for JKn weights with self-representing strata - jackknife replicates are not created for these strata - svytotal does not use these strata in variance calculation. - svytotal, svymean, svyratio,svyquantile,svyglm recognize designs (eg subsets) where all strata are self-representing. [.repweights_compressed does less copying and is a lot faster for large designs Added verbose= option to svyby() to monitor slow computations. Added vartype="cv","cvpct" options for svyby(). Two-phase designs gave incorrect variances in some cases [they were correct if the first stage was infinite superpopulation sampling or if all phase 1 ultimate sampling units were represented in phase 2]. These are fixed but twophase() now limits the first phase to single-stage cluster or element sampling. [detailed bug report from Takahiro Tsuchiya] added vignette describing estimator of phase-one variance in two-phase designs minor speedup in svyrecvar() for self-representing strata added make.formula() for convenience with many variables. 3.4 twophase() for specifying two-phase designs. two vignettes: a simple example and a description of two-phase epi designs svyratio handles missing data. cv() gives NaN rather than an error when the statistic is zero (for ana.pmartins@ine.pt) oldsvydesign() is officially deprecated Jackknife variances for strata with a single population PSU were wrong (non-zero) (ana.pmartins@ine.pt) svyglm refused to work on subsets of calibrated designs 3.3-2 Add cv, SE, coef, and deff methods for svyby (for Ana Quiterio) as.data.frame methods for svystat, svrepstat regTermTest can do F-tests now (Daryl Morris). fix documentation of value for as.svrepdesign (Alan Zaslavsky) 3.3-1 Make nest=TRUE in multistage designs work when only some initial sampling stages are stratified Multistage recursive variances were only going to two stages. Add "(with replacement)" to output of print.survey.design2 when no fpc is specified. 3.3 Added more generalized raking estimators: raking ratio, bounded raking ratio, logit, (for Ana Quiterio) svytable() could sometimes leave the class attribute off the result. summary() now gives tests of association for svytable(). svychisq() works for replicate designs degf() gives approximate degrees of freedom for replicate designs. Clearer error messages when design information is missing. 3.2-1 Fix ordering bug in ftable.svyby (Stefano Calza) The "probability" option added to svyquantile for replicate designs in 3.1 computed standard errors for the wrong tail. (Gillian Raab). 3.2 Add option to calibrate() to make weights constant within clusters. Add bounded regression calibration to calibrate() 3.1-1 Rescale svyvar output by n/(n-1) to match Kish, which makes a small difference to design effect computations. (for Takahiro Tsuchiya) Test for presence of intercept in calibrate() was too fussy. 3.1 Quantiles for replicate-weight designs now by default compute confidence intervals on the probability scale and transform, so they are valid for jackknife designs. (as Gillian Raab suggested long ago) Analyses on replicate weights should use eg svymean, which has methods for replicate weight designs; the old (eg svrepmean) variants are now deprecated. calibrate() can now use regression models with variance proportional to linear combination of predictors (and so can duplicate ratio estimators of means and totals) Prettier labelling of objects created by postStratify(), calibrate(), update(), subset() svytotal on replicate weight designs was computing means, not totals (probably since 3.0). 3.0-1 Allow some strata to have an infinite population (zero sampling fraction) (this doesn't happen in reality but is the recommended analysis for handling certainty PSUs in some large NCHS studies). Let svyby() handle vectors that are not in the design object (even though they are discouraged) calibrate() was working only under stratified/simple random sampling. Allow user-supplied Hadamard matrix for brrweights. as.svrepdesign gave a spurious warning when converting post-2.9-1 objects without finite population corrections to BRR. Allow multicolumn response variable in svymle() (for survival data) Add nlm() as the default optimization method for svymle(). 3.0 Added simple GREG (G-calibration) estimators with calibrate() Added deff="replace" option to compute design effects comparing to simple random sampling with replacement, eg for designs where the weights do not sum to the population size. (for Gillian Raab) Added more references for median estimation. Added separate ratio estimator of totals for stratified samples. (for Renzo Vettori) cv.svyratio was inverted. rake() on survey design objects was accumulating cruft in the postStrata component on each iteration. Subsetting of raked designs without replicate weights was broken (Steve Roberts) Standard errors were wrong for some domain estimates in post-stratified models without replicate weights. More extensive tests comparing domain estimates to equivalent ratio and regression formulations. Changed default in svyby to keep.var=TRUE Prettier stratum labels. New homepage at http://faculty.washington.edu/tlumley/survey/ svyplot(type="hex") works with both pre1.0 and post1.0 versions of the hexbin package. Fixed svychisq denominator degrees of freedom for stratified designs for bug introduced by multistage revision. (Takahiro Tsuchiya) 2.9-1 Fixed typo in description of fpc in svydesign.Rd Added inst/twostage.pdf with examples of two-stage analyses. Handling of fpc specified as proportion in the absence of weights was wrong. 2.9 Added full multistage sampling, involving a redesign of the survey.design object. The old objects are deprecated; they may be converted with as.svydesign2. Use options(survey.ultimate.cluster=TRUE) to get the same one-stage standard errors as earlier versions and options(survey.want.obsolete=TRUE) to turn off the annoying warnings about old-style survey objects. If you must create old-style survey objects use oldsvydesign(). As a consequence of the redesign, most of the svyxxx functions are now generic, with methods for both svydesign and svrepdesign objects. Use svymean instead of svrepmean, for example. Added more Hadamard matrices, including the Paley construction. brrweights() now finds designs of nearly optimal size for most surveys. Faster svymean, svytotal for replicates, with less memory use. Added "bootstrap" option for as.svrepdesign svyby and ftable.svyby now handle Deff (expanded from a suggestion by Tobias Verbeke) svyhist() for probability-weighted histograms added svycoxph() for replicate weight designs The "lonely.psu" corrections will be applied to strata with a single PSU in a subset (domain) if options("survey.adjust.domain.lonely") is TRUE. The default is FALSE. subset.survey.design was not working for post-stratified designs. Added a PDF file with examples from UCLA ATS web site, including comparisons with WesVar and SUDAAN. (inst/ucla-examples.pdf) Added slides from a talk at Vanderbilt University. (inst/survey-vanderbilt.pdf) Fixed Deff to use simple random sampling without replacement. Much faster confidence intervals for quantiles based on inverting a Wald test are now default. These are less accurate in small samples; the old method is still available. (based on suggestion from Gillian Raab) 2.8-4 Added a whole lot more references to the documentation. data(hospital) now has two sets of weights, one matching the UCLA ATS site and one matching the original reference. (from Tobias Verbeke) summary.svyrep.design was reporting 1 replicates for compressed weights (but still computing correctly) 2.8-3 postStratify for svydesign objects was giving too large standard errors Add deff() to extract design effects. 2.8-2 rewrite cv() to use coef() and SE() 2.8-1 Make Deff estimates work with ftable. (for Gillian Raab) ftable.svyby didn't work with a single by() variable (for Gillian Raab) Missing values now allowed in svychisq(). (for Lee Sieswerda) 2.8 fix printing of svyby broken in 2.7 add ftable.svyby postStratify for svydesign surveys. 2.7-1 as.svrepdesign was giving the wrong weights for type="Fay" in 2.7 2.7 Option compress=TRUE in as.svrepdesign to reduce size of replicate weight matrix (and in rake(), postStratify()). Also function compressWeights() to do this to arbitrary replicate designs. terms() reorders variables in interactions, which confused regTermTest (Daniel Almirall) Added extractor function SE() for standard errors (Andrew Robinson) hadamard() now finds smaller Hadamard matrices. svyCprod warns if a subset has only one PSU in some stratum (Gillian Raab) Added tests/lonely.psu.R Added another option "average" for lonely.psu (Gillian Raab) svydesign can now detect from sampling weights or fpc when a stratum with a single PSU is self-representing, and in these cases options("survey.lonely.psu") is not used. ftable.svystat and ftable.svrepstat to produce better tables of percentages and totals. Experimental set of functions to help in computing non-response weights (see ?nonresponse for details) 2.6-2 Better handling of NAs in svyby Subsetting didn't work right for single-observation subsets. svyglm and svycoxph had scoping problems when run inside a function (Daniel Almirall) svyglm and svycoxph now accept weights (to be multiplied by the sampling weights) With R 2.0.0 less copying will occur, especially when variables= is not specified in a design 2.6-1 Totals for factors give cell totals. 2.6 Design effects were broken for multiple means computed at once. Add coefficient of variation for mean, total, ratio,... variables= argument of svydesign works with missing data (Tobias Verbeke) Fix reference to Binder (1991) (Tobias Verbeke) Means for factors now give cell means. coef and vcov methods for svystat and svrepstat. Another tiny example dataset from the VPLX manual svrepvar was incorrect for multiple variables simultaneously Better error messages for missing data in svrVar. 2.5 Wald tests for association in contingency tables. svyplot() for weighted graphics (some of these require "hexbin") Examples for rake(), postStratify() svyby() works for svrepdesign analyses as well added subset.svyrep.design() svrepvar() added Design effects for means and totals. (Gillian Raab) 2.4 Make regTermTest work with svycoxph() Clearer output for print.svycoxph() (Daniella Gollinelli) Rao-Scott adjusted tests for contingency tables. svyby() for tables of means, medians, etc 2.3-2 Fix for svyquantile confidence intervals. 2.3-1 clearer warnings in svrVar when some replicates give NA . (for Gillian Raab) 2.3 svyquantile has confidence intervals, added svrepquantile. 2.2-1 as.svrepdesign didn't pass options to brrweights (for Fred Rohde) 2.2 published in Journal of Statistical Software - If population size is specified, but not weights or probabilities, work out the probabilities from the population size - Clearer error message when some design information is NA (for Tobias Verbeke) - better update() methods 2.0 Just a numbering change. 1.9-3 Fix svytotal variance estimate as.svrepdesign wasn't handling unstratified cluster samples right. Check for fpc in multistage samples, which we don't handle. add print method for basic survey statistics add rake() California API data. 1.9-2 Added post-stratification of replicate-weights 1.9-1 Bugfix: jknweights was requiring finite population correction. 1.9 - "certainty" option for single-PSU strata - Replication weight analyses (alpha version) 1.4 - I think all the possible permutations of arguments in svydesign now work. - The examples in svyglm incorrectly had a data= argument. 1.3 svydesign wasn't allowing weights to be a vector. 1.2 - svydesign(nest=TRUE) now uses less memory - added regTermTest for testing regression terms. 1.1 Added subset, update methods. Variance estimation is now correct for subpopulations produced with select or subscripting. 1.0 No changes 0.9-5 - finite population correction should be done with PSUs not individuals - added Cox models 0.9-4 - svyCprod was computing n/(n-1) using number of observations, not number of PSUs, and was averaging observations rather than PSU means to compute stratum means. - Bug in handling multiple levels of cluster id in svydesign 0.9-3: Finite population correction. Adjustments for stratum with single PSU (Fred Rohde) Fixed svydesign(nest=TRUE) to work with strata 0.9-1: First release. survey/inst/COPYING0000644000176200001440000000031213517345027013622 0ustar liggesusersExcept as otherwise stated, the code is copyright 2002-2014 Thomas Lumley svyolr() and many of its methods are closely based on polr() from the MASS package, copyright Brian Ripley and Bill Venables. survey/inst/twostage.pdf0000644000176200001440000022114113517345027015124 0ustar liggesusers%PDF-1.4 3 0 obj << /Length 2144 /Filter /FlateDecode >> stream xڽYKБB^$^m1|}J-zLDͬ'䯧^M6E;4dwWuWt̔ ͂6fv{s]?Ÿb7_RGx_kY&榢3l'x?=#M|AO}^73U`󳅩Uh:aE <^ddzfF`lio%ܾ00vb,V=kT, PaAՁ3t-tㅩ\i+8ܻ."wr%0D\aZ޲頕,%#2\Ȧ&:vN!r gлpe-\2jJDmҍ$MD{U.81U+s_^9:6! p_wﾻx|809p0P?Bhjy e 3aކμb#oS ˥wp1W;9L:ec$"iMqDi<:Q_R#ki|Wݜ|\;*]:EiFbÆ`YR*IqN=J^F;R$%̐Er 0$G"C <@C*.;gB bEx iX07bYĶo t_8<}CZ&hVNW_򔸵'UfA2eD!z }t[kiW)mëFũjTgx0jW{"$,֞0 M2=8U=;H6 F>B#"Lm37F:*j`uT+_m^ݨdp!_:4@|LŬ%$/.2W%_'Xy-<<(6*TX0ﲯ:ײjTPq]t4Úi!;W줤*47vX'iB2E: x-B }9\Wl56S#X!F굼DyA *Dީ;Iw"eW)"*gPPD-H| V%ciϻD tNVr1>YǙ4NF/bz]j 47AhnK) IڝWbwfJ=/X>cݴAkqNߘQ?$*-[zyRb|o491dwa~1+X&Ah] \̺OY )ڲ|廼;Zu-f /W!Utд Y}}\vĤon;hyT T[[ٷnsdJ }=t+gdE`ictOmEVeRC% |u.Pendstream endobj 2 0 obj << /Type /Page /Contents 3 0 R /Resources 1 0 R /MediaBox [0 0 612 792] /Parent 28 0 R >> endobj 1 0 obj << /Font << /F34 6 0 R /F19 9 0 R /F8 12 0 R /F43 15 0 R /F44 18 0 R /F46 21 0 R /F48 24 0 R /F53 27 0 R >> /ProcSet [ /PDF /Text ] >> endobj 31 0 obj << /Length 1534 /Filter /FlateDecode >> stream xڵXK60z)R)ɡӮYk]?v{HzE Z| 3<8wI4&*fLMSYl4Oa-~:KyDus5MwN<+iR>_~ o{"-tsÄ%KL<|%FU *Ǥ i& qTEH™Lk2s/)lCH5 ţn 'v{zJO=4iWT2ȈUU̘* 1Z8c]ΜI?c9nuf OНqIj} -DL b,30v*MB&VXhDF\#  x1O+d@DW@j30~aL{hij$mXoj}&&+٠NV1Y:8U{$S=1 5Sp7iOͷvZIL%G`µH0څVm+L Bv̚|hnS;m?(d0͠w[ҺsťLr aJܑwŲ K˲/LK@ r%Y!(-JP \COY՛3};.klPAUmkiNQփJ1::&li=0sw*뺚Y5b k?k,"l5A 7* mڶy uݩ,Cڊ# wP5 3CL8Qx+&+E/[|B0A;ɓ|AQ96Fjendstream endobj 30 0 obj << /Type /Page /Contents 31 0 R /Resources 29 0 R /MediaBox [0 0 612 792] /Parent 28 0 R >> endobj 29 0 obj << /Font << /F8 12 0 R /F53 27 0 R /F46 21 0 R /F48 24 0 R >> /ProcSet [ /PDF /Text ] >> endobj 34 0 obj << /Length 1232 /Filter /FlateDecode >> stream xXKs6WHM-4IC:==(d{j=ߞ}AEIڴ=p}|o&/A*o@[ƾx7Vƺdsv2{TVX |_>>nj9䮼ln\nVvn _'߿*lVeWZm@j'M"0ʝ.@fZoC^]5VgCfF;x0ZkA6Ghm02(oHkU9 ek`5pg+`d$,%fovcQZg \s26jܮe'K|d\P;$ "P\[,5벀}ͻxgӪX3Gr\Urs?bCl0T\ΈʵWV2]!~,/˫^8r6kg\9<j͚LD{X'5]Sd[E1@}$NB+g{-!af.D ,  3N ]C`.{j"4DF][_I0df;/ #7|Ղ;Qv.a218R@ێe`MZթ=NY%@;BPRƒ4/U- 23S|b\Qr3U-9Ί[.$_& 8ZK_B/F_A[12oT?%ŀOD粎߳n\+bUU@5wvpdf@2I̭8~>nQthcbq!6$us+UfOl%¸΅na8BXt<|szAX^slosWcyp:oo)2z34p>PYY^cwsubZJeI,J]\|~Lendstream endobj 33 0 obj << /Type /Page /Contents 34 0 R /Resources 32 0 R /MediaBox [0 0 612 792] /Parent 28 0 R >> endobj 32 0 obj << /Font << /F8 12 0 R /F43 15 0 R /F53 27 0 R /F46 21 0 R >> /ProcSet [ /PDF /Text ] >> endobj 37 0 obj << /Length 1270 /Filter /FlateDecode >> stream xWKo6WEr7VDRϢ[ m@EbXeu;/J"]Y2973'ghYlF2OtGM4.~o$ԑgM9{kcdКW㉉So+"lr 8]frKh?qQg |VT]F'_Nܖ5zRuXG2p, T(.(<|>, gk'KݏN3 G3|NqW nO# VoMҵnYJm>PFC4(OhSƼ7X wb)*gjޙD39+yJz—7b#B̋ڒ- T6R uYqްISq:OTJ|?Dx\BKCa 'D$(ϷӶ^_MOLUDf#kO[\%AKSo5Kx.ؾ@[b T^2Jq Qtyd!7Z Z*WND1v:u;6UؽceYh^R[&Jt)pFt/#`dߴ'RFG*uthQ/.BQؠwcv*6q\ѦA]头yq&/-)% hKߡypZDɴvDSdtd);E/4}cC=_Nj1[?푩Ќ-۸S]c? 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