robust/0000755000176200001440000000000014660215412011571 5ustar liggesusersrobust/tests/0000755000176200001440000000000014443667220012742 5ustar liggesusersrobust/tests/weight-tst.R0000644000176200001440000000243714443667220015172 0ustar liggesusers#### Tests for psi(), rho() etc library(robust) ### Test internal consistency ## (1) rho'(x) = psi(x) ## (2) psi'(x) = psp(x) ## (3) chi(x) = pmin(R, rho(x)) ( *not* the definition for S-estimates !! ) ## but really chi() = 1/2 psi()^2 Huber; for the others chi() == rho() ## x <- seq(-4,4, length = 801)# large n x. <- seq(-4,4, length = 2001)# larger n verbose <- TRUE for(ipsi in 1:3) { # later 1:4 -- ipsi = 4 nowhere documented cat("ipsi = ", ipsi, "\n") f.x <- cbind(psi = psi.weight(x, ips=ipsi), psp = psp.weight(x, ips=ipsi), chi = chi.weight(x, ips=ipsi), rho = rho.weight(x, ips=ipsi)) rhoF <- splinefun(x,f.x[,"rho"]) psiF <- splinefun(x,f.x[,"psi"]) pspF <- splinefun(x,f.x[,"psp"]) ## chiF <- splinefun(x,f.x[,"chi"]) p1 <- psiF(x., deriv=1); p. <- pspF(x.) r1 <- rhoF(x., deriv=1); ps <- psiF(x.) if(verbose) { cat("psi'(.) = psp(.):", all.equal(p1, p., tol = 1e-6),"\n") cat("rho'(.) = psi(.):", all.equal(r1, ps, tol = 1e-6),"\n") ## TODO: chi ? } stopifnot(all.equal(p1, p., tol = if(ipsi == 3) .05 else 1e-3), all.equal(r1, ps, tol = 1e-4)) if(verbose) cat("\n---------------------\n\n") } ### Plots --> ../man/weight.funs.Rd robust/tests/Examples/0000755000176200001440000000000014445406607014522 5ustar liggesusersrobust/tests/Examples/robust-Ex.Rout.save0000644000176200001440000007466114445407722020237 0ustar liggesusers R Under development (unstable) (2021-10-11 r81035) -- "Unsuffered Consequences" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (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. > pkgname <- "robust" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > options(pager = "console") > library('robust') Loading required package: fit.models > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("OverlaidDenPlot.fdfm") > ### * OverlaidDenPlot.fdfm > > flush(stderr()); flush(stdout()) > > ### Name: overlaidDenPlot.fdfm > ### Title: Overlaid Density Plot > ### Aliases: overlaidDenPlot.fdfm > ### Keywords: hplot > > ### ** Examples > > data(los, package="robustbase") > > ## Not run: > ##D > ##D los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"), > ##D x = los, densfun = "gamma") > ##D > ##D > ##D los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"), > ##D x = los, densfun = "weibull") > ##D > ##D overlaidDenPlot.fdfm(los.fm, xlab = "x-axis label", ylab = "y-axis label", > ##D main = "Plot Title") > ##D > ## End(Not run) > > > > cleanEx() > nameEx("anova.glmRob") > ### * anova.glmRob > > flush(stderr()); flush(stdout()) > > ### Name: anova.glmRob > ### Title: ANOVA for Robust Generalized Linear Model Fits > ### Aliases: anova.glmRob anova.glmRoblist > ### Keywords: robust regression methods > > ### ** Examples > > data(breslow.dat) > > bres.int <- glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(), data = breslow.dat) > anova(bres.int) Analysis of Deviance Table poisson model Response: sumY Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev NULL 58 11983.1 Age10 1 9125.7 57 2857.5 Base4 1 803.0 56 2054.5 Trt 1 -884.6 55 2939.1 Base4:Trt 1 -949.1 54 3888.2 > > bres.main <- glmRob(sumY ~ Age10 + Base4 + Trt, family = poisson(), data = breslow.dat) > anova(bres.main, bres.int) Terms Resid. Df Resid. Dev Test Df Deviance 1 Age10 + Base4 + Trt 55 2939.072 NA NA 2 Age10 + Base4 * Trt 54 3888.204 +Base4:Trt 1 -949.1315 > > > > cleanEx() > nameEx("anova.lmRob") > ### * anova.lmRob > > flush(stderr()); flush(stdout()) > > ### Name: anova.lmRob > ### Title: ANOVA for Robust Linear Model Fits > ### Aliases: anova.lmRob anova.lmRoblist > ### Keywords: robust regression methods > > ### ** Examples > > data(stack.dat) > stack.small <- lmRob(Loss ~ Water.Temp + Acid.Conc., data = stack.dat) > stack.full <- lmRob(Loss ~ ., data = stack.dat) > anova(stack.full) Terms added sequentially (first to last) Chisq Df RobustF Pr(F) (Intercept) 1 Air.Flow 1 41.228 6.026e-11 *** Water.Temp 1 6.522 0.009257 ** Acid.Conc. 1 0.551 0.449386 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(stack.full, stack.small) Response: Loss Terms Df RobustF Pr(F) [1,] 1 1 [2,] 2 1 1 27.354 9.839e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > > > cleanEx() > nameEx("breslow.dat") > ### * breslow.dat > > flush(stderr()); flush(stdout()) > > ### Name: breslow.dat > ### Title: Breslow Data > ### Aliases: breslow.dat > ### Keywords: datasets > > ### ** Examples > > data(breslow.dat) > > > > cleanEx() > nameEx("covClassic") > ### * covClassic > > flush(stderr()); flush(stdout()) > > ### Name: covClassic > ### Title: Classical Covariance Estimation > ### Aliases: covClassic > ### Keywords: robust multivariate > > ### ** Examples > > data(stack.dat) > covClassic(stack.dat) Call: covClassic(data = stack.dat) Classical Estimate of Covariance: Loss Air.Flow Water.Temp Acid.Conc. Loss 103.46 85.76 28.148 21.793 Air.Flow 85.76 84.06 22.657 24.571 Water.Temp 28.15 22.66 9.990 6.621 Acid.Conc. 21.79 24.57 6.621 28.714 Classical Estimate of Location: Loss Air.Flow Water.Temp Acid.Conc. 17.52 60.43 21.10 86.29 > > > > cleanEx() > nameEx("covRob") > ### * covRob > > flush(stderr()); flush(stdout()) > > ### Name: covRob > ### Title: Robust Covariance/Correlation Matrix Estimation > ### Aliases: covRob > ### Keywords: multivariate robust > > ### ** Examples > > data(stackloss) > covRob(stackloss) Call: covRob(data = stackloss) Robust Estimate of Covariance: Air.Flow Water.Temp Acid.Conc. stack.loss Air.Flow 33.93 11.203 22.135 29.41 Water.Temp 11.20 8.298 8.794 12.03 Acid.Conc. 22.14 8.794 37.887 17.60 stack.loss 29.41 12.030 17.605 28.17 Robust Estimate of Location: Air.Flow Water.Temp Acid.Conc. stack.loss 56.92 20.43 86.29 13.73 > > > > cleanEx() > nameEx("covRob.control") > ### * covRob.control > > flush(stderr()); flush(stdout()) > > ### Name: covRob.control > ### Title: Control Parameters for Robust Covariance Estimation > ### Aliases: covRob.control > ### Keywords: utilities > > ### ** Examples > > mcd.control <- covRob.control("mcd", quan = 0.75, ntrial = 1000) > > ds.control <- covRob.control("donostah", prob = 0.95) > > qc.control <- covRob.control("pairwiseqc") > > > > cleanEx() > nameEx("ddPlot.covfm") > ### * ddPlot.covfm > > flush(stderr()); flush(stdout()) > > ### Name: ddPlot.covfm > ### Title: Distance - Distance Plot > ### Aliases: ddPlot.covfm > ### Keywords: hplot > > ### ** Examples > > data(woodmod.dat) > woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"), + data = woodmod.dat) > ddPlot.covfm(woodm.fm, main = "Plot Title", xlab = "x-axis label", + ylab = "y-axis label", pch = 4, col = "purple") > > > > cleanEx() > nameEx("distancePlot.covfm") > ### * distancePlot.covfm > > flush(stderr()); flush(stdout()) > > ### Name: distancePlot.covfm > ### Title: Side-by-Side Mahalanobis Distance Plot > ### Aliases: distancePlot.covfm > ### Keywords: hplot > > ### ** Examples > > data(woodmod.dat) > woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"), + data = woodmod.dat) > distancePlot.covfm(woodm.fm, main = "Plot Title", xlab = "x-axis label", + ylab = "y-axis label", pch = 4, col = "purple") > > > > cleanEx() > nameEx("drop1.lmRob") > ### * drop1.lmRob > > flush(stderr()); flush(stdout()) > > ### Name: drop1.lmRob > ### Title: Compute an Anova Object by Dropping Terms > ### Aliases: drop1.lmRob > ### Keywords: robust regression methods > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > drop1(stack.rob) Single term deletions Model: Loss ~ Air.Flow + Water.Temp + Acid.Conc. scale: 1.837073 Df RFPE 16.032 Air.Flow 1 36.213 Water.Temp 1 20.829 Acid.Conc. 1 16.049 > > > > cleanEx() > nameEx("ellipsesPlot.covfm") > ### * ellipsesPlot.covfm > > flush(stderr()); flush(stdout()) > > ### Name: ellipsesPlot.covfm > ### Title: Ellipses Plot - Visual Correlation Matrix Comparison > ### Aliases: ellipsesPlot.covfm > ### Keywords: hplot > > ### ** Examples > > data(woodmod.dat) > woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"), + data = woodmod.dat) > ellipsesPlot.covfm(woodm.fm) > > > > cleanEx() > nameEx("glmRob") > ### * glmRob > > flush(stderr()); flush(stdout()) > > ### Name: glmRob > ### Title: Fit a Robust Generalized Linear Model > ### Aliases: glmRob > ### Keywords: robust regression models > > ### ** Examples > > data(breslow.dat) > > glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(), + data = breslow.dat, method = "cubif") Call: glmRob(formula = sumY ~ Age10 + Base4 * Trt, family = poisson(), data = breslow.dat, method = "cubif") Coefficients: (Intercept) Age10 Base4 Trtprogabide 1.83516 0.12081 0.13915 -0.39279 Base4:Trtprogabide 0.02182 Degrees of Freedom: 59 Total; 54 Residual Residual Deviance: 3888 > > > > cleanEx() > nameEx("glmRob.mallows") > ### * glmRob.mallows > > flush(stderr()); flush(stdout()) > > ### Name: glmRob.mallows > ### Title: Mallows Type Estimator > ### Aliases: glmRob.mallows > ### Keywords: robust regression > > ### ** Examples > > data(mallows.dat) > > glmRob(y ~ a + b + c, data = mallows.dat, family = binomial(), method = 'mallows') Call: glmRob(formula = y ~ a + b + c, family = binomial(), data = mallows.dat, method = "mallows") Coefficients: (Intercept) a b c -1.3214 -0.9052 -0.7435 -1.0201 Degrees of Freedom: 70 Total; 58 Residual Residual Deviance: 27.04 > > > > cleanEx() > nameEx("glmRob.misclass") > ### * glmRob.misclass > > flush(stderr()); flush(stdout()) > > ### Name: glmRob.misclass > ### Title: Consistent Misclassification Estimator > ### Aliases: glmRob.misclass > ### Keywords: robust regression > > ### ** Examples > > data(leuk.dat) > > glmRob(y ~ ag + wbc, data = leuk.dat, family = binomial(), method = 'misclass') Call: glmRob(formula = y ~ ag + wbc, family = binomial(), data = leuk.dat, method = "misclass") Coefficients: (Intercept) ag wbc -1.265e+00 2.219e+00 -3.276e-05 Degrees of Freedom: 33 Total; 30 Residual Residual Deviance: 29.59 > > > > cleanEx() > nameEx("leuk.dat") > ### * leuk.dat > > flush(stderr()); flush(stdout()) > > ### Name: leuk.dat > ### Title: Leuk Data > ### Aliases: leuk.dat > ### Keywords: datasets > > ### ** Examples > > data(leuk.dat) > > > > cleanEx() > nameEx("lmRob") > ### * lmRob > > flush(stderr()); flush(stdout()) > > ### Name: lmRob > ### Title: High Breakdown and High Efficiency Robust Linear Regression > ### Aliases: lmRob > ### Keywords: robust regression models > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > > > > cleanEx() > nameEx("lmRob.RFPE") > ### * lmRob.RFPE > > flush(stderr()); flush(stdout()) > > ### Name: lmRob.RFPE > ### Title: Robust Final Prediction Errors > ### Aliases: lmRob.RFPE > ### Keywords: robust regression > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > lmRob.RFPE(stack.rob) [1] 16.03201 > > > > cleanEx() > nameEx("lmRob.control") > ### * lmRob.control > > flush(stderr()); flush(stdout()) > > ### Name: lmRob.control > ### Title: Control Parameters for Robust Linear Regression > ### Aliases: lmRob.control > ### Keywords: robust regression > > ### ** Examples > > data(stack.dat) > my.control <- lmRob.control(weight=c("Bisquare","Optimal")) > stack.bo <- lmRob(Loss ~ ., data = stack.dat, control = my.control) > > > > cleanEx() > nameEx("lsRobTest") > ### * lsRobTest > > flush(stderr()); flush(stdout()) > > ### Name: lsRobTest > ### Title: Bias Test for Least-Squares Regression Estimates > ### Aliases: lsRobTest > ### Keywords: robust regression > > ### ** Examples > > rob.fit <- lmRob(stack.loss ~ ., data = stackloss) > lsRobTest(rob.fit) Test for least squares bias H0: composite normal/non-normal regression error distribution Individual coefficient tests: LS Robust Delta Std. Error Stat p-value Air.Flow 0.7156 0.79769 -0.08205 0.1353 -0.6064 0.54427 Water.Temp 1.2953 0.57734 0.71795 0.3366 2.1332 0.03291 Acid.Conc. -0.1521 -0.06706 -0.08506 0.1200 -0.7091 0.47824 Joint test for bias: Test statistic: 6.61 on 3 DF, p-value: 0.08541 > lsRobTest(rob.fit, test = "T1") Test for least squares bias H0: normal regression error distribution Individual coefficient tests: LS Robust Delta Std. Error Stat p-value Air.Flow 0.7156 0.79769 -0.08205 0.02101 -3.906 9.388e-05 Water.Temp 1.2953 0.57734 0.71795 0.05225 13.741 5.764e-43 Acid.Conc. -0.1521 -0.06706 -0.08506 0.01862 -4.568 4.928e-06 Joint test for bias: Test statistic: 274.3 on 3 DF, p-value: 0 > > > > cleanEx() > nameEx("mallows.dat") > ### * mallows.dat > > flush(stderr()); flush(stdout()) > > ### Name: mallows.dat > ### Title: Mallows Data > ### Aliases: mallows.dat > ### Keywords: datasets > > ### ** Examples > > data(mallows.dat) > > > > cleanEx() > nameEx("plot.covfm") > ### * plot.covfm > > flush(stderr()); flush(stdout()) > > ### Name: plot.covfm > ### Title: Plot Method > ### Aliases: plot.covfm plot.covRob plot.covClassic > ### Keywords: methods hplot > > ### ** Examples > > data(woodmod.dat) > > woodm.cov <- covClassic(woodmod.dat) > woodm.covRob <- covRob(woodmod.dat) > > plot(woodm.cov) > plot(woodm.covRob) > > woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"), + data = woodmod.dat) > plot(woodm.fm) > > > > cleanEx() > nameEx("plot.fdfm") > ### * plot.fdfm > > flush(stderr()); flush(stdout()) > > ### Name: plot.fdfm > ### Title: fdfm Plot Method > ### Aliases: plot.fdfm > ### Keywords: hplot > > ### ** Examples > > data(los, package = "robustbase") > los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"), + x = los, densfun = "gamma") > plot(los.fm) > > > > cleanEx() > nameEx("plot.lmRob") > ### * plot.lmRob > > flush(stderr()); flush(stdout()) > > ### Name: plot.lmRob > ### Title: Diagnostic Regression Plots > ### Aliases: plot.lmRob > ### Keywords: methods hplot > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > plot(stack.rob, which.plots = 6) > > > > cleanEx() > nameEx("predict.glmRob") > ### * predict.glmRob > > flush(stderr()); flush(stdout()) > > ### Name: predict.glmRob > ### Title: Predict Method for Robust Generalized Linear Model Fits > ### Aliases: predict.glmRob > ### Keywords: robust regression methods > > ### ** Examples > > data(breslow.dat) > bres.rob <- glmRob(sumY ~ Age10 + Base4 * Trt, family = poisson(), data = breslow.dat) > predict(bres.rob) 1 2 3 4 5 6 7 8 2.592342 2.580261 2.345916 2.548386 4.396928 3.124781 2.627130 4.151528 9 10 11 12 13 14 15 16 3.082281 2.521311 4.079040 3.273100 2.739205 3.731164 5.175793 3.888652 17 18 19 20 21 22 23 24 2.799611 6.071102 2.847937 2.784617 2.602967 2.401954 2.813149 3.111243 25 26 27 28 29 30 31 32 4.110915 2.631499 2.412579 3.735964 4.718229 3.358170 2.448596 2.207231 33 34 35 36 37 38 39 40 2.424433 2.698131 3.052314 2.428606 2.211229 4.380213 3.358083 2.062342 41 42 43 44 45 46 47 48 2.605566 2.448770 3.692188 3.144792 3.394414 2.026098 3.205198 2.187066 49 50 51 52 53 54 55 56 7.784706 2.714298 3.394327 3.152962 3.949632 2.903514 2.472846 2.641810 57 58 59 2.702129 2.400445 2.372284 > > > > cleanEx() > nameEx("predict.lmRob") > ### * predict.lmRob > > flush(stderr()); flush(stdout()) > > ### Name: predict.lmRob > ### Title: Use predict() on an lmRob Object > ### Aliases: predict.lmRob > ### Keywords: robust regression methods > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > predict(stack.rob) 1 2 3 4 5 6 7 8 35.782223 35.849283 30.572054 19.825981 18.671300 19.248641 19.423620 19.423620 9 10 11 12 13 14 15 16 16.057899 13.640618 13.037076 12.526796 13.506497 13.346176 6.655592 6.856772 17 18 19 20 21 8.372955 7.903534 8.413814 13.065807 23.629863 > predict(stack.rob, newdata = stack.dat[c(1,2,4,21), ], se.fit = TRUE) $fit 1 2 4 21 35.78222 35.84928 19.82598 23.62986 $se.fit 1 2 4 21 1.0869527 1.1059685 0.5557136 0.9526912 $residual.scale [1] 1.837073 $df [1] 17 > > > > cleanEx() > nameEx("qqPlot.fdfm") > ### * qqPlot.fdfm > > flush(stderr()); flush(stdout()) > > ### Name: qqPlot.fdfm > ### Title: Comparison Quantile-Quantile Plot > ### Aliases: qqPlot.fdfm > ### Keywords: hplot > > ### ** Examples > > data(los, package = "robustbase") > los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"), + x = los, densfun = "gamma") > qqPlot.fdfm(los.fm, xlab = "x-axis label", ylab = "y-axis label", + main = "Plot Title", pch = 4, col = "purple") > > > > cleanEx() > nameEx("screePlot.covfm") > ### * screePlot.covfm > > flush(stderr()); flush(stdout()) > > ### Name: screePlot.covfm > ### Title: Comparison Screeplot > ### Aliases: screePlot.covfm > ### Keywords: hplot > > ### ** Examples > > data(woodmod.dat) > woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"), + data = woodmod.dat) > screePlot.covfm(woodm.fm, main = "Plot Title", xlab = "x-axis label", + ylab = "y-axis label", pch = 4:5) > > > > cleanEx() > nameEx("stack.dat") > ### * stack.dat > > flush(stderr()); flush(stdout()) > > ### Name: stack.dat > ### Title: Brownlee's Stack-Loss Data > ### Aliases: stack.dat > ### Keywords: datasets > > ### ** Examples > > data(stack.dat) > stack.dat Loss Air.Flow Water.Temp Acid.Conc. 1 42 80 27 89 2 37 80 27 88 3 37 75 25 90 4 28 62 24 87 5 18 62 22 87 6 18 62 23 87 7 19 62 24 93 8 20 62 24 93 9 15 58 23 87 10 14 58 18 80 11 14 58 18 89 12 13 58 17 88 13 11 58 18 82 14 12 58 19 93 15 8 50 18 89 16 7 50 18 86 17 8 50 19 72 18 8 50 19 79 19 9 50 20 80 20 15 56 20 82 21 15 70 20 91 > > > > cleanEx() > nameEx("step.lmRob") > ### * step.lmRob > > flush(stderr()); flush(stdout()) > > ### Name: step.lmRob > ### Title: Build a Model in a Stepwise Fashion > ### Aliases: step.lmRob > ### Keywords: robust regression methods > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > > ## The default behavior is to try dropping all terms ## > step.lmRob(stack.rob) Start: RFPE= 16.032 Loss ~ Air.Flow + Water.Temp + Acid.Conc. Single term deletions Model: Loss ~ Air.Flow + Water.Temp + Acid.Conc. scale: 1.837073 Df RFPE 16.032 Air.Flow 1 36.213 Water.Temp 1 20.829 Acid.Conc. 1 16.049 Call: lmRob(formula = Loss ~ ., data = stack.dat) Coefficients: (Intercept) Air.Flow Water.Temp Acid.Conc. -37.65246 0.79769 0.57734 -0.06706 > > ## Keep Water.Temp in the model ## > my.scope <- list(lower = . ~ Water.Temp, upper = . ~ .) > step.lmRob(stack.rob, scope = my.scope) Start: RFPE= 16.032 Loss ~ Air.Flow + Water.Temp + Acid.Conc. Single term deletions Model: Loss ~ Air.Flow + Water.Temp + Acid.Conc. scale: 1.837073 Df RFPE 16.032 Air.Flow 1 36.213 Acid.Conc. 1 16.049 Call: lmRob(formula = Loss ~ ., data = stack.dat) Coefficients: (Intercept) Air.Flow Water.Temp Acid.Conc. -37.65246 0.79769 0.57734 -0.06706 > > > > cleanEx() > nameEx("summary.covfm") > ### * summary.covfm > > flush(stderr()); flush(stdout()) > > ### Name: summary.covfm > ### Title: Summary Method > ### Aliases: summary.covClassic summary.covRob summary.covfm > ### Keywords: methods > > ### ** Examples > > data(woodmod.dat) > woodm.cov <- covClassic(woodmod.dat) > ## IGNORE_RDIFF_BEGIN > summary(woodm.cov) Call: covClassic(data = woodmod.dat) Classical Estimate of Covariance: V1 V2 V3 V4 V5 V1 0.0082920 -0.0002912 0.003602 0.0026908 -0.0028684 V2 -0.0002912 0.0004888 -0.000352 -0.0008388 0.0006124 V3 0.0036022 -0.0003520 0.004185 0.0015788 -0.0016916 V4 0.0026908 -0.0008388 0.001579 0.0039462 -0.0007920 V5 -0.0028684 0.0006124 -0.001692 -0.0007920 0.0027570 Classical Estimate of Location: V1 V2 V3 V4 V5 0.5508 0.1330 0.5087 0.5112 0.9070 Eigenvalues: Eval. 1 Eval. 2 Eval. 3 Eval. 4 Eval. 5 0.0128527 0.0029621 0.0021125 0.0016344 0.0001075 Squared Mahalanobis Distances: 1 2 3 4 5 6 7 8 9 10 11 12 13 4.327 1.552 3.224 3.959 3.277 3.974 9.124 4.536 5.665 7.588 5.075 6.833 4.506 14 15 16 17 18 19 20 1.500 1.945 9.049 4.548 4.637 4.599 5.084 > ## IGNORE_RDIFF_END > > woodm.covRob <- covRob(woodmod.dat) > summary(woodm.covRob) Call: covRob(data = woodmod.dat) Robust Estimate of Covariance: V1 V2 V3 V4 V5 V1 0.038232 0.0066282 -0.0021650 -0.0015136 -0.0048570 V2 0.006628 0.0016512 0.0001382 -0.0010400 -0.0003837 V3 -0.002165 0.0001382 0.0036709 0.0001514 0.0015113 V4 -0.001514 -0.0010400 0.0001514 0.0048313 -0.0014409 V5 -0.004857 -0.0003837 0.0015113 -0.0014409 0.0044166 Robust Estimate of Location: V1 V2 V3 V4 V5 0.5693 0.1189 0.5093 0.5399 0.8964 Eigenvalues: Eval. 1 Eval. 2 Eval. 3 Eval. 4 Eval. 5 0.0402612 0.0063495 0.0039998 0.0019171 0.0002747 Squared Robust Distances: [1] 1.2996 0.3348 0.4099 15.8192 0.4578 18.0052 8.5876 24.2857 8.1617 [10] 5.1665 2.0412 5.3157 5.7099 0.2672 0.3173 5.5845 3.4097 0.4362 [19] 25.9520 3.5364 > > woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"), + data = woodmod.dat) > summary(woodm.fm) Calls: Robust: covRob(data = woodmod.dat) Classical: covClassic(data = woodmod.dat) Comparison of Covariance/Correlation Estimates: (unique correlation terms) [1,1] [2,1] [3,1] [4,1] [5,1] [2,2] Robust 0.038232 0.0066282 -0.002165 -0.001514 -0.004857 0.0016512 Classical 0.008292 -0.0002912 0.003602 0.002691 -0.002868 0.0004888 [3,2] [4,2] [5,2] [3,3] [4,3] [5,3] Robust 0.0001382 -0.0010400 -0.0003837 0.003671 0.0001514 0.001511 Classical -0.0003520 -0.0008388 0.0006124 0.004185 0.0015788 -0.001692 [4,4] [5,4] [5,5] Robust 0.004831 -0.001441 0.004417 Classical 0.003946 -0.000792 0.002757 Comparison of center Estimates: V1 V2 V3 V4 V5 Robust 0.5693 0.1189 0.5093 0.5399 0.8964 Classical 0.5508 0.1330 0.5087 0.5112 0.9070 Comparison of Eigenvalues: Eval. 1 Eval. 2 Eval. 3 Eval. 4 Eval. 5 Robust 0.04026 0.006349 0.004000 0.001917 0.0002747 Classical 0.01285 0.002962 0.002112 0.001634 0.0001075 > > > > cleanEx() > nameEx("summary.glmRob") > ### * summary.glmRob > > flush(stderr()); flush(stdout()) > > ### Name: summary.glmRob > ### Title: Summarizing Robust Generalized Linear Model Fits > ### Aliases: summary.glmRob > ### Keywords: methods robust regression > > ### ** Examples > > data(breslow.dat) > bres.rob <- glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(), data = breslow.dat) > bres.sum <- summary(bres.rob) > bres.sum Call: glmRob(formula = sumY ~ Age10 + Base4 * Trt, family = poisson(), data = breslow.dat) Deviance Residuals: Min 1Q Median 3Q Max -54.31624 -1.48734 0.04103 0.87948 8.92507 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.83516 0.28542 6.4296 1.279e-10 Age10 0.12081 0.07495 1.6118 1.070e-01 Base4 0.13915 0.03541 3.9298 8.501e-05 Trtprogabide -0.39279 0.22101 -1.7772 7.554e-02 Base4:Trtprogabide 0.02182 0.04003 0.5451 5.857e-01 (Dispersion Parameter for poisson family taken to be 1 ) Null Deviance: 11983 on 58 degrees of freedom Residual Deviance: 3888.204 on 54 degrees of freedom Number of Iterations: 9 Correlation of Coefficients: (Intercept) Age10 Base4 Trtprogabide Age10 -0.80956 Base4 -0.62030 0.10855 Trtprogabide -0.46447 0.02404 0.69012 Base4:Trtprogabide 0.52264 -0.06402 -0.88082 -0.89436 > > > > cleanEx() > nameEx("summary.lmRob") > ### * summary.lmRob > > flush(stderr()); flush(stdout()) > > ### Name: summary.lmRob > ### Title: Summarizing Robust Linear Model Fits > ### Aliases: summary.lmRob > ### Keywords: methods robust regression > > ### ** Examples > > data(stack.dat) > stack.rob <- lmRob(Loss ~ ., data = stack.dat) > stack.sum <- summary(stack.rob) > stack.sum Call: lmRob(formula = Loss ~ ., data = stack.dat) Residuals: Min 1Q Median 3Q Max -8.6299 -0.6713 0.3594 1.1507 8.1740 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -37.65246 5.00256 -7.527 8.29e-07 *** Air.Flow 0.79769 0.07129 11.189 2.91e-09 *** Water.Temp 0.57734 0.17546 3.291 0.00432 ** Acid.Conc. -0.06706 0.06512 -1.030 0.31757 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.837 on 17 degrees of freedom Multiple R-Squared: 0.6205 Test for Bias: statistic p-value M-estimate 2.751 0.6004 LS-estimate 2.640 0.6197 > stack.bse <- summary(stack.rob, bootstrap.se = TRUE) > stack.bse Call: lmRob(formula = Loss ~ ., data = stack.dat) Residuals: Min 1Q Median 3Q Max -8.6299 -0.6713 0.3594 1.1507 8.1740 Coefficients: Estimate Std. Error Bootstrap SE t value Pr(>|t|) (Intercept) -37.65246 5.00256 4.43790 -7.527 8.29e-07 *** Air.Flow 0.79769 0.07129 0.05086 11.189 2.91e-09 *** Water.Temp 0.57734 0.17546 0.13551 3.291 0.00432 ** Acid.Conc. -0.06706 0.06512 0.05842 -1.030 0.31757 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.837 on 17 degrees of freedom Multiple R-Squared: 0.6205 Test for Bias: statistic p-value M-estimate 2.751 0.6004 LS-estimate 2.640 0.6197 > > > > cleanEx() > nameEx("weight.funs") > ### * weight.funs > > flush(stderr()); flush(stdout()) > > ### Name: weight.funs > ### Title: Weight Functions Psi, Rho, Chi > ### Aliases: psi.weight rho.weight psp.weight chi.weight > ### Keywords: robust > > ### ** Examples > > x <- seq(-4,4, length=401) > f.x <- cbind(psi = psi.weight(x), psp = psp.weight(x), + chi = chi.weight(x), rho = rho.weight(x)) > es <- expression(psi(x), {psi*minute}(x), chi(x), rho(x)) > leg <- as.expression(lapply(seq_along(es), function(i) + substitute(C == E, list(C=colnames(f.x)[i], E=es[[i]])))) > matplot(x, f.x, type = "l", lwd = 1.5, + main = "psi.weight(.) etc -- 'optimal'") > abline(h = 0, v = 0, lwd = 2, col = "#D3D3D380") # opaque gray > legend("bottom", leg, inset = .01, + lty = 1:4, col = 1:4, lwd = 1.5, bg = "#FFFFFFC0") > > > > cleanEx() > nameEx("woodmod.dat") > ### * woodmod.dat > > flush(stderr()); flush(stdout()) > > ### Name: woodmod.dat > ### Title: Modified Wood Data > ### Aliases: woodmod.dat > ### Keywords: datasets > > ### ** Examples > > data(woodmod.dat) > woodmod.dat V1 V2 V3 V4 V5 1 0.573 0.1059 0.465 0.538 0.841 2 0.651 0.1356 0.527 0.545 0.887 3 0.606 0.1273 0.494 0.521 0.920 4 0.437 0.1591 0.446 0.423 0.992 5 0.547 0.1135 0.531 0.519 0.915 6 0.444 0.1628 0.429 0.411 0.984 7 0.489 0.1231 0.562 0.455 0.824 8 0.413 0.1673 0.418 0.430 0.978 9 0.536 0.1182 0.592 0.464 0.854 10 0.685 0.1564 0.631 0.564 0.914 11 0.664 0.1588 0.506 0.481 0.867 12 0.703 0.1335 0.519 0.484 0.812 13 0.653 0.1395 0.625 0.519 0.892 14 0.586 0.1114 0.505 0.565 0.889 15 0.534 0.1143 0.521 0.570 0.889 16 0.523 0.1320 0.505 0.612 0.919 17 0.580 0.1249 0.546 0.608 0.954 18 0.448 0.1028 0.522 0.534 0.918 19 0.417 0.1687 0.405 0.415 0.981 20 0.528 0.1057 0.424 0.566 0.909 > > data(wood, package = "robustbase") > stopifnot(data.matrix(woodmod.dat) == + data.matrix(wood [,1:5])) > > > > ### *