lasso2/0000755000176200001440000000000014127776332011471 5ustar liggesuserslasso2/NAMESPACE0000644000176200001440000000212413377173515012707 0ustar liggesusersuseDynLib(lasso2) export(gcv) export(gl1ce) export(l1ce) export(qr.rtr.inv) export(tr) S3method(gcv, l1ce) S3method(gcv, l1celist) importFrom(stats, coef, deviance, fitted, predict, residuals, vcov, family) S3method(coef, l1ce) S3method(coef, l1celist) S3method(deviance, l1ce) S3method(deviance, l1celist) S3method(deviance, gl1ce) S3method(family, gl1ce) S3method(fitted, l1ce) S3method(fitted, l1celist) S3method(predict, l1ce) S3method(predict, gl1ce) S3method(residuals, l1ce) S3method(residuals, l1celist) S3method(residuals, gl1ce) S3method(vcov, l1ce) S3method(vcov, l1celist) importFrom(graphics, plot) S3method(plot, l1celist) ## in base S3method(labels, l1ce) S3method(labels, l1celist) S3method(print, l1ce) S3method(print, l1celist) S3method(print, gl1ce) S3method(summary, l1ce) S3method(summary, gl1ce) S3method("[", l1celist) S3method("[[", l1celist) S3method(merge, formula) importFrom("graphics", "matplot", "rug") importFrom("stats", "as.formula", "gaussian", "glm.control", "model.extract", "model.matrix", "pnorm", "quantile", "residuals.glm", "sd", "terms") lasso2/README0000644000176200001440000000131307430272324012336 0ustar liggesusersR package "lasso2" ================== I have ported these from the Dec.1999 version as available ( for S-plus ) at http://www.maths.uwa.edu.au/~berwin/software/lasso.html in order to use them for R. Berwin Turlach will probably take over maintenance of this package after while since "lasso2" is one of his ongoing research projects. See also ./MM-NOTES ./ChangeLog Feedback (by E-mail to me and or Berwin) is very welcome! -------- Zurich, Feb.6, 2002 Martin Maechler http://stat.ethz.ch/~maechler/ Seminar fuer Statistik, ETH-Zentrum LEO C16 Leonhardstr. 27 ETH (Federal Inst. Technology) 8092 Zurich SWITZERLAND phone: x-41-1-632-3408 fax: ...-1228 <>< lasso2/GPL-20000644000176200001440000004310311273015516012163 0ustar liggesusers GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. 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If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. 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If this is what you want to do, use the GNU Lesser General Public License instead of this License. lasso2/ChangeLog0000644000176200001440000004616714127632775013263 0ustar liggesusers2021-10-08 Berwin A Turlach * DESCRIPTION: New version is 1.2-22 * src/lasso.c, and src/lasso.h: removed dependence on , mainly by copying the macros that are used into lasso.h and prefixing everything with "LASSO2_" 2013-10-31 Berwin A Turlach * DESCRIPTION: New version is 1.2-18 * moved COPYRIGHT to inst/COPYRIGHTS as suggested by BDR * renamed LICENCE.GPL-[23] to GPL-[23] as suggested by BDR * use .Rbuildignore to remove some files from the build, as suggested by BDR. * DESCRIPTION: New version is 1.2-17 * man/residuals.l1ce.Rd: Removed link to residuals.default (as required by newer versions of R) * moved vignette to vignettes/ subdirectory. 2013-04-19 Berwin A Turlach * tests/l1ce-tst.R: Changed lowest bound to 1e-5 in two places to avoid problems with valgrind. * DESCRIPTION: New version is 1.2-16 * R/gl1ce.q: Followed Brian's advice to use utils::globalVariables to make codetools happy regarding dev.res and mustart. * src/lasso.h: removed typedef declaration in USING_R conditional as is included there now (which contains the typedef) 2013-04-18 Berwin A Turlach * R/gl1ce.q: Added "dev.res <- NULL" and "mustart <- NULL" at strategic places to make codetools happy. 2013-04-17 Berwin A Turlach * DESCRIPTION (Date): New version is 1.2-15 * NAMESPACE: Added S3mehod() instructions for methods for gcv, methods do not seem to be exported automatically with generic anymore since 2.15.3 2012-11-20 Berwin A Turlach * DESCRIPTION (Version): New version is 1.2-14 * NAMESPACE: added NAMESPACE * R/.First.lib.q: renamed to zzz.q, changed from .First.lib() to .onAttach(), also using packageStartupMessage() now. * R/l1ce.q, R/gl1ce.q: spelled formal arguments out to avoid notes about partial matching during check. * src/lasso.h: changed definition of Sprintf to be unconditional as "R CMD check" otherwise complains. 2011-05-12 Berwin A Turlach * DESCRIPTION (Version): New version is 1.2-12 * COPYRIGHT: Changed license to GPL >= 2 to conform with information in DESCRIPTION. * Changed e-mail address to Gmail e-mail in various files. 2010-04-12 Berwin A Turlach * DESCRIPTION (Version): New version is 1.2-11 * DESCRIPTION (Maintainer): Changed e-mail address from former NUS e-mail to UWA e-mail. * man/gl1ce.Rd: updated link from [base]{glm.control} to [stats]{glm.control} 2009-01-26 Berwin A Turlach * Added check in l1ce() and gl1ce() whether matrix passed to C routine contains only constant column to avoid attempts for fitting non-sensical models. * Corrected errors in gl1ce.object.Rd and l1celist.object.Rd found by now Rd parser of (future) R-2.9.0. 2008-09-11 Berwin A Turlach * Removed the Makefile from inst/doc since I found a "pure" LaTeX way to check for missing PDFs that are to be included with \includepdf. 2008-09-08 Berwin A Turlach * Added a Makefile to inst/doc since "R CMD check" does not behave in documented way, it actually tries to build the vignette PDF. 2008-09-07 Berwin A Turlach * Various .Rd files contained an unmatched right brace. Fixed those files. Reported by Tim Hesterberg . 2007-09-30 Berwin A Turlach * Updated various .Rd files to cope with changes in R-2.6.0 * Updated some .q files to cope with changes in R-2.6.0 2006-01-06 Berwin A Turlach * DESCRIPTION (Version, Date, Maintainer) * man/predict.gl1ce.Rd: cope with change to all.equal() in R-2.3.x 2005-06-10 Martin Maechler * DESCRIPTION (Version, Date): 1.2-2 * src/lasso.c (lasso): indent and use spaces "properly". * R/gl1ce.q (gl1ce): use eval(m, parent.frame()) * R/l1ce.q (l1ce): instead of eval(m, data) * R/l1ce.q (l1ce): warn about rank only when p <= n 2005-02-11 Martin Maechler * R/l1ce.q (l1ce): improve error message for 'constant column' 2004-04-13 Martin Maechler * DESCRIPTION (Version, Date): 1.2-0 * R/l1ce.q: fix intercepts for multi-bound situation (reported by Martin Keller-Ressel). * tests/l1ce-tst.Rout.save: modified correspondingly and for : * tests/l1ce-tst.R: new consistency test (for the above problem) 2004-02-05 Martin Maechler * DESCRIPTION (Version): 1.1-0 * R/summary.q (summary.gl1ce): $coefficients should be "usual" matrix, not a 1-dim array. {The docs do not talk about it.} * R/print.q (print.summary.gl1ce): coefficients; also visualize '0'! * R/predict.q (predict.gl1ce): type = "response": fix R port * R/gl1ce.q (family.gl1ce): new method * man/gl1ce.Rd: and docu * man/predict.gl1ce.Rd: add \examples{} * man/residuals.gl1ce.Rd: \seealso{} + cosmetic 2004-02-05 Berwin Turlach * R/gl1ce.q (gl1ce): bug fixes (3 places), particularly fitted values. 2003-12-29 Martin Maechler * DESCRIPTION (Version): 1.0-4 * src/lasso.c : get rid of 3 minor compiler warnings * man/coef.l1ce.Rd: do not \link{coef.default} 2003-12-15 Martin Maechler * R/formula.util.q (is.gEnv): new function, needed for testing about .GlobalEnv {gave wrong warnings}. * tests/l1ce-tst.R: RNGversion("1.6.0") added for back compatibility * man/predict.l1ce.Rd: now the newdata works, add example * man/summary.l1ce.Rd: usage for print.summary.l1ce * man/...: a few more such changes * R/gl1ce.q (gl1ce): PACKAGE = "lasso2" was missing * R/predict.q (predict.l1ce): use "term.labels", not "variables" (fix by Berwin himself!). 2003-07-09 Martin Maechler * R/formula.util.q (merge.formula): keep formula environment (and warn if differing). * man/merge.formula.Rd: add example 2003-03-22 Martin Maechler * R/vcov.q (vcov): generic only for older R versions 2002-04-09 Martin Maechler * release 1.0-2 to CRAN * man/l1ce.Rd: correct reference to JCGS paper * man/gl1ce.Rd: link to Berwin's website mentioning Justin Lokhorst's honors report. * inst/doc/justin.lokhorst.ps: is now removed * inst/doc/Manual-wo-help.pdf: instead of ps. * man/qr.rtr.inv.Rd: new file documenting qr.rtr.inv() 2002-02-25 Martin Maechler * man/zaux.Rd and zaux.l1celist.Rd : renamed from aux.* since that is `invalid' on Windows. 2002-02-08 Martin Maechler * DESCRIPTION (Version): 1.0-2 (not submitted; for internal use) * tests/l1ce-tst.R: new file; first testing, also n < p * R/misc.q (aux.l1celist): simplification * R/l1ce.q (l1ce): patch to improve working with rank deficient X e.g. (n < p)! * Changelog: Appended Berwin's S-plus original one. * man/lasso-internal.Rd: added (to get rid of last warning). * man/plot.l1celist.Rd: * R/plot.q: use type = "b" and other more useful default arguments * R/force_ld.q is garbage for R --> rm! 2002-02-06 Martin Maechler * First release to CRAN --------------------- * DESCRIPTION (Version): 1.0-1 (as Berwin explained, "2" is no version number!) * src/lasso.h: define macro Sprintf() = Rprintf() in R, else = printf() * src/lasso.c: s/printf/Sprintf/ * R/gl1ce.q (gl1ce): replace as.family; use is.R() for R differences (such that this still runs in S+ as before!). s/check.vector/Cvector/ {readability}. Many more (using R's glm.fit()) -- now +/- working * R/summary.q (summary.gl1ce): R adaptions * R/print.q (print.summary.gl1ce): R adaptions * man/deviance.l1ce.Rd: and almost all (!!) others: added \keyword{}; added quite a few hyper \link{} s; sometimes merged the almost identical *.l1ce and *.l1celist files; added missing argument lists; .... * R/*.q: generic and methods *must* have maching argument lists! 2002-02-05 Martin Maechler * pre-version (Version): 2.1-0 made available for Berwin to look at. * MM-NOTES: 1) - 7) explaining open questions 2002-01-26 Martin Maechler * DESCRIPTION: a first unreleased version at least runs R CMD check to completion; still many warnings * R/l1ce.q (l1ce): cosmetic; added qr.rtr.inv() {and a note on why I think one should change..} fixed two severe `typos' : 1) single-bound && something.to.sweep.out ("orig" shows) 2) multibound case: `fit' was used before defined ----------------------------------------------------------------------------- ^^^^^^^^^ R Package ----------------------------------------------------------------------------- ----------------------------------------------------------------------------- vvvvvvvvv S-Plus library section ----------------------------------------------------------------------------- Wed Dec 8 23:35:43 1999 Berwin Turlach * First.lib.template: Changed to new release * predict.q: Modified predict.l1ce to support offset for predict.gl1ce. Mimics predict.{lm,glm} behaviour. * l1ce.q, gl1ce.q: Changed some t(foo)%*%bar to crossprod(foo,bar). Fixed a bug that appeared while playing around with update (subscription error when putting end result together). Tue Nov 30 06:31:53 1999 Berwin Turlach * summary.q: Added glm stuff. * summary.gl1ce.d: Initial revision * residuals.q: Added glm stuff. * residuals.gl1ce.d, print.summary.gl1ce.d: Initial revision * print.q: Added glm stuff. * print.gl1ce.d: Initial revision * predict.q: Added glm stuff. * gl1ce.d, gl1ce.object.d, gl1ce.q, predict.gl1ce.d: Initial revision * deviance.q: Added glm stuff. * deviance.gl1ce.d: Initial revision Tue Nov 30 06:15:53 1999 Berwin Turlach * vcov.q, residuals.q: Modified to make port to R easier. * plot.q: Added parameter `constrained'. Modified to make port to R easier. * plot.l1celist.d: Added documentation for additional parameter. * misc.q: Added function `tr'. Modified to make port to R easier. * l1ce.q, gcv.q, fitted.q, deviance.q: Modified to make port to R easier. * coef.q: Added parameter `constrained'. Modified to make port to R easier. * coef.l1celist.d, coef.l1ce.d: Added documentation for additional parameter. * First.lib.template: Changed way of dynamic loading. Fri Dec 18 06:42:48 1998 Berwin Turlach * misc.q: Added tr() command. (Arman Maghbouleh pointed out that it was missing and is not in standard S-Plus.) * tr.d: Initial revision Wed Nov 4 07:50:55 1998 Berwin Turlach * First.lib.template: Changed release number to 1.2 and saved as snapshot. * Finished the documentation and the manual. Sun Sep 27 02:02:02 1998 Berwin Turlach * summary.l1ce.d, l1ce.d, Prostate.d, Prostate.q: Corrected spelling of prostate. Thu Sep 24 10:30:02 1998 Berwin Turlach * First.lib.template: Changed to Release 1.1 and saved as snapshot. * l1ce.q: If the object that is given down to "data" is not a data frame we now try to coerce it into one. Wed Sep 23 10:22:23 1998 Berwin Turlach * l1ce.q: Fixed a bug in l1ce in the case that a vector of bounds is given (coefficients of swept out variables were not handled correctly). Sun Sep 20 09:44:18 1998 Berwin Turlach * First.lib.template: Changed to Release 1.0 and saved as snapshot. * summary.l1ce.d, l1celist.object.d, l1ce.object.d, l1ce.d: Initial revision Fri Sep 18 11:32:58 1998 Berwin Turlach * First.lib.template: Changed to Release 0.12 and saved as snapshot. * [[.l1celist.d, aux.l1celist.d, aux.d, plot.l1celist.d, vcov.l1celist.d, vcov.l1ce.d, vcov.d, residuals.l1celist.d, residuals.l1ce.d, print.summary.l1ce.d, print.l1celist.d, print.l1ce.d, predict.l1ce.d, gcv.l1celist.d, gcv.l1ce.d, gcv.d, formula.l1celist.d, formula.l1ce.d, formula.q, labels.l1celist.d, labels.l1ce.d, labels.q, Prostrate.d, Prostrate.q, Iowa.q, Iowa.d, merge.formula.d, is.formula.d, fitted.l1celist.d, fitted.l1ce.d, deviance.l1celist.d, deviance.l1ce.d, coef.l1celist.d, coef.l1ce.d: Initial revision Tue Sep 15 11:23:42 1998 Berwin Turlach * First.lib.template: Changed to Release 0.11 and saved as snapshot. * l1ce.q: The `contrasts' and `assign' result from model.matrix are now returned as components of l1ce objects and attributes of l1celist objects. (Analogous to what is found in lm.fit). Sun Sep 13 04:08:42 1998 Berwin Turlach * First.lib.template: Changed to Release 0.10 and saved as snapshot. * vcov.q: Added check in `opt.covmat' whether `bound==0'. * summary.q: Programmed and tested `summary.l1ce'. * print.q: Added and tested `print.summary.l1ce'. * gcv.q: Made `gcv' a generic method. Programmed and tested `gcv.l1ce' and `gcv.l1celist'. * summary.q: Initial revision Fri Sep 11 08:55:26 1998 Berwin Turlach * gcv.q: Initial revision Thu Sep 10 10:23:10 1998 Berwin Turlach * First.lib.template: Changed to Release 0.9 and saved as snapshot. * l1ce.q: The terms in the `formula' are calculated and given back too. We need those in the predict function. Minor cosmetic changes to error/warning messages. * misc.q: Corrected bug in transfer from attributes of objects of class l1celist to components of objects of class l1ce. Streamlines the code and works also if new attributes/components are added. * predict.q: Adapted from `predict.mlm'. Programmed and tested. * predict.q: Initial revision Wed Sep 9 11:26:54 1998 Berwin Turlach * First.lib.template: Changed to Release 0.8 and saved as snapshot. * vcov.q: Fixed but in tib.covmat (didn't take absolute values). Fixed typos in vcov.l1ce. Added code that calculates the degrees of freedom by calculating the trace of the "hat" matrix. The parameter s2hat was dropped, not the covaraince matrix is returned in a list as `cov.unscaled' together with the degrees of freedom (`df', vector with two values). Programmed and debugged vcov.l1celist. Tue Sep 8 10:47:42 1998 Berwin Turlach * First.lib.template: Changed to Release 0.7 and saved as snapshot. * vcov.q: Programmed and tested `vcov.l1ce'. Perhaps some thought is still needed for a sensible default for the parameter `s2hat'. * misc.q: Changed `[.l1celist' and `[[.l1celist'' to take into account the changes made to `l1ce' wrt parameters returned. * l1ce.q: Added check whether a dataframe is given down. Added check whether variables are left over after sweeping out. Changed what values are returned, these changes were necessary in order to be able to calculate the covariance matrix of the estimates. Mon Sep 7 07:18:08 1998 Berwin Turlach * First.lib.template: Changed to Release 0.6 and saved as snapshot. * l1ce.q: Changed X.to.C[,-name.matches] to X.to.C[,-name.matches,drop=F] so that the result remains a matrix. Explicitly added as.double() when the guess for the constrained parameter is given down to C. If a vector of constraints is given, then the routine uses now .C("mult_lasso") instead of repeated calls to .C("lasso") (saves initialisation work in the C routine). * deviance.q: Added `deviance.l1celist'. Sat Sep 5 06:49:59 1998 Berwin Turlach * First.lib.template: Changed to Release 0.5 and saved as snapshot. * residuals.q: Added `residuals.l1celist'. * print.q: Added `print.l1celist'. * plot.q: Changed to make use of the methods `aux' and `coef'. * misc.q: Changed `[[.l1celist' such that indexes out of bounds are correctly handled. Added `[.l1celist' and `aux' as generic method. `aux.l1celist' returns a matrix with the bounds (relative and absolute) and the Lagrangians. * l1ce.q: Renamed the parameter `constraint' to `bound'. * fitted.q: Added fitted.l1ce (practically fitted.default) and fitted.l1celist. * coef.q: Added coef.l1celist. * fitted.q: Initial revision Fri Sep 4 11:28:13 1998 Berwin Turlach * First.lib.template: Changed to Release 0.4 and saved as snapshot. * misc.q: First go at the function, adapted from [[.times. * print.q: Added constraint and Lagrangian to output. * misc.q: Initial revision Fri Sep 4 10:30:26 1998 Berwin Turlach * vcov.q: Preparation for implementing the vcov function. Checked in in order to rename all files from *.sf to *.q. * plot.q: First go at plot.l1celist. * l1ce.q: Added the possibility that constraint is a vector. In this case an object of class "l1celist" is returned. * vcov.q, plot.q: Initial revision Mon Aug 31 07:40:46 1998 Berwin Turlach * First.lib.template: Changed to Release 0.3 and saved as snapshot. * l1ce.sf: Renamed a variable from `m' to `p' so that it would not overwrite the variable holding the information about the model. * print.sf: Adapted from printed.lm, programmed and debugged. * print.sf: Initial revision * deviance.sf: Adapted from deviance.lm, programmed and debugged. * l1ce.sf: Renamed some components to have same names as in objects returned by lm. * coef.sf: Renamed beta to coefficients. * deviance.sf: Initial revision * l1ce.sf: Corrected an error in the way the formula were handled (noticable when weights were present). Added heaps of bells and whistles. * residuals.sf: Adapted from residuals.lm, programmed and debugged. Fri Aug 28 08:02:11 1998 Berwin Turlach * residuals.sf: Initial revision Sun Aug 16 02:50:33 1998 Berwin Turlach * First.lib.template: Changed to Release 0.2 and saved as snapshot. * coef.l1ce.sf: Renamed a parameter. * l1ce.sf: Corrected an error in the adjustment of the swept out parameters. Sun Aug 16 02:44:58 1998 Berwin Turlach * formula.util.sf: Corrected merge.formula such that it handles long formulae correctly. (deparse may return a *vector* of strings.) * coef.l1ce.sf: Initial revision Tue Aug 11 06:15:18 1998 Berwin Turlach * l1ce.sf: Complete rewrite to allow that several variables are swept out. * formula.util.sf: Some changes to merge.formula * formula.util.sf: Added merge.formula * formula.util.sf: Initial revision Sun Aug 9 02:57:17 1998 Berwin Turlach * l1ce.sf: A first version. Fri Aug 7 03:19:47 1998 Berwin Turlach * l1ce.sf: Initial revision Fri Aug 7 02:57:41 1998 Berwin Turlach * First.lib.template: Initial revision lasso2/data/0000755000176200001440000000000014127633221012367 5ustar liggesuserslasso2/data/Iowa.tab.gz0000644000176200001440000000141514127633221014376 0ustar liggesusers=TAU1 [T/i@lf,!vWōc;}{Ͼ~oήs:Oio?g>=zY=#l=cX i3Y9lX6Yl1f5*e o|Fͬ>,'yvY Q[7sYI~͇418sYKB@l[-Lʪ͏Y'i*\cwS|r⃆M1!URsDXXP_s1"9$TmYCABv1qiD0xYPA V!L&MG']ăXu`!9嬕hawLR2Jχ+E5\sb`!FЈ)4nۘkfr,;C"-p,qc);(+Iy+N /wH=jJyWi)G;@$ 1(y}c0 c=:`~N!ܺƄx6-pSĹ)zvnHZ:h}oFno_:c9j B 8q-.8YjO n^˱RhN&?0%]vѻ<= M@fY2uQ:mB} J׺zN5bpF ,}$̀!z6eѝ\&4QZ#?\=辝,7bDBW rѯ~2ݏlasso2/data/Prostate.rda0000644000176200001440000000617214127633221014666 0ustar liggesusersYwTOB!Q+GE.`Y5()l*ª(*P,P*HAA!t %uf8 rPwy9'wW~w斱7wraQ(NRh2nOT " @7 E'sO(*, s)ͪmIj۠d/j4[)~TUof_)L*c Uܨ荁iGQоcG%I߳G eYJD"|t fYf;I~,x15Lf@-ɱzF=: hO5b+ii֔(Vuljwdb wZ]`$7g#"nLX=:*ƒɹa.utP!h d4ʆg= Meٯi(lQ~!]S}s7V-j5>5zabQZ&&_U#qV΄[HN=Tv0ȓ=ef}H>ѳԖ0zf'oE {7'P{M-aMItPʰhe٨-"rOe3 ?12:25M,<CE#]^^<;0 {i5+P][ұh7m`G z'2߭h8y8KbP'ekҹ %`yO|(jERC狗[cz-YFwF`tzqLns֮o6X&Py -~r=n:8 Xffs5nc1z|&ǶZ‹@ɳfߨCnjӴ.f#b>>kgjΐ@ Zrʼc8o =:>84Ӽc"`S {o>u5ug0G{Á{rݧn3;l@gít{`Z?Jĕ69e'Ak5yt+Zظ'wشj[yCs/$7HS D\O#l v1>6(48Jx`7\8aSUAIۀ_bC4e* = ūs|)*Mۃm7H1_\_[*LK w򐅖pƴ} N^zgv;4;PSZ|UK_P<4W-D7XŌQ^7 XW D?ɿ02l nGKc~sv?P6V8(zỲ$n~'mEn/Țy|GzE zԗ2Tlrs Yy2/UZ˟=xcDoz/~d%'S~Q48oh+F9>by뉸zd.#"c| Թrv?&j[Uӏ7CC;/ {[/;O1Jݑ\ߧ/|CObq$4Y߽:${ 9ײ‹<7?K>ΉwKɿ%5iz_or9^o@7'p1yIHkU] $@TwiC&d;YӘ9I|/ 'D}OO_Qo"#8~ΩswW9wZ|&."(“x@9X1+u)zn`~)J&\v5m u|8?/C&o w=J-%[O tJ\0Jdo a-sĸF`̍b: uY*ǖj,br<3t_[ܯ r[#.693~N 2Aeہ5a6Jc,bBZ`s EG-U0jD.q~A@w?_V|] hKЅIjo.Tpr\ņϚAvǶ]]ԃWm|a-FFbC R#H0W i%Fy9N^'#\Ǻ/ `_oL^4en-o)^z/mݽ݄>x4?wwq_Lg0Ĕ.og/΄O_LJڲlasso2/man/0000755000176200001440000000000013732326057012240 5ustar liggesuserslasso2/man/zaux.Rd0000644000176200001440000000145007430226240013505 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: aux.d,v 1.1 1998/09/18 11:12:48 bturlach Exp $ % --> ../COPYRIGHT for more details \name{aux} \alias{aux} \title{Extract Auxiliary Information From an Object} \description{ Generic function for extracting auxiliary information from fitted model objects. } \usage{ aux(object, \dots) } \arguments{ \item{object}{fitted model object (here typically of class \code{l1celist}, see \code{\link{aux.l1celist}}).} \item{\dots}{potentilly further arguments passed to methods.} } \value{ a matrix with the bound(s) (relative [if used] and absolute) and the Lagrangian(s) for the fitted model(s). } \details{ See documentation (technical reports). } \keyword{utilities} lasso2/man/fitted.l1ce.Rd0000644000176200001440000000145310677503457014642 0ustar liggesusers\name{fitted.l1ce} \alias{fitted.l1ce} \alias{fitted.l1celist} \title{Fitted Values for `l1ce', `l1celist' and `gl1ce' Objects} \description{ These are methods of the generic function \code{\link{fitted}()} for objects inheriting from class \code{l1ce} or \code{l1celist} (see \code{help(\link{l1ce.object})} and \code{help(\link{l1celist.object})}). } \usage{ \method{fitted}{l1ce}(object, \dots) \method{fitted}{l1celist}(object, \dots) } \arguments{ \item{object}{an object inheriting from class \code{l1ce} or \code{l1celist}, e.g., an \code{gl1ce} one (see \code{\link{gl1ce.object}}).} \item{\dots}{further potential arguments passed to methods.} } \seealso{\code{\link{fitted}} for the general behavior of this function and for the interpretation of \code{object}. } \keyword{models} lasso2/man/is.formula.Rd0000644000176200001440000000101607430230305014570 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: is.formula.d,v 1.2 1999/12/01 05:12:09 bturlach Exp $ % --> ../COPYRIGHT for more details \name{is.formula} \alias{is.formula} \title{Tests for Formula Objects} \usage{ is.formula(x) } \arguments{ \item{x}{an \R object to be tested.} } \description{ \code{is.formula} returns \code{TRUE} if \code{x} is an object of class \code{"formula"}, and \code{FALSE} otherwise. } \keyword{utilities} lasso2/man/deviance.gl1ce.Rd0000644000176200001440000000116710677503360015303 0ustar liggesusers\name{deviance.gl1ce} \alias{deviance.gl1ce} \title{Deviance Method for `gl1ce' Objects} \usage{ \method{deviance}{gl1ce}(object, \dots) } \description{ This is a method of the generic function \code{\link{deviance}()} for objects inheriting from class \code{gl1ce} (see \code{help(\link{gl1ce.object})}). } \arguments{ \item{object}{an object inheriting from class \code{gl1ce}.} \item{\dots}{possibly further arguments (none at the moment).} } \seealso{\code{\link{deviance}} for the general behavior of this function and for the interpretation of \code{object}. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/plot.l1celist.Rd0000644000176200001440000000260610677503612015227 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: plot.l1celist.d,v 1.2 1999/11/30 06:14:16 bturlach Exp $ % --> ../COPYRIGHT for more details \name{plot.l1celist} \alias{plot.l1celist} \title{Plot Method for `l1celist' Objects} \description{ Plots a \code{l1celist} object on the current graphics device. } \usage{ \method{plot}{l1celist}(x, plot=TRUE, all=TRUE, constrained=FALSE, type = "b", xlab = "bounds", ylab = "coeff | bounds", \dots) } \arguments{ \item{x}{fitted model object of class \code{l1celist}.} \item{plot}{logical; if \code{TRUE} a \code{matplot()} of all the coefficients in the list against the relative bound (absolute bound if relative is not present) is plotted. Otherwise no plot is done. } \item{all}{logical; if \code{FALSE}, then only the non-zero coefficients are returned.} \item{constrained}{ if \code{TRUE} then only the coefficients that were constrained are returned.} \item{type, xlab, ylab, \dots}{further arguments with useful defaults passed to \code{\link{matplot}}.} } \value{ A matrix with the bound(s) (relative [if used] and absolute), the Lagrangian(s) and coefficints of the fitted model(s). } \examples{ data(Prostate) l1c.P <- l1ce(lpsa ~ ., Prostate, bound=(1:20)/20) length(l1c.P)# 20 l1ce models plot(l1c.P) } \keyword{hplot} lasso2/man/gl1ce.object.Rd0000644000176200001440000001047111137254451014765 0ustar liggesusers\name{gl1ce.object} \alias{gl1ce.object} \title{Generalized L1 Constrained Estimation Model Object} \description{ These are objects of class \code{gl1ce} They represent the fit of a generalized regression model under an L1 constraint on (some of) the parameters. } \section{GENERATION}{ This class of objects is returned from the \code{gl1ce} function to represent a fitted model. } \section{METHODS}{ The \code{gl1ce} class of objects has methods for the following generic functions: \code{deviance}, \code{predict}, \code{print}, \code{residuals}, \code{summary}. Other generic functions are inherited from the class \code{l1ce}. } \section{STRUCTURE}{ The following components must be included in a legitimate \code{gl1ce} object. \describe{ \item{coefficients}{ the coefficients of the fit of the response to the columns of the model matrix. The names of the coefficients are the names of the columns of the model matrix. } \item{residuals}{ the residuals from the fit. If weights were used, then the residuals are the raw residuals - the weights are not taken into account. If you need residuals that all have the same variance, then use the \code{residuals} function with \code{type="pearson"}. } \item{fitted.values}{ the fitted values from the fit. If weights were used, the fitted values are not adjusted for the weights. } \item{family}{ the family of which the fitted regression model belongs, eg., \code{\link{binomial}(link=probit)}. } \item{bound}{ the (absolute) L1 constraint imposed on the parameters. } \item{Lagrangian}{ the value of the Lagrangian that enforces the constraint at the solution. } \item{xtx}{ the moment matrix of the variables that are under the constraint. (After taking weights, sweep-out variables and standardization into account). } \item{xtr}{ the product of the design matrix of the variables that are under the constraint (after taking weights, sweep-out variables and standardization into account) with the residual vector. } \item{constrained.coefficients}{ the coefficients on the scale on which they are constrained. Useful as initial value for further fits. } \item{sweep.out}{ information on the variables that are not under the constraint and on which the other variables and the response is projected first. Optional, not present if \code{sweep.out = NULL}. } \item{assign}{ the list of assignments of coefficients (and effects) to the terms in the model. The names of this list are the names of the terms. The \code{i}th element of the list is the vector saying which coefficients correspond to the \code{i}th term. It may be of length 0 if there were no estimable effects for the term. See also \code{R.assign} below. } \item{terms}{ an object of mode \code{expression} and class \code{term} summarizing the formula. Used by various methods, but typically not of direct relevance to users. } \item{call}{ an image of the call that produced the object, but with the arguments all named and with the actual formula included as the formula argument. } \item{contrasts}{ a list containing sufficient information to construct the contrasts used to fit any factors occurring in the model. The list contains entries that are either matrices or character vectors. When a factor is coded by contrasts, the corresponding contrast matrix is stored in this list. Factors that appear only as dummy variables and variables in the model that are matrices correspond to character vectors in the list. The character vector has the level names for a factor or the column labels for a matrix. } \item{x}{ optionally the model matrix, if \code{x=T}. } \item{y}{ optionally the response, if \code{y=T}. } } } \details{ The residuals, fitted values, coefficients, and effects should be extracted by the generic functions of the same name, rather than by the \code{$} operator. } \seealso{ \code{\link{gl1ce}}, \code{\link{coefficients}}. } \keyword{classes} \keyword{regression} \keyword{methods} % Converted by Sd2Rd version 1.21. lasso2/man/predict.l1ce.Rd0000644000176200001440000000472307767421147015021 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: predict.l1ce.d,v 1.1 1998/09/18 07:27:59 bturlach Exp $ % % This library is free software; you can redistribute it and/or % modify it under the terms of the GNU Library General Public % License as published by the Free Software Foundation; either % version 2 of the License, or (at your option) any later version. % % This library is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU % Library General Public License for more details. % % You should have received a copy of the GNU Library General Public % License along with this library; if not, write to the Free Software % Foundation, Inc., 59 Temple Place, Suite 330, Boston, % MA 02111-1307 USA \name{predict.l1ce} \alias{predict.l1ce} \title{Predict Method for `l1ce' Objects} \description{ This is a method for the generic function \code{\link{predict}} for class \code{"l1ce"}, typically produced from \code{\link{l1ce}()}. When \code{newdata} is missing, the fitted values are extracted, otherwise returns \emph{new} predictions. } \usage{ \method{predict}{l1ce}(object, newdata, type = c("response"), se.fit = FALSE, \dots) } \arguments{ \item{object}{a fitted \code{l1ce} object. } \item{newdata}{ a data frame containing the values at which predictions are required. This argument can be missing, in which case predictions are made at the same values used to compute the object. Only those predictors referred to in the right side of the formula in object need be present by name in \code{newdata}.} \item{type}{currrently only \code{"response"}.} \item{se.fit}{logical indicating if standard errors should be returned as well. \bold{Not yet available}.} \item{\dots}{further potential arguments passed to methods.} } \value{ a vector of predictions. } \section{Warning}{ \code{predict} can produce incorrect predictions when the \code{newdata} argument is used if the formula in \code{object} involves transformations, such as \code{poly(Age, 3)} or \code{sqrt(Age - min(Age))}. MM: Not sure this is true for \R \ldots. } \examples{ data(Iowa) l1c.I <- l1ce(Yield ~ ., Iowa, bound = 10, absolute.t=TRUE) p10 <- predict(l1c.I, newdata = Iowa[10:19,]) stopifnot(all.equal(p10, fitted(l1c.I)[10:19])) } \keyword{models} lasso2/man/print.l1ce.Rd0000644000176200001440000000175110677503652014515 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: print.l1ce.d,v 1.2 1999/12/01 05:11:22 bturlach Exp $ \name{print.l1ce} \alias{print.l1ce} \alias{print.l1celist} \alias{print.gl1ce} \title{Print Methods for `l1ce', `l1celist' and `gl1ce' Objects} \usage{ \method{print}{l1ce}(x, \dots) \method{print}{l1celist}(x, \dots) \method{print}{gl1ce}(x, \dots) } \arguments{ \item{x}{fitted model of class \code{"l1ce"} or \code{"l1celist"}, respectively.} \item{\dots}{potentially further arguments passed to method.} } \description{ These are methods of the generic \code{\link{print}()} function for objects inheriting from class \code{l1ce}, \code{l1celist} or \code{gl1ce} (see \code{help(\link{l1ce.object})}, \code{help(\link{l1celist.object})} or \code{help(\link{gl1ce.object})}). See \code{\link{print}} or \code{\link{print.default}} for the general behavior of this function. } \keyword{print} lasso2/man/predict.gl1ce.Rd0000644000176200001440000000462310357365373015164 0ustar liggesusers\name{predict.gl1ce} \alias{predict.gl1ce} \title{Prediction Method for a `gl1ce' Object} \description{ This is a method for the generic function \code{\link{predict}} for class \code{"gl1ce"}, typically produced from \code{\link{gl1ce}()}. When \code{newdata} is missing, the fitted values are extracted, otherwise returns \emph{new} predictions. } \usage{ \method{predict}{gl1ce}(object, newdata, type=c("link", "response"), se.fit = FALSE, \dots) } \arguments{ \item{object}{a fitted \code{\link{gl1ce}} object.} \item{newdata}{ a data frame containing the values at which predictions are required. This argument can be missing, in which case predictions are made at the same values used to compute the object. Only those predictors referred to in the right side of the formula in object need be present by name in \code{newdata}.} \item{type}{type of predictions, with choices "link" (the default), or "response". The default produces predictions on the scale of the additive predictors, and with \code{newdata} missing, \code{predict()} is simply an extractor function for this component of a \code{gl1ce} object. If "response" is selected, the predictions are on the scale of the response, and are monotone transformations of the additive predictors, using the inverse link function.} \item{se.fit}{logical indicating if standard errors should be returned as well. \bold{Not yet available}.} \item{\dots}{further potential arguments passed to methods.} } \value{ a vector of predictions. } \section{Warning}{ \code{predict} can produce incorrect predictions when the \code{newdata} argument is used if the formula in \code{object} involves data-dependent transformations, such as \code{poly(Age, 3)} %% MM: this was for S-plus -- which has such problems with glm() -- maybe not R or \code{sqrt(Age - min(Age))}. } \examples{ ## start with example(gl1ce) predict(gl1c.E, new = esoph[1:7,])# type 'link' predict(gl1c.E, new = esoph[1:7,], type = "response") ## identities / consistency checks : stopifnot(predict(gl1c.E, type = "response") == fitted(gl1c.E), all.equal(predict(gl1c.E)[1:7], as.vector(predict(gl1c.E, new = esoph[1:7,]))), all.equal(fitted(gl1c.E)[1:7], as.vector(predict(gl1c.E, new = esoph[1:7,], type = "response"))) ) } \keyword{models} lasso2/man/labels.l1ce.Rd0000644000176200001440000000174210677503503014616 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: labels.l1ce.d,v 1.2 1999/12/01 05:11:03 bturlach Exp $ % --> ../COPYRIGHT for more details \name{labels.l1ce} \alias{labels.l1ce} \alias{labels.l1celist} \title{`Labels' Method for `l1ce' and `l1celist' Objects} \usage{ \method{labels}{l1ce}(object, \dots) \method{labels}{l1celist}(object, \dots) } \arguments{ \item{object}{fitted model of class \code{"l1ce"} or \code{"l1celist"}, respectively.} \item{\dots}{potentially further arguments passed to method.} } \description{ This is a method for the \code{\link{labels}()} function for objects inheriting from class \code{l1ce} or \code{l1celist} (see \code{help(\link{l1ce.object})} and \code{help(\link{l1celist.object})}). See \code{\link{labels}} or \code{\link{labels.default}} for the general behavior of this function and for the interpretation of \code{object}. } \keyword{utilities} lasso2/man/summary.l1ce.Rd0000644000176200001440000001135111061126764015044 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: summary.l1ce.d,v 1.2 1998/09/27 02:02:02 bturlach Exp $ % --> ../COPYRIGHT for more details \name{summary.l1ce} \alias{summary.l1ce} \alias{print.summary.l1ce} \title{Summary Method for ``l1ce'' Objects (Regression with L1 Constraint)} \description{ Returns a summary list for a regression model with an L1 constraint on the parameters. A null value will be returned if printing is invoked. } \usage{ \method{summary}{l1ce}(object, correlation = TRUE, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, sigma = NULL, \dots) \method{print}{summary.l1ce}(x, digits = max(3, getOption("digits") - 3), \dots) } \arguments{ \item{object}{fitted model of class \code{"l1ce"}.} \item{correlation}{logical indicating if the correlation matrix for the coefficients should be included in the summary.} \item{type}{character string specifying whether to use the covariance formula of Osborne, Presnell and Turlach or the formula of Tibshirani.} \item{gen.inverse.diag}{ if Tibshirani's formula for the covariance matrix is used, this value is used for the diagonal elements of the generalised inverse that appears in the formula that corresponds to parameters estimated to be zero. The default is 0, i.e. use the Moore-Penrose inverse. Tibshirani's code uses gen.inverse.diag=1e11.} \item{sigma}{ the residual standard error estimate. If not provided, then it is estimated by the deviance of the model divided by the error degrees of freedom.} \item{x}{an \R object of class \code{summary.l1ce}.} \item{digits}{number of significant digits to use.} \item{\dots}{further potential arguments passed to methods.} } \value{ an object of class \code{summary.l1ce} (for which there's a \code{print} method). It is basically a list with the following components: \item{correlation}{the computed correlation coefficient matrix for the coefficients in the model.} \item{cov.unscaled}{the unscaled covariance matrix; i.e, a matrix such that multiplying it by an estimate of the error variance produces an estimated covariance matrix for the coefficients. } \item{df}{the number of degrees of freedom for the model and for residuals.} \item{coefficients}{a matrix with three columns, containing the coefficients, their standard errors and the corresponding t statistic.} \item{residuals}{the model residuals. These are the weighted residuals if weights were given in the model.} \item{sigma}{the residual standard error estimate.} \item{terms}{the terms object used in fitting this model.} \item{call}{the call object used in fitting this model.} \item{bound}{the bound used in fitting this model.} \item{relative.bound}{the relative bound used in fitting this model (may not be present).} \item{Lagrangian}{the Lagrangian of the model.} } \details{ This function is a method for the generic function \code{\link{summary}()} for class \code{"l1ce"}. It can be invoked by calling \code{summary(x)} for an object \code{x} of the appropriate class, or directly by calling \code{summary.l1ce(x)} regardless of the class of the object. } \seealso{ \code{\link{l1ce}}, \code{\link{l1ce.object}}, \code{\link{summary}}. } \examples{%%- or just those in ./l1ce.Rd data(Prostate) summary(l1ce(lpsa ~ .,Prostate)) # Produces the following output: \dontrun{ Call: l1ce(formula = lpsa ~ ., data = Prostate) Residuals: Min 1Q Median 3Q Max -1.636 -0.4119 0.076 0.452 1.83 Coefficients: Value Std. Error Z score Pr(>|Z|) (Intercept) 0.7285 1.3898 0.5242 0.6002 lcavol 0.4937 0.0919 5.3711 0.0000 lweight 0.2682 0.1774 1.5115 0.1307 age 0.0000 0.0111 0.0000 1.0000 lbph 0.0093 0.0587 0.1581 0.8744 svi 0.4551 0.2525 1.8023 0.0715 lcp 0.0000 0.0947 0.0000 1.0000 gleason 0.0000 0.1685 0.0000 1.0000 pgg45 0.0002 0.0046 0.0391 0.9688 Residual standard error: 0.7595 on 88.36 degrees of freedom The relative L1 bound was : 0.5 The absolute L1 bound was : 0.9219925 The Lagrangian for the bound is: 13.05806 Correlation of Coefficients: (Intercept) lcavol lweight age lbph svi lcp gleason lcavol 0.1988 lweight -0.4815 -0.2071 age -0.3938 -0.0603 -0.0974 lbph 0.3629 -0.0201 -0.5165 -0.1303 svi -0.0624 -0.2273 -0.1442 0.0635 0.0648 lcp 0.0457 -0.4153 0.0598 0.0665 0.0632 -0.3779 gleason -0.7666 -0.2009 0.1163 -0.0774 -0.0617 0.1084 -0.0243 pgg45 0.4988 0.0956 -0.0380 -0.0630 -0.1111 -0.1921 -0.2935 -0.6526 } } \keyword{regression} % Converted by Sd2Rd version 1.21. lasso2/man/Iowa.Rd0000644000176200001440000000306207430011533013413 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: Iowa.d,v 1.3 1999/12/01 05:07:43 bturlach Exp $ % --> ../COPYRIGHT for more details \name{Iowa} \alias{Iowa} \title{The Iowa Wheat Yield Data} \description{ The data gives the pre-season and three growing months' precipitation, the mean temperatures for the three growing months and harvest month, the year, and the yield of wheat for the USA state of Iowa, for the years 1930--1962. } \usage{ data(Iowa) } \format{ The data frame has the following components: \describe{ \item{\code{Year}}{Year of measurement (surrogate for variety improvements)} \item{\code{Rain0}}{Pre-season rainfall (in.)} \item{\code{Temp1}}{Mean temperature for the first growing month (deg. F)} \item{\code{Rain1}}{Rainfall for the first growing month (in.)} \item{\code{Temp2}}{Mean temperature for the second growing month (deg. F)} \item{\code{Rain2}}{Rainfall for the second growing month (in.)} \item{\code{Temp3}}{Mean temperature for the third growing month (deg. F)} \item{\code{Rain3}}{Rainfall for the third growing month (in.)} \item{\code{Temp4}}{Mean temperature for the harvest month (deg. F)} \item{\code{Yield}}{Yield of wheat in Iowa for the given year (bush./acre)} } } \section{CATEGORY}{ Multiple regression; diagnostics. } \source{ CAED Report, 1964. Quoted in Draper and Smith, Applied Regression Analysis. } \examples{ data(Iowa) pairs(Iowa) } \keyword{datasets} % Converted by Sd2Rd version 1.21. lasso2/man/l1ce.Rd0000644000176200001440000001223413732044251013346 0ustar liggesusers\name{l1ce} \alias{l1ce} \title{Regression Fitting With L1-constraint on the Parameters} \description{ Returns an object of class \code{"l1ce"} or \code{"licelist"} that represents fit(s) of linear models while imposing L1 constraint(s) on the parameters. } \usage{ l1ce(formula, data = parent.frame(), weights, subset, na.action, sweep.out = ~ 1, x = FALSE, y = FALSE, contrasts = NULL, standardize = TRUE, trace = FALSE, guess.constrained.coefficients = double(p), bound = 0.5, absolute.t = FALSE) } \arguments{ \item{formula}{a formula object, with the response on the left of a \code{ ~ } operator, and the terms, separated by \code{+} operators, on the right.} \item{data}{ a \code{data.frame} in which to interpret the variables named in the formula, the \code{weights}, the \code{subset} and the \code{sweep.out} argument. If this is missing, then the variables in the formula should be globally available.} \item{weights}{ vector of observation weights. The length of \code{weights} must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the \code{subset} argument. } \item{subset}{ expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default. } \item{na.action}{ a function to filter missing data. This is applied to the \code{\link{model.frame}} after any \code{subset} argument has been used. The default (with \code{na.fail}) is to create an error if any missing values are found. A possible alternative is \code{na.omit}, which deletes observations that contain one or more missing values. } \item{sweep.out}{ a formula object, variables whose parameters are not put under the constraint are swept out first. The variables should appear on the right of a \code{ ~ } operator and be separated by \code{+} operators. Default is \code{ ~1 }, i.e. the constant term is not under the constraint. If this parameter is \code{NULL}, then all parameters are put under the constraint. } \item{x}{logical indicating if the model matrix should be returned in component \code{x}.} \item{y}{logical indicating if the response should be returned in component \code{y}.} \item{contrasts}{ a list giving contrasts for some or all of the factors appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any full-rank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels. } \item{standardize}{ logical flag: if \code{TRUE}, then the columns of the model matrix that correspond to parameters that are constrained are standardized to have emprical variance one. The standardization is done after taking possible weights into account and after sweeping out variables whose parameters are not constrained; see vignette for details. } \item{trace}{ logical flag: if \code{TRUE}, then the status during each iteration of the fitting is reported. } \item{guess.constrained.coefficients}{ initial guess for the parameters that are constrained. } \item{bound}{numeric, either a single number or a vector: the constraint(s) that is/are put onto the L1 norm of the parameters.} \item{absolute.t}{ logical flag: if \code{TRUE}, then \code{bound} is an absolute bound and all entries in \code{bound} can be any positive number. If \code{FALSE}, then \code{bound} is a relative bound and all entries must be between 0 and 1; see vignette for details.} } \value{ an object of class \code{l1ce} (if \code{bound} was a single value) or \code{l1celist} (if \code{bound} was a vector of values) is returned. See \code{\link{l1ce.object}} and \code{\link{l1celist.object}} for details. } \references{ Osborne, M.R., Presnell, B. and Turlach, B.A. (2000) On the LASSO and its Dual, \emph{Journal of Computational and Graphical Statistics} \bold{9}(2), 319--337. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso, \emph{Journal of the Royal Statistical Society, Series B} \bold{58}(1), 267--288. } \examples{ data(Iowa) l1c.I <- l1ce(Yield ~ ., Iowa, bound = 10, absolute.t=TRUE) l1c.I ## The same, printing information in each step: l1ce(Yield ~ ., Iowa, bound = 10, trace = TRUE, absolute.t=TRUE) data(Prostate) l1c.P <- l1ce(lpsa ~ ., Prostate, bound=(1:30)/30) length(l1c.P)# 30 l1ce models l1c.P # -- MM: too large; should do this in summary(.)! %% summary(l1c.P) \testonly{ str(l1c.P, max.lev = 1) } plot(resid(l1c.I) ~ fitted(l1c.I)) abline(h = 0, lty = 3, lwd = .2) } \keyword{models} \keyword{optimize} \keyword{regression} % Converted by Sd2Rd version 1.21. lasso2/man/zaux.l1celist.Rd0000644000176200001440000000144310677503765015247 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: aux.l1celist.d,v 1.2 1999/12/01 05:08:22 bturlach Exp $ % --> ../COPYRIGHT for more details \name{aux.l1celist} \alias{aux.l1celist} \title{Use `aux()' on a `l1celist' object} \usage{ \method{aux}{l1celist}(object, \dots) } \arguments{ \item{object}{fitted model object (here typically of class \code{l1celist}).} \item{\dots}{potentially further arguments passed to methods.} } \description{ This is a method for the function \code{aux()} for objects inheriting from class \code{l1celist}. See \code{\link{aux}} for the general behavior of this function and for the interpretation of \code{object}. } \keyword{utilities} % Converted by Sd2Rd version 1.21. lasso2/man/Extract.l1celist.Rd0000644000176200001440000000206310677503210015652 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: [[.l1celist.d,v 1.1 1998/09/18 11:23:09 bturlach Exp $ % --> ../COPYRIGHT for more details \name{Extract.l1celist} \alias{[.l1celist} \alias{[[.l1celist} \title{Extract Parts of a `l1celist' Object} \description{ Allows the user to extract values from a \code{l1celist} object by using subscripts. } \usage{ \method{[}{l1celist}(x, \dots, drop = TRUE) \method{[[}{l1celist}(x, \dots, drop = TRUE) } \arguments{ \item{x}{an object inheriting from class \code{"l1celist"}.} \item{\dots}{a specification of indices -- see \code{\link{Extract}}.} \item{drop}{logical defaulting to \code{TRUE}. If only one model is subscribed, then it is returned as an object of class \code{"l1ce"}. If \code{drop=F}, then an object of class \code{"l1celist"} is always returned.} } \value{ an object of class \code{"l1celist"} or class \code{"l1ce"} extracted from the original list. } \keyword{manip} % Converted by Sd2Rd version 1.21. lasso2/man/qr.rtr.inv.Rd0000644000176200001440000000136007454534457014563 0ustar liggesusers\name{qr.rtr.inv} \alias{qr.rtr.inv} \title{Reconstruct the Inverse of R'R from a QR Object} \description{ From a QR object, compute the inverse matrix which is implicitely (but not explicitly!) used to solve the underlying least squares problem. } \usage{ qr.rtr.inv(qr) } \arguments{ \item{qr}{\"qr\" object, typically resulting from \code{\link{qr}(.)}.} } \value{ The \eqn{p \times p}{p * p} matrix \eqn{(R'R)^{-1}}{(R'R)^(-1)} or equivalently, the inverse of \eqn{X'X} (i.e. \code{t(X) \%*\% X} in \R). } \seealso{\code{\link{qr}}, \code{\link{qr.R}}, \code{\link{backsolve}}.} \examples{ (h3 <- 1/outer(0:5, 1:3, "+")) rtr <- qr.rtr.inv(qr(h3)) all.equal(c(rtr \%*\% 1:3), solve(crossprod(h3), 1:3)) } \keyword{algebra} \keyword{array} lasso2/man/residuals.gl1ce.Rd0000644000176200001440000000322510010407415015476 0ustar liggesusers\name{residuals.gl1ce} \alias{residuals.gl1ce} \title{Compute Residuals for `gl1ce' Objects} \description{ Computes one of the four types of residuals available for \code{gl1ce} objects. This is a method for the function residuals() for objects inheriting from class \code{gl1ce}. As several types of residuals are available for \code{gl1ce} objects, there is an additional optional argument type. } \usage{ \method{residuals}{gl1ce}(object, type=c("deviance", "pearson", "working", "response"), \dots) } \arguments{ \item{object}{ an object inheriting from class \code{gl1ce} representing a fitted model. } \item{type}{ type of residuals, with choices "deviance", "pearson", "working" or "response"; the first is the default. } \item{\dots}{possibly further arguments (none at the moment).} } \value{ A numeric vector of residuals. See Statistical Models in S for detailed definitions of each type of residual. The sum of squared deviance residuals add up to the deviance. The pearson residuals are standardized residuals on the scale of the response. The working residuals reside on the object, and are the residuals from the final fit. The response residuals are simply \code{y - fitted(object)}. The \code{summary()} method for \code{gl1ce} objects produces deviance residuals. The residuals component of a \code{gl1ce} object contains the working residuals. } \references{ Chambers, J.M., and Hastie, T.J. (1991). Statistical Models in S, pp. 204--206. } \seealso{\code{\link{gl1ce}} for examples; \code{\link{fitted.l1ce}} is used for fitted values. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/vcov.l1ce.Rd0000644000176200001440000000277607430222161014330 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: vcov.l1ce.d,v 1.2 1999/12/01 05:11:56 bturlach Exp $ % --> ../COPYRIGHT for more details \name{vcov.l1ce} \alias{vcov.l1ce} \alias{vcov.l1celist} \title{Variance-Covariance Matrix of `l1ce' or `l1celist' Objects} \usage{ \method{vcov}{l1ce}(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, \dots) \method{vcov}{l1celist}(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, \dots) } \arguments{ \item{object}{an object of class \code{l1ce} or \code{l1celist}.} \item{type}{character indicating whether to use the covariance formula of Osborne, Presnell and Turlach or the formula of Tibshirani.} \item{gen.inverse.diag}{if Tibshirani's formula for the covariance matrix is used, this value is used for the diagonal elements of the generalised inverse that appears in the formula that corresponds to parameters estimated to be zero. The default is 0, i.e. use the Moore-Penrose inverse. Tibshirani's code uses \code{gen.inverse.diag = 1e11}.} \item{\dots}{further potential arguments passed to methods.} } \description{ This is a method for the function \code{vcov()} for objects inheriting from class \code{l1ce} or \code{l1celist} (see \code{help(\link{l1ce.object})} and \code{help(\link{l1celist.object})}). See \code{\link{vcov}} for the general behavior of this function. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/coef.l1ce.Rd0000644000176200001440000000203307774125704014271 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: coef.l1ce.d,v 1.5 1999/12/01 05:08:40 bturlach Exp $ % --> ../COPYRIGHT for more details \name{coef.l1ce} \alias{coef.l1ce} \title{Coefficients of an `l1ce' Object} \usage{ \method{coef}{l1ce}(object, all=TRUE, constrained=FALSE, \dots) } \description{ %% For R >= 1.9.0, replace \link{ by \link[stats]{: This is a method for \code{\link{coef}()} for objects inheriting from class \code{l1ce}. See \code{\link{coef}} for the general behavior of this function and for the interpretation of \code{object}. } \arguments{ \item{object}{an object of class \code{l1ce}, see help on \code{\link{l1ce.object}}.} \item{all}{logical; if false, then only the non-zero coefficients are returned.} \item{constrained}{logical; if true, then only the coefficients that were constrained are returned.} \item{\dots}{possibly further arguments (none at the moment).} } \keyword{regression} \keyword{models} lasso2/man/Prostate.Rd0000644000176200001440000000332207430012615014316 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: Prostate.d,v 1.4 1999/12/01 05:07:34 bturlach Exp $ % --> ../COPYRIGHT for more details \name{Prostate} \alias{Prostate} \title{Prostate Cancer Data} \description{ These data come from a study that examined the correlation between the level of prostate specific antigen and a number of clinical measures in men who were about to receive a radical prostatectomy. It is data frame with 97 rows and 9 columns. } \usage{ data(Prostate) } \format{ The data frame has the following components: \describe{ \item{\code{lcavol}}{log(cancer volume)} \item{\code{lweight}}{log(prostate weight)} \item{\code{age}}{age} \item{\code{lbph}}{log(benign prostatic hyperplasia amount)} \item{\code{svi}}{seminal vesicle invasion} \item{\code{lcp}}{log(capsular penetration)} \item{\code{gleason}}{Gleason score} \item{\code{pgg45}}{percentage Gleason scores 4 or 5} \item{\code{lpsa}}{log(prostate specific antigen)} } } \source{ Stamey, T.A., Kabalin, J.N., McNeal, J.E., Johnstone, I.M., Freiha, F., Redwine, E.A. and Yang, N. (1989)\cr Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate: II. radical prostatectomy treated patients, \emph{Journal of Urology} \bold{141}(5), 1076--1083. } \examples{ data(Prostate) attach(Prostate) pairs(Prostate, col = 1+svi, pch = gleason - 5, main = paste("Prostate data, n = ", nrow(Prostate))) detach() l1c.P <- l1ce(lcavol ~ ., data = Prostate) coef(l1c.P)[coef(l1c.P) != 0] ## only age, lcp, lpsa (+ intercept) summary(l1c.P) } \keyword{datasets} % Converted by Sd2Rd version 1.21. lasso2/man/gl1ce.Rd0000644000176200001440000001301513732326050013513 0ustar liggesusers\name{gl1ce} \alias{gl1ce} \alias{family.gl1ce} \title{Generalized Regression With L1-constraint on the Parameters} \description{ Fit a generalized regression problem while imposing an L1 constraint on the parameters. Returns an object of class \code{gl1ce}. } \usage{ gl1ce(formula, data = parent.frame(), weights, subset, na.action, family = gaussian, control = glm.control(\dots), sweep.out = ~ 1, x = FALSE, y = TRUE, contrasts = NULL, standardize = TRUE, guess.constrained.coefficients = double(p), bound = 0.5, \dots) \method{family}{gl1ce}(object, \dots) } \arguments{ \item{formula}{a \code{\link{formula}}, with the response on the left hand side of a \code{~} operator, and the terms, separated by a \code{+} operator, on the right hand side.} \item{data}{a \code{\link{data.frame}} in which to interpret the variables named in the formula, the \code{weights}, the \code{subset} and the \code{sweep.out} argument. If this is missing, then the variables in the formula should be globally available.} \item{weights}{vector of observation weights. The length of \code{weights} must be the same as the number of observations. The weights must be strictly positive, since zero weights are ambiguous, compared to use of the \code{subset} argument.} \item{subset}{ expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default. } \item{na.action}{ a function to be applied to the \code{model.frame} after any \code{subset} argument has been used. The default (with \code{na.fail}) is to create an error if any missing values are found. A possible alternative is \code{na.omit}, which deletes observations that contain one or more missing values. } \item{family}{a \code{\link{family}} object - a list of functions and expressions for defining the link and variance functions, initialization and iterative weights. Families supported are gaussian, binomial, poisson, Gamma, inverse.gaussian and quasi. Functions like binomial produce a family object, but can be given without the parentheses. Family functions can take arguments, as in \code{binomial(link=probit)}. } \item{control}{ a list of iteration and algorithmic constants. See glm.control for their names and default values. These can also be set as arguments to gl1ce itself. } \item{sweep.out}{ a formula object, variables whose parameters are not put under the constraint are swept out first. The variables should appear on the right of a \code{~} operator and be separated by \code{+} operators. Default is \code{~1}, i.e. the constant term is not under the constraint. If this parameter is \code{NULL}, then all parameters are put under the constraint. } \item{x}{ logical flag: if \code{TRUE}, the model matrix is returned in component \code{x}. } \item{y}{ logical flag: if \code{TRUE}, the response is returned in component \code{y}. } \item{contrasts}{ a list giving contrasts for some or all of the factors appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any full-rank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels. } \item{standardize}{ logical flag: if \code{TRUE}, then the columns of the model matrix that correspond to parameters that are constrained are standardized to have empirical variance one. The standardization is done after taking possible weights into account and after sweeping out variables whose parameters are not constrained. } \item{guess.constrained.coefficients}{ initial guess for the parameters that are constrained. } \item{bound}{numeric, either a single number or a vector: the constraint(s) that is/are put onto the L1 norm of the parameters.} \item{\dots}{potential arguments for \code{\link[stats]{glm.control}}, as default for the \code{control} argument above.} \item{object}{an \R object of class \code{"gl1ce"}.} }% args \value{ an object of class \code{gl1ce} is returned by \code{gl1ce()}. See \code{\link{gl1ce.object}} for details. } \references{ See the references in \code{\link{l1ce}}. Justin Lokhorst (1999). The LASSO and Generalised Linear Models, Honors Project, Nov. 1999, Dept.Statist., Univ. of Adelaide. } \seealso{\code{\link{glm}} for unconstrained generalized regression modeling. } \examples{ ## example from base: data(esoph) summary(esoph) ## effects of alcohol, tobacco and interaction, age-adjusted modEso <- formula(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp) glm.E <- glm(modEso, data = esoph, family = binomial()) gl1c.E <- gl1ce(modEso, data = esoph, family = binomial()) gl1c.E plot(residuals(gl1c.E) ~ fitted(gl1c.E)) sg1c <- summary(gl1c.E) sg1c ## Another comparison glm() / gl1c.E: plot(predict(glm.E, type="link"), predict(glm.E, type="response"), xlim = c(-3,0)) points(predict(gl1c.E, type="link"), predict(gl1c.E, type="response"), col = 2, cex = 1.5) %%% mabye FIXME!! labels(gl1c.E)#-- oops! empty!! } \keyword{models} \keyword{optimize} \keyword{regression} % Converted by Sd2Rd version 1.21. lasso2/man/summary.gl1ce.Rd0000644000176200001440000000355407767416302015231 0ustar liggesusers\name{summary.gl1ce} \alias{summary.gl1ce} \alias{print.summary.gl1ce} \title{Summary Method for Generalized L1 Constrained Regression Models} \description{ Returns (and prints) a summary list for fitted generalized L1 constrained regression models. } \usage{ \method{summary}{gl1ce}(object, dispersion = NULL, correlation = FALSE, \dots) \method{print}{summary.gl1ce}(x, digits = max(3, getOption("digits") - 3), \dots) } \arguments{ \item{object}{ fitted \code{gl1ce} model object. This is assumed to be the result of some fit that produces an object inheriting from the class \code{gl1ce}, in the sense that the components returned by the gl1ce() function will be available. } \item{dispersion}{prescribed dispersion, see \code{\link{summary.glm}}.} \item{correlation}{logical indicating if the correlation matrix should be returned as well.} \item{x}{object of class \code{summary.gl1ce}.} \item{digits, \dots}{further arguments for the \code{print} method.} } \value{ an object of class \code{summary.gl1ce} (for which there's a \code{print} method). It is basically a list with the following components: \item{call}{as contained on object} \item{residuals}{the deviance residuals, as produced by \code{residuals(object, type = "deviance")}.} \item{coefficients}{the coefficients of the model.} \item{family}{ the family of models to which object belongs, along with the variance and link functions for that model.} \item{bound}{the bound used in fitting this model} \item{Lagrangian}{the Lagrangian of the model} } \details{ This function is a method for the generic function summary for class \code{gl1ce}. It can be invoked by calling summary for an object of the appropriate class, or directly by calling summary.gl1ce regardless of the class of the object. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/residuals.l1ce.Rd0000644000176200001440000000222712234365013015337 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: residuals.l1ce.d,v 1.2 1999/12/01 05:11:45 bturlach Exp $ % --> ../COPYRIGHT for more details \name{residuals.l1ce} \alias{residuals.l1ce} \alias{residuals.l1celist} \title{Residuals of `l1ce' or `l1celist' Objects} \usage{ \method{residuals}{l1ce}(object, type, \dots) \method{residuals}{l1celist}(object, type, \dots) } \arguments{ \item{object}{ an object inheriting from class \code{l1ce} representing a fitted model. } \item{type}{ type of residuals, with choices "deviance", "pearson", "working" or "response"; the first is the default. } \item{\dots}{possibly further arguments (none at the moment).} } \description{ This is a method for the function \code{\link{residuals}} for objects inheriting from class \code{l1ce} or \code{l1celist} (see \code{help(\link{l1ce.object})} and \code{help(\link{l1celist.object})}). See \code{\link{residuals}} for the general behavior of this function and for the interpretation of \code{object} and \code{type}. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/l1celist.object.Rd0000644000176200001440000000577211137254514015522 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: l1celist.object.d,v 1.2 1998/09/23 02:55:04 bturlach Exp $ % --> ../COPYRIGHT for more details \name{l1celist.object} \alias{l1celist.object} \title{Object of Several L1 Constrained Estimation Models} \description{ These are objects of class \code{"l1celist"} They represent the fits of several regression models under an L1 constraint on (some of the parameters). } \section{GENERATION}{ This class of objects is returned from the \code{l1ce} function to represent a fitted model. } \section{METHODS}{ The \code{"l1celist"} class of objects has methods for the following generic functions: \code{[}, \code{[[}, \code{aux}, \code{coef}, \code{deviance}, \code{fitted}, \code{formula}, \code{gcv}, \code{labels}, \code{plot}, \code{print}, \code{residuals}, \code{vcov}. } \section{STRUCTURE}{ An object of class \code{l1celist} is a list of lists. Each component of the list is a list with the information of an object of class \code{l1ce} that is unique for that information. All shared information is stored as attributes. Each component of the list must include the following components for it to be a legitimate \code{l1celist} object. \describe{ \item{coefficients}{ the coefficients of the fit of the response to the columns of the model matrix. The names of the coefficients are the names of the columns of the model matrix. } \item{residuals}{ the residuals from the fit. If weights were used, then the residuals are the raw residuals - the weights are not taken into account. If you need residuals that all have the same variance, then use the \code{residuals} function with \code{type="pearson"}. } \item{fitted.values}{ the fitted values from the fit. If weights were used, the fitted values are not adjusted for the weights. } \item{bound}{ the (absolute) L1 constraint imposed on the parameters. } \item{relative.bound}{ optional, the (relative) L1 constraint imposed on the parameters. Present if \code{absolute.t=F}. } \item{Lagrangian}{ the value of the Lagrangian that enforces the constraint at the solution. } \item{xtr}{ the product of the design matrix of the variables that are under the constraint (after taking weights, sweep-out variables and standardization into account) with the residual vector. } \item{constrained.coefficients}{ the coefficients on the scale on which they are constrained. Useful as initial value for further fits. } } } \details{ The residuals, fitted values, coefficients, and effects should be extracted by the generic functions of the same name, rather than by using the \code{[[]]} and the \code{$} operator. } \seealso{ \code{\link{l1ce}}, \code{\link{coefficients}}. } \keyword{classes} \keyword{regression} \keyword{methods} % Converted by Sd2Rd version 1.21. lasso2/man/lasso-internal.Rd0000644000176200001440000000047707454262250015470 0ustar liggesusers\name{lasso-internal} \alias{opt.covmat} \alias{tib.covmat} \title{Internal lasso functions} \description{ Internal lasso functions. } \usage{ opt.covmat(xtx, xtr, bound, Lagrangian) tib.covmat(xtx, gen.inverse.diag, beta, Lagrangian) } \details{ These are not (yet?) to be called by the user. } \keyword{internal} lasso2/man/gcv.l1ce.Rd0000644000176200001440000000307607430223104014122 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: gcv.l1ce.d,v 1.4 1999/12/01 05:10:52 bturlach Exp $ % --> ../COPYRIGHT for more details \name{gcv.l1ce} \alias{gcv.l1ce} \alias{gcv.l1celist} \title{`gcv()' Methods for `l1ce' and `l1celist' Objects.} \usage{ \method{gcv}{l1ce}(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, \dots) \method{gcv}{l1celist}(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, \dots) } \arguments{ \item{object}{an object of class \code{l1ce} or \code{l1celist}.} \item{type}{character (string) indicating whether to use the covariance formula of Osborne, Presnell and Turlach or the formula of Tibshirani.} \item{gen.inverse.diag}{ if Tibshirani's formula for the covariance matrix is used, this value is used for the diagonal elements of the generalised inverse that appears in the formula that corresponds to parameters estimated to be zero. The default is 0, i.e. use the Moore-Penrose inverse. Tibshirani's code uses \code{gen.inverse.diag = 1e11}.} \item{\dots}{further potential arguments passed to methods.} } \description{ This is a method for the function \code{gcv()} for objects inheriting from class \code{l1ce} or \code{l1celist}. } \seealso{ \code{\link{gcv}} for the general behavior of this function; \code{\link{l1ce.object}} and \code{\link{l1celist.object}} for description of the \code{object} argument. } \details{ See documentation. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/merge.formula.Rd0000644000176200001440000000145513377173735015307 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % --> ../COPYRIGHT for more details \name{merge.formula} \alias{merge.formula} \title{Merge Formula With Right Hand Side of Second Formula} \usage{ \method{merge}{formula}(x, y, \dots) } \arguments{ \item{x,y}{formulas.} \item{\dots}{potentially further arguments passed to methods.} } \description{ This is method for formulas of the \code{\link[base]{merge}} generic function. Here it is support for the function \code{\link{l1ce}} and not intended to be called directly by users. } \examples{ merge(y ~ x1, ~ x2) ## -> y ~ x1 + x2 f2 <- merge(y ~ x1*x2, z ~ (x2+x4)^3) f. <- merge(y ~ x1*x2, ~ (x2+x4)^3) # no LHS for 2nd term f2 stopifnot(f2 == f.) } \keyword{utilities} lasso2/man/coef.l1celist.Rd0000644000176200001440000000214007774125672015170 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: coef.l1celist.d,v 1.4 1999/12/01 05:08:55 bturlach Exp $ % --> ../COPYRIGHT for more details \name{coef.l1celist} \alias{coef.l1celist} \title{Coefficients of an `l1celist' Object} \usage{ \method{coef}{l1celist}(object, all=TRUE, constrained=FALSE, \dots) } \description{ %% For R >= 1.9.0, replace \link{ by \link[stats]{: This is a method for \code{\link{coef}()} for objects inheriting from class \code{l1celist}. See \code{\link{coef}} for the general behavior of this function and for the interpretation of \code{object}. } \arguments{ \item{object}{an object of class \code{l1celist}, see help on \code{\link{l1celist.object}}.} \item{all}{logical; if false, then the coefficients that are zero in all fitted models of the list are not returned.} \item{constrained}{logical; if true, then only the coefficients that were constrained are returned.} \item{\dots}{possibly further arguments (none at the moment).} } \keyword{regression} \keyword{models} lasso2/man/gcv.Rd0000644000176200001440000000143411061126736013303 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: gcv.d,v 1.2 1998/09/18 07:03:35 bturlach Exp $ % --> ../COPYRIGHT for more details \name{gcv} \alias{gcv} \title{Generalised Cross-Validation Score} \description{ Extracts the generalised cross-validation score(s) from fitted model objects. } \usage{ gcv(object, \dots) } \arguments{ \item{object}{fitted model object; see gcv methods for details.} \item{\dots}{arguments passed to methods.} } \value{ A vector (or matrix) with the bound(s) (relative [if used] and absolute), the Lagrangian(s) and the generalised cross-validation score(s) for the fitted model(s). } \details{ See documentation. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/deviance.l1ce.Rd0000644000176200001440000000144510677503435015136 0ustar liggesusers\name{deviance.l1ce} \alias{deviance.l1ce} \alias{deviance.l1celist} \title{Deviance Method for `l1ce' and `l1celist' Objects} \usage{ \method{deviance}{l1ce}(object, \dots) \method{deviance}{l1celist}(object, \dots) } \arguments{ \item{object}{an object inheriting from class \code{l1ce} or \code{l1celist}, respectively.} \item{\dots}{possibly further arguments (none at the moment).} } \description{ These are methods of the generic function \code{\link{deviance}()} for objects inheriting from class \code{l1ce} or \code{l1celist} (see \code{help(\link{l1ce.object})} and \code{help(\link{l1celist.object})}). } \seealso{\code{\link{deviance}} for the general behavior of this function and for the interpretation of \code{object}. } \keyword{models} % Converted by Sd2Rd version 1.21. lasso2/man/l1ce.object.Rd0000644000176200001440000001116511137254307014617 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: l1ce.object,v 1.2 1998/09/20 09:28:27 bturlach Exp $ % --> ../COPYRIGHT for more details \name{l1ce.object} \alias{l1ce.object} \title{L1 Constrainted Estimation Model Object} \description{ These are objects of class \code{"l1ce"}. They represent the fit of a regression model under an L1 constraint on (some of) the parameters. } \section{GENERATION}{ This class of objects is returned from the \code{\link{l1ce}} function to represent a fitted model. } \section{METHODS}{ The \code{"l1ce"} class of objects has methods for the following generic functions:\cr \code{coef}, \code{deviance}, \code{fitted}, \code{formula}, \code{gcv}, \code{labels}, \code{predict}, \code{print}, \code{residuals}, \code{summary}, \code{vcov}. } \section{STRUCTURE}{ The following components must be included in a legitimate \code{l1ce} object. \describe{ \item{coefficients}{ the coefficients of the fit of the response to the columns of the model matrix. The names of the coefficients are the names of the columns of the model matrix. } \item{residuals}{ the residuals from the fit. If weights were used, then the residuals are the raw residuals - the weights are not taken into account. If you need residuals that all have the same variance, then use the \code{residuals} function with \code{type="pearson"}. } \item{fitted.values}{ the fitted values from the fit. If weights were used, the fitted values are not adjusted for the weights. } \item{bound}{the (absolute) L1 constraint imposed on the parameters. } \item{relative.bound}{ optional, the (relative) L1 constraint imposed on the parameters. Present if \code{absolute.t=F}. } \item{Lagrangian}{ the value of the Lagrangian that enforces the constraint at the solution. } \item{xtx}{ the moment matrix of the variables that are under the constraint. (After taking weights, sweep-out variables and standardization into account). } \item{xtr}{ the product of the design matrix of the variables that are under the constraint (after taking weights, sweep-out variables and standardization into account) with the residual vector. } \item{constrained.coefficients}{ the coefficients on the scale on which they are constrained. Useful as initial value for further fits. } \item{sweep.out}{ Optional information on the variables that are not under the constraint and on which the other variables and the response is projected first.%% FIXME?? isn't this needed by summary? Optional, not present if \code{sweep.out = NULL}. } \item{assign}{ the list of assignments of coefficients (and effects) to the terms in the model. The names of this list are the names of the terms. The \code{i}th element of the list is the vector saying which coefficients correspond to the \code{i}th term. It may be of length 0 if there were no estimable effects for the term. See also \code{R.assign} below. } \item{terms}{%% FIXME : differs between R and S+ an object of mode \code{expression} and class \code{term} summarizing the formula. Used by various methods, but typically not of direct relevance to users. } \item{call}{ an image of the call that produced the object, but with the arguments all named and with the actual formula included as the formula argument. } \item{contrasts}{ a list containing sufficient information to construct the contrasts used to fit any factors occurring in the model. The list contains entries that are either matrices or character vectors. When a factor is coded by contrasts, the corresponding contrast matrix is stored in this list. Factors that appear only as dummy variables and variables in the model that are matrices correspond to character vectors in the list. The character vector has the level names for a factor or the column labels for a matrix. } \item{x}{optionally the model matrix, if \code{x=TRUE}.} \item{y}{optionally the response, if \code{y=TRUE}.} }%describe } \details{ The residuals, fitted values, coefficients, and effects should be extracted by the generic functions of the same name, rather than by the \code{$} operator. } \seealso{ \code{\link{l1ce}}, \code{\link{coefficients}}. } \keyword{classes} \keyword{regression} \keyword{methods} % Converted by Sd2Rd version 1.21. lasso2/man/tr.Rd0000644000176200001440000000104007425040612013137 0ustar liggesusers% Copyright (C) 1998 % Berwin A. Turlach % Bill Venables % $Id: tr.d,v 1.1 1998/12/18 06:41:47 bturlach Exp $ % --> ../COPYRIGHT for more details \name{tr} \alias{tr} \title{Trace of a Matrix} \description{Calculates the trace of a matrix} \usage{ tr(mat) } \arguments{ \item{mat}{a square matrix.} } \value{ The trace of the matrix, i.e. the sum of its diagonal elements, is returned. } \examples{ tr(cbind(1,1:3,4:2)) # 5 } \keyword{math} % Converted by Sd2Rd version 1.21. lasso2/DESCRIPTION0000644000176200001440000000115014127776332013174 0ustar liggesusersPackage: lasso2 Version: 1.2-22 Date: 2021-10-08 Author: Justin Lokhorst, Bill Venables and Berwin Turlach; port to R, tests etc: Martin Maechler Maintainer: Berwin Turlach Title: L1 Constrained Estimation aka `lasso' Description: Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. (1998). Depends: R (>= 4.0.0) License: GPL (>= 2) Packaged: 2021-10-07 18:04:01 UTC; berwin NeedsCompilation: yes Repository: CRAN Date/Publication: 2021-10-08 08:10:02 UTC lasso2/build/0000755000176200001440000000000014127633221012555 5ustar liggesuserslasso2/build/vignette.rds0000644000176200001440000000032614127633221015115 0ustar liggesusersb```b`add`b2 1# 'M+M-LK-)I +GS"QX\oPJȨ44%  q `aBgKM-FZ]?4-ީE0=(jؠjX2sRad9.nP&c0Gq?gQ~ 6@dPP+%$Q/n8lasso2/tests/0000755000176200001440000000000014127633221012620 5ustar liggesuserslasso2/tests/l1ce-tst.R0000644000176200001440000000660312134206642014403 0ustar liggesusers####------ l1ce() tests ------------ library(lasso2) ###-------- +/- the same as example(l1ce) : data(Iowa) l1c.I <- l1ce(Yield ~ ., Iowa, bound = 10, trace = TRUE, absolute.t=TRUE) ## the next ones give a l1ce LIST (one for each bound) l1c.liI <- l1ce(Yield ~ ., Iowa, bound = seq(1e-6,1, len= 17)) print(plot(l1c.liI)) data(Prostate) ## '0' fails on 64b: l1c.P <- l1ce(lpsa ~ ., Prostate, bound= c(0, 1, len= 17)) l1c.P <- l1ce(lpsa ~ ., Prostate, bound= c(1e-6,seq(1/16, 1, by=1/16))) ## complicated bound (for the comparison test below) print(plot(l1c.P)) ## test multi-/single- bound problem: l1c.P.25 <- l1ce(lpsa ~ ., Prostate, bound= 0.25)# 0.25 is nr. [5] above l1c.P.1 <- l1ce(lpsa ~ ., Prostate, bound= 1) lm.P <- lm (lpsa ~ ., Prostate) stopifnot(all.equal(coef(l1c.P.25), coef(l1c.P)[ 5,], tol= 1e-13), all.equal(coef(l1c.P.1 ), coef(l1c.P)[17,], tol= 1e-13), all.equal(coef(lm.P), coef(l1c.P.1), tol= 1e-13) ) ###-------- Try a case where p > n : n <- 100 p <- 120 RNGversion("1.6.0") set.seed(n+p) x <- matrix(runif(n*p), n,p, dimnames= list(NULL, paste("x",1:p,sep=""))) x <- as.data.frame(apply(x, 2, sort)) with(x, y <<- 5 + 4*x1 -3*x2 + 10*x3 + (eps <<- rnorm(n, sd = 1/200))) d.ex <- cbind(y = y, x) dim(d.ex)# 100 x 121 if(FALSE) summary(lm(y ~ ., data = d.ex))# almost complete nonsense ## both these give something, but not at all the true model ... l20 <- l1ce(y ~ ., data = d.ex, bound = 20, absolute.t = TRUE) coef(l20)[coef(l20) > 0] l15 <- l1ce(y ~ ., data = d.ex, bound = 15, absolute.t = TRUE) coef(l15)[coef(l15) > 0] sum(eps^2) sum(resid(l20)^2) / sum(eps^2) sum(resid(l15)^2) / sum(eps^2) ## Lower the bounds dramatically now: l1.lis <- l1ce(y ~ ., data = d.ex, bound = seq(1e-5, 0.1, len=21)) pl1lis <- plot(l1.lis)#ylim = c(-10,10)) round(pl1lis$mat,3) bnds <- pl1lis$bounds[,"rel.bound"] round(1000 * t(pl1lis$mat[0.01 < bnds & bnds < 0.06 ,])) ### ---------- Check that l1ce(.) can be used inside functions ------- ## {has not worked for a long time, till 2005-06-10}: myLasso <- function(formula, data, sweep.out = ~ 1, boundset) { ## Of course this is silly: calling l1ce() for each bound separately: sapply(boundset, function(B) l1ce(formula, data = data, sweep.out = sweep.out, bound = B), simplify = FALSE) } my.lis <- myLasso(y ~ ., data = d.ex, boundset = seq(1e-5, 0.1, len=6)) ## should have a subset from the models in l1.lis above ! ### ------------------------------------------------------------------ value = c(-1.2229125582, 0.0291875346, -1.1354055994, -1.1586843969, 0.0265310929, -1.2761698858, -0.9567990729, 0.0001296789, -0.9174081609, -0.8564132893 , 0.0825918391, -0.8348783996, -0.8967906196, -0.1102660185, -0.9392872627, -0.6826339823, 0.0521741319, -0.7737865885) P3 = c(1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1) P4 = c(0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1) P1 = c(1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0) df = data.frame(value,P1,P3,P4) try( l1ce(value~-1+(P3+P4+P1)^2,data=df,na.action=na.omit,absolute.t=TRUE,standardize=FALSE,bound=0.4,sweep.out=~-1+ (P3+P4+P1) )) try( l1ce(value~-1+(P3+P4+P1)^2,data=df,na.action=na.omit,absolute.t=TRUE,standardize=FALSE,bound=0.5,sweep.out=~-1+ (P3+P4+P1) )) try(l1ce(value~-1+(P3+P4+P1)^2,data=df,na.action=na.omit,absolute.t=TRUE,standardize=FALSE,bound=0.6,sweep.out=~-1+ (P3+P4+P1) )) lasso2/GPL-30000644000176200001440000010451311273015516012167 0ustar liggesusers GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . lasso2/src/0000755000176200001440000000000014127633221012245 5ustar liggesuserslasso2/src/init.c0000644000176200001440000000125013377411072013356 0ustar liggesusers#include // for NULL #include /* FIXME: Check these declarations against the C/Fortran source code. */ /* .C calls */ extern void lasso(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *); extern void mult_lasso(void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *, void *); static const R_CMethodDef CEntries[] = { {"lasso", (DL_FUNC) &lasso, 12}, {"mult_lasso", (DL_FUNC) &mult_lasso, 12}, {NULL, NULL, 0} }; void R_init_lasso2(DllInfo *dll) { R_registerRoutines(dll, CEntries, NULL, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } lasso2/src/lasso.h0000644000176200001440000000610614127632374013552 0ustar liggesusers/* Copyright (C) 1998 Berwin A Turlach */ /* This library is free software; you can redistribute it and/or modify it under the terms of the GNU Library General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. */ /* This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Library General Public License for more details. */ /* You should have received a copy of the GNU Library General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. */ #define S_or_R #ifndef BT_LASSO_H #define BT_LASSO_H #include #include #include #include #if defined(S_or_R) # include #define LASSO2_PROBLEM_BUFSIZE 4096 /* Parentheses added for FC4 with gcc4 and -D_FORTIFY_SOURCE=2 */ #define LASSO2_PROBLEM {char LASSO2_problem_buf[LASSO2_PROBLEM_BUFSIZE];(snprintf)(LASSO2_problem_buf, LASSO2_PROBLEM_BUFSIZE, #define LASSO2_MESSAGE {char LASSO2_problem_buf[LASSO2_PROBLEM_BUFSIZE];(snprintf)(LASSO2_problem_buf, LASSO2_PROBLEM_BUFSIZE, #define LASSO2_RECOVER(x) ),Rf_error(LASSO2_problem_buf);} #define LASSO2_WARNING(x) ),Rf_warning(LASSO2_problem_buf);} #define LASSO2_NULL_ENTRY /**/ #else /* !defined(S_or_R) */ # define Calloc(n,t) malloc((n)*sizeof(t)) # define Realloc(p,n,t) realloc(p,(n)*sizeof(t)) # define Free(p) free(p), p=NULL # include # define Memcpy(p,q,n) memcpy(p,q,(n)*sizeof(*(p))) #endif # define Sprintf Rprintf void lasso(double *x, Sint *pn, Sint *pm, double *pt, double *beta, double *y, double *yhat1, double *r, double *lagrangian, Sint *psuc, Sint *pverb, Sint *pas_sub); void mult_lasso(double *x, Sint *pn, Sint *pm, double *pt, Sint *pl, double *beta, double *y, double *yhat1, double *r, double *lagrangian, Sint *psuc, Sint *pverb); void rs_lasso(double *x, double *y, Sint *pn, Sint *porder, double *pt, double *beta, double *yhat1, double *yhat2, double *r, Sint *psuc, Sint *pverb, Sint *pas_sub); void rs_auto(double *x, double *y, Sint *pn, Sint *porder, double *pt, double *beta, double *yhat1, double *yhat2, double *r, Sint *pcrit1, double *pprec, Sint *psuc, Sint *pverb, double *tgr, double *tgr_val, Sint *ptgr_len); #define TRUE 1 #define FALSE 0 #define RMAT(i,j) (*(rmat+(j)*((j)+1)/2+(i))) #define QMAT(i,j) (*(qmat+(j)*q_nrow+(i))) #define RLAST(i) (*(rmat+(r_ncol-1)*r_ncol/2+(i))) #define RLTEL (*(rmat+r_ncol*(r_ncol+1)/2-1)) #define RCOL(j) (rmat+(j)*((j)+1)/2) #define QCOL(j) (qmat+(j)*q_nrow) #define RLTCOL (rmat+(r_ncol-1)*r_ncol/2) #define QR_CHUNK 10 #if defined (S_or_R) # define ERRMSG(where,msg) errmsg(msg) #else # define ERRMSG(where,msg) errmsg(where,msg) #endif #endif lasso2/src/lasso.c0000644000176200001440000005162514127632472013552 0ustar liggesusers/* Copyright (C) 1998 Berwin A Turlach */ /* This library is free software; you can redistribute it and/or modify it under the terms of the GNU Library General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. */ /* This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Library General Public License for more details. */ /* You should have received a copy of the GNU Library General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. */ #include "lasso.h" static void lasso_alloc(Sint n, Sint m); static void lasso_free(void); static void qr_init(int n); static void qr_incr(void); static void qr_free(void); static void qr_del(int l, int aug); static void qr_add(double *x, int swap); #if defined (S_or_R) static void errmsg(char* string); #else static void errmsg(char* where, char* string); #endif /* Global Variables [yuck!] : */ static double *xtr=NULL, *btmp=NULL, *qtr=NULL, *rinvt_theta=NULL, *step=NULL, ytyd2=0.0; static int *theta=NULL, *nz_x=NULL, num_nz_x=0; static double *qmat=NULL, *rmat=NULL; static int qr_max_size=0, r_ncol=0, q_nrow=0, q_use_row=0; static char *no_dyn_mem_message="Cannot allocate dynamic memory"; void lasso(double *x, Sint *pn, Sint *pm, double *pt, double *beta, double *y, double *yhat1, double *r, double *lagrangian, Sint *psuc, Sint *pverb, Sint *pas_sub) { double t = *pt, prec; Sint n = *pn, m = *pm, verb = *pverb, as_sub = *pas_sub; int not_solved; double *x_elem=NULL, tmp, max_val, b_1norm; int i, j, max_ind; double p_obj, d_obj; int num_iter=0, max_num; double b_1norm_old, mu; double rho_up, rho_low, rho; int to_del; double *q_elem, wtc, wtw; double *r_elem; int k; int add; if( !as_sub) lasso_alloc(n,m); prec = sqrt(DBL_EPSILON); if( as_sub ) { b_1norm = 0.0; for(j=0;j < num_nz_x;j++) b_1norm += fabs(beta[nz_x[j]]); } else { b_1norm = 0.0; num_nz_x = 0; for(j=0; j < m; j++) { if(fabs(beta[j]) > prec) { b_1norm += fabs(beta[j]); nz_x[num_nz_x] = j; num_nz_x++; } else beta[j] = 0.0; } } if( b_1norm > t) { if(verb) { Sprintf("******************************\n"); Sprintf("Rescaling beta from L1-norm %f to %f\n",b_1norm,t); } for(j=0; j < num_nz_x; j++) beta[nz_x[j]] = beta[nz_x[j]] * t/b_1norm; b_1norm = t; } for(i=0; i < n; i++) yhat1[i] = 0.0; for(j=0; j < num_nz_x; j++) { /* x_elem points to the first element in the column of X to which the j-th entry in nz_x points */ x_elem = x + nz_x[j]*n; tmp = beta[nz_x[j]]; for(i=0; i < n; i++) { yhat1[i] += *x_elem*tmp; x_elem++; /* now we point to the next element in X */ } } /* calculate the residual vector */ for(i=0; i < n; i++) r[i] = y[i]-yhat1[i]; /* multiply X^T with the residual vector */ x_elem = x; for(j=0; j < m; j++) { tmp = 0.0; for(i=0; i < n; i++) { tmp += *x_elem*r[i]; x_elem++; } xtr[j] = tmp; } max_val = fabs(xtr[0]); max_ind = 0; for(j=1; j < m; j++) if( fabs(xtr[j]) > max_val ) { max_val = fabs(xtr[j]); max_ind = j; } if( !as_sub ) { qr_add(y,TRUE); ytyd2 = *rmat * *rmat/2.0; for(j=0;j < num_nz_x;j++) { qr_add(x+nz_x[j]*n, TRUE); if(fabs(beta[nz_x[j]]) < prec) theta[j] = xtr[nz_x[j]] < 0 ? -1 : 1; else theta[j] = beta[nz_x[j]] < 0 ? -1 : 1; } } if( num_nz_x==0 ) { nz_x[0] = max_ind; num_nz_x = 1; if(verb) { Sprintf("******************************\n"); Sprintf(" -->\tAdding variable: %d\n",max_ind+1); } qr_add(x+max_ind*n, TRUE); theta[0] = xtr[max_ind] < 0 ? -1 : 1; } *psuc=0; if(verb) { /* Find out how many times [[max_val]] is attained */ tmp = (1.0-prec)*max_val; if( tmp < prec ) tmp = 0.0; max_num = 1; for(j=0; j < m; j++) { if( tmp <= fabs(xtr[j]) && j!=max_ind) { /*we found another element equal to the (current) maximal absolute value */ max_num++; } } /* for the value of the dual objective function we need to calculate the L2 norm of the vector of fitted values */ p_obj = 0.0; d_obj = 0.0; for(i=0;i < n;i++) { p_obj += r[i]*r[i]; d_obj += yhat1[i]*yhat1[i]; } p_obj /= 2.0; d_obj = ytyd2 - d_obj/2.0 - t*max_val ; Sprintf("******************************\n"); Sprintf("\nIteration number: %d\n", num_iter); Sprintf("Value of primal object function : %f\n", p_obj); Sprintf("Value of dual object function : %f\n", d_obj); Sprintf("L1 norm of current beta : %f", b_1norm); Sprintf(" <= %f\n", t); Sprintf("Maximal absolute value in t(X)%%*%%r : %e", max_val); Sprintf(" attained %d time(s)\n", max_num); Sprintf("Number of parameters allowed to vary : %d\n", num_nz_x); num_iter++; } while(1) { do { q_elem = qmat; for(j=0;j < num_nz_x;j++) { tmp = 0.0; for(i=0;i < n;i++,q_elem++) tmp += *q_elem*r[i]; qtr[j] = tmp; } if( b_1norm < (1.0-prec)*t ) mu = 0.0; else { /* z=R^{-T}\theta can be calculated by solving R^Tz=\theta */ r_elem = rmat; for(j=0;j < num_nz_x;j++,r_elem++) { tmp = theta[j]; for(k=0;k < j;k++,r_elem++) tmp -= *r_elem * rinvt_theta[k]; rinvt_theta[j] = tmp / *r_elem; } wtc = 0.0; wtw = 0.0; for(j=0;j < num_nz_x;j++) { wtc += rinvt_theta[j]*qtr[j]; wtw += rinvt_theta[j]*rinvt_theta[j]; } mu = (wtc-(t-b_1norm))/wtw; } if(mu<=0.0) for(j=0;j < num_nz_x;j++) step[j] = qtr[j]; else for(j=0;j < num_nz_x;j++) step[j] = qtr[j] - mu*rinvt_theta[j]; /* h=R^{-1}z can be calculated by solving Rh=z */ for(j=num_nz_x-1;j>=0;j--) { tmp = step[j]; for(k=num_nz_x-1;k>j;k--) tmp -= RMAT(j,k) * step[k]; step[j] = tmp / RMAT(j,j); } for(j=0;j < num_nz_x;j++) { btmp[j] = beta[nz_x[j]]; beta[nz_x[j]] += step[j]; } b_1norm_old=b_1norm; b_1norm = 0.0; for(j=0;j < num_nz_x;j++) b_1norm += fabs(beta[nz_x[j]]); not_solved=FALSE; if( b_1norm > (1+prec)*t) { not_solved=TRUE; if(b_1norm_old < (1.0-prec)*t) { if(verb) Sprintf(" -->\tStepping onto the border of the L1 ball.\n"); rho_up = rho = 1.0; rho_low = 0.0; while( fabs(t-b_1norm) > prec*t ) { if( b_1norm > t) { rho_up = rho; rho = (rho+rho_low)/2.0; } if( b_1norm < t) { rho_low = rho; rho = (rho+rho_up)/2.0; } if(rho < prec) break; for(j=0; j < num_nz_x; j++) beta[nz_x[j]] = btmp[j]+rho*step[j]; b_1norm = 0.0; for(j=0;j < num_nz_x;j++) b_1norm += fabs(beta[nz_x[j]]); } for(j=0;j < num_nz_x;j++) { if(fabs(beta[nz_x[j]]) < prec) theta[j] = btmp[j] > 0 ? -1 : 1; else theta[j] = beta[nz_x[j]] < 0 ? -1 : 1; } } else { rho = 1.0; to_del = -1; for(j=0; j < num_nz_x; j++) { if(fabs(step[j]) > prec) { tmp = -btmp[j]/(step[j]); if( 0.0 < tmp && tmp < rho ) { rho = tmp; to_del = j; } } } if(to_del < 0 ) { *psuc= -1; goto EXIT_HERE; } for(j=0; j\tRemoving variable: %d",nz_x[to_del]+1); beta[nz_x[to_del]] = 0.0; qr_del(to_del,TRUE); for(j=to_del+1; j< num_nz_x; j++) { nz_x[j-1] = nz_x[j]; theta[j-1] = theta[j]; } num_nz_x--; b_1norm = 0.0; for(j=0;j max_val ) { max_val = fabs(xtr[j]); max_ind = j; } if(verb) { /* Find out how many times [[max_val]] is attained */ tmp = (1.0-prec)*max_val; if( tmp < prec ) tmp = 0.0; max_num = 1; for(j=0; j\tAdding variable: %d\n",max_ind+1); } EXIT_HERE: if( !as_sub) lasso_free(); } void mult_lasso(double *x, Sint *pn, Sint *pm, double *pt, Sint *pl, double *beta, double *y, double *yhat1, double *r, double *lagrangian, Sint *psuc, Sint *pverb) { double prec; Sint n = *pn, m = *pm, l = *pl, verb = *pverb, as_sub = TRUE; int i, j; lasso_alloc(n,m); qr_add(y,TRUE); ytyd2 = *rmat * *rmat/2.0; prec = sqrt(DBL_EPSILON); num_nz_x = 0; for(j=0; j prec) { qr_add(x+j*n, TRUE); nz_x[num_nz_x] = j; num_nz_x++; } else beta[j] = 0.0; } *psuc = 0; for(i=0; i0) Memcpy(beta,beta-m,m); lasso(x, pn, pm, pt+i, beta, y, yhat1, r, lagrangian, psuc, pverb, &as_sub); if( *psuc < 0 ) { goto EXIT_HERE; } beta += m; yhat1 += n; r += n; lagrangian++; } EXIT_HERE: lasso_free(); } static void lasso_alloc(Sint n, Sint m) { #if defined(S_or_R) if( nz_x != NULL || theta != NULL || xtr != NULL || btmp != NULL || qtr != NULL || rinvt_theta != NULL || step != NULL || num_nz_x != 0 || ytyd2 != 0.0) { LASSO2_MESSAGE "Possible memory corruption or memory leak.\n We" "advise to restart your S+ session" LASSO2_WARNING(LASSO2_NULL_ENTRY); lasso_free(); } #endif nz_x = Calloc(m,int); if( nz_x == NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); theta = Calloc(m,int); if( theta==NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); xtr = Calloc(m,double); if( xtr==NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); btmp = Calloc(m,double); if( btmp==NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); qtr = Calloc(m,double); if( qtr==NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); rinvt_theta = Calloc(m,double); if( rinvt_theta==NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); step = Calloc(m,double); if( step==NULL ) ERRMSG("lasso_alloc", no_dyn_mem_message); qr_init(n); } static void lasso_free(void) { num_nz_x=0; ytyd2 = 0.0; Free(nz_x); Free(theta); Free(xtr); Free(btmp); Free(qtr); Free(rinvt_theta); Free(step); qr_free(); } static void qr_init(int n) { #if defined (S_or_R) if(qr_max_size!=0 || r_ncol!=0 || q_nrow!=0 || q_use_row!=0 || qmat!=NULL || rmat!=NULL) { LASSO2_MESSAGE "Possible memory corruption or memory leak.\n We" "advise to restart your S+ session" LASSO2_WARNING(LASSO2_NULL_ENTRY); qr_free(); } #endif qr_max_size = 2*n; //QR_CHUNK; r_ncol = 0; q_nrow = n; qmat = Calloc(n*qr_max_size,double); if(qmat==NULL) ERRMSG("qr_init", no_dyn_mem_message); rmat = Calloc(qr_max_size*(qr_max_size+1)/2,double); if(rmat==NULL) ERRMSG("qr_init", no_dyn_mem_message); } static void qr_incr(void) { qr_max_size += QR_CHUNK; /* reallocate R always */ rmat = Realloc(rmat,qr_max_size*(qr_max_size+1)/2,double); if(rmat==NULL) ERRMSG("qr_incr", no_dyn_mem_message); /* reallocate Q only if necessary and only to maximal necessary size */ qmat = Realloc(qmat,q_nrow*qr_max_size,double); if(qmat==NULL) ERRMSG("qr_incr", no_dyn_mem_message); } static void qr_free(void) { qr_max_size = 0; r_ncol = 0; q_nrow = 0; q_use_row = 0; Free(qmat); Free(rmat); } static void qr_del(int l, int aug) { double c, s, tau, nu, *col_k, *col_kp1, *a, *b, tmp; int i, j, k, l0; if( l<0 || l>=r_ncol ) ERRMSG("qr_del", "Invalid column number"); r_ncol--; if(l == r_ncol) { if( aug ) ERRMSG("qr_del", "Trying to delete last column of augmented matrix"); else return; } /* this is TRUE if $m\le n$ ($m-1\le n$ if the matrix is augmented) */ if ( r_ncol < q_nrow ) { for(k=l; k= q_nrow ) { /* Multiply new column with Q-transpose in [[qr_add]] */ q_elem = qmat; for(i=0;i= norm_last/2.0) break; if( norm_new > 0.1*norm_init*DBL_EPSILON ) norm_last = norm_new; else{ norm_init = norm_last = 0.1*norm_last*DBL_EPSILON; for(i=0;i 0.0 ) { /*Calculate Givens rotation*/ nu = tau*sqrt((*a/tau)*(*a/tau)+(*b/tau)*(*b/tau)); c = *a/nu; s = *b/nu; *a = nu; /* Fix column before last in R */ *b = -s* *(b-1); /* Fix last element of last column in R */ *(b-1) = c * *(b-1); /* Fix second last element of last column in R */ if( *b < 0) { *b = -*b; col_lm1 = QCOL(r_ncol-1); col_l = col_lm1+q_nrow; for(j=0;j> stream xW{\S ^IIFl$Cnuu16uucXJzpu\:bW횕]~!&QPE V-%/뜮_/?s}ys E'NMbXgSUQ))}bʧ$`hbuxUY'X)ZT-ŴuYέn;?w}㷭|Z”鴐]WH wSLQOɲ?+2ɋM+ eŊ{MrlU?k*R3* zZ zfE-N a(mG@Qi2;is #T2]{ \߫7l6۸ewV=hZ")%F H{$ -uɋ?Zv6ʚlmB mb%$JVپ*lVˢR'ɯrFV{=m[em&ek?io+VٮޅE_~ 6"VΆ&/HɆVt~VW[mhBSoʨ2\OUȵ/ZZ-d%]B0Z*& 9^&8}#%ɲlOFiQ⽍7Ah!_p`M%FNR*M+JIQjg7nҵWC#1Ipg @*򎡓] =UxB@T a=uU]zpH rQ}3/&sbA_|Tq0?700Ͻ2G"s*k~6*9AMa_^v6xm, )^EAQ#> EjɞO^8x3!56@ :q`CgU%!ҭ& s@ jLZ~=a16k54=5-}X#XZU٠/ 4s6DQհOl#nh$X\K9˹t+ǩZ,Qv# ߈W#wc~= Гgf,̀ 28 嘟'NFt-K2a4gFP _ ~CrtT]Gdc/T^j$NO5iQh$J; 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R /Info 626 0 R >> startxref 180420 %%EOF lasso2/vignettes/Manual-vignette.Rnw0000644000176200001440000000053411062144243017214 0ustar liggesusers\documentclass[a4paper]{article} %\VignetteIndexEntry{Manual of lasso2 package} %\VignettePackage{lasso2} \usepackage{pdfpages} \begin{document} \newread\PDFtoInclude \openin\PDFtoInclude=Manual-wo-help.pdf \ifeof\PDFtoInclude \typeout{No file Manual-wo-help.pdf} \else \includepdf[fitpaper=true,pages=-]{Manual-wo-help.pdf} \fi \end{document} lasso2/R/0000755000176200001440000000000012134211711011647 5ustar liggesuserslasso2/R/predict.q0000644000176200001440000000671310010403155013466 0ustar liggesusers### Copyright (C) 1998 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA predict.l1ce <- function(object, newdata, type=c("response"), se.fit = FALSE, ...) { type <- match.arg(type) if(missing(newdata)) return(object$fitted) if(se.fit) stop("The `se.fit' argument is not currently implemented for l1ce objects") tt <- object$terms if(!inherits(tt, "terms")) stop("invalid terms component of fit") offset <- attr(tt, "offset") intercept <- attr(tt, "intercept") if(missing(newdata)) { x <- model.matrix(object) } else if(!((is.atomic(newdata) && length(newdata) == 1 && length(object$coef) != 1 && newdata > 0 && (newdata - trunc(newdata) < .Machine$single.eps)) | is.list(newdata))) { ## try and coerce newdata to look like the x matrix if (!is.null(offset)) { warning("Offset not included") offset <- NULL } TT <- length(object$coef) if(is.matrix(newdata) && ncol(newdata) == TT) x <- newdata else if(length(newdata) == TT) x <- matrix(newdata, 1, TT) else stop("Argument `newdata' is not a data frame, and cannot be coerced to an appropriate model matrix") } else { ## newdata is a list, data frame or frame number vv <- attr(tt, "term.labels") attr(tt, "term.labels") <- vv[ - attr(tt, "response")] x <- model.matrix(tt, newdata, object$contrasts) if(!is.null(offset)) offset <- eval(attr(tt, "term.labels")[offset], newdata) } coefs <- coef(object) pred <- drop( x%*% coefs ) if(!is.null(offset)) { if(missing(newdata)) { warning("Offset not included") } else { pred <- pred + offset } } pred } predict.gl1ce <- function(object, newdata, type=c("link", "response"), se.fit = FALSE, ...) { type <- match.arg(type) if(!se.fit){ if (missing(newdata)) { switch(type, link = object$linear.predictors, response = object$fitted) } else { switch(type, response = { linkinv <- family(object)[[if(is.R())"linkinv" else "inverse"]] linkinv(NextMethod("predict")) }, link = { type <- "response" NextMethod("predict") }) } } else { stop("The `se.fit' argument is not currently implemented for gl1ce objects") } } lasso2/R/print.q0000644000176200001440000001206010010405611013157 0ustar liggesusers### Copyright (C) 1998 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA print.l1ce <- function(x, ...){ if(!is.null(cl <- x$call)) { cat("Call:\n") dput(cl) } cat("\nCoefficients:\n") print(coef(x), ...) if(!is.null(rel <- x$relative.bound)){ cat("\nThe relative L1 bound was :", format(rel), "\n") }else{ cat("\n") } cat("The absolute L1 bound was :", format(x$bound), "\n") cat("The Lagrangian for the bound is: ", format(x$Lagrangian), "\n") if(!is.null(attr(x, "na.message"))) cat("\n", attr(x, "na.message"), "\n") invisible(x) } print.l1celist <- function(x, ...){ if(!is.null(cl <- attr(x, "call"))) { cat("Call:\n") dput(cl) } mat.of.coef <- coef(x) cat("\nCoefficients:\n") print(mat.of.coef, ...) x.aux <- aux(x) if( ncol(x.aux) == 2 ){ cat("\nAbsolute L1 bounds and the Lagrangians:\n") print(x.aux, ...) }else{ cat("\nRelative and absolute L1 bounds and the Lagrangians:\n") print(x.aux, ...) } if(!is.null(attr(x, "na.message"))) cat("\n", attr(x, "na.message"), "\n") invisible(x) } print.summary.l1ce <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall:\n ") dput(x$call) resid <- x$residuals df <- x$df rdf <- df[2] if(rdf > 5) { cat("Residuals") if(any(na <- is.na(resid))) cat(" (",sum(na), "NA's)") cat(":\n") qnam <- c("Min", "1Q", "Median", "3Q", "Max") if(length(dim(resid)) == 2) { rq <- apply(t(resid), 1, quantile, na.rm = TRUE) dimnames(rq) <- list(qnam, dimnames(resid)[[2]]) } else { rq <- quantile(resid[!na]) names(rq) <- qnam } print(rq, digits = digits, ...) } else if(rdf > 0) { cat("Residuals:\n") print(resid, digits = digits, ...) } cat("\nCoefficients:\n") print(format(round(x$coef, digits = digits)), quote = FALSE, ...) if(x$sigma.provided){ cat("\nResidual standard error:", format(signif(x$sigma, digits)), "was provided to \"summary.l1ce\"\n") }else{ cat("\nResidual standard error:", format(signif(x$sigma, digits)), "on", format(signif(rdf,digits)), "degrees of freedom\n") } if(!is.null(rel <- x$relative.bound)) cat("The relative L1 bound was :", format(rel), "\n") cat("The absolute L1 bound was :", format(x$bound), "\n") cat("The Lagrangian for the bound is: ", format(x$Lagrangian), "\n") correl <- x$correlation if(!is.null(correl)) { p <- dim(correl)[2] if(p > 1) { cat("\nCorrelation of Coefficients:\n") ll <- lower.tri(correl) correl[ll] <- format(round(correl[ll], digits)) correl[!ll] <- "" print(correl[-1, -p, drop = FALSE], quote = FALSE, digits = digits, ...) } } invisible(x) } print.gl1ce <- function(x, ...) { if(!is.null(cl <- x$call)) { cat("Call:\n") dput(cl) } cat("\nCoefficients:\n") print(coef(x), ...) cat("\nFamily:\n") print(x$family, ...) cat("\nThe absolute L1 bound was : ", format(x$bound), "\n") cat("The Lagrangian for the bound is : ", format(x$Lagrangian), "\n") if(!is.null(attr(x, "na.message"))) cat("\n", attr(x, "na.message"), "\n") invisible(x) } print.summary.gl1ce <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall:\n") dput(x$call) resid <- residuals(x) cat("\nDeviance Residuals") qnam <- c("Min", "1Q", "Median", "3Q", "Max") if(any(na <- is.na(resid))) cat(" (",sum(na), "NA's)") cat(":\n") qnam <- c("Min", "1Q", "Median", "3Q", "Max") if(length(dim(resid)) == 2) { rq <- apply(t(resid), 1, quantile, na.rm = TRUE) dimnames(rq) <- list(qnam, dimnames(resid)[[2]]) } else { rq <- quantile(resid[!na]) names(rq) <- qnam } print(rq, digits = digits, ...) ccf <- format(round(x$coeff, digits = digits)) ccf[x$coeff == 0.] <- "0" # visualize exact zeroes cat("\nCoefficients\n") print(ccf, quote = FALSE, ...) cat("\nFamily:\n") print(x$family, ...) cat("\nThe absolute L1 bound was : ", format(x$bound), "\n") cat("The Lagrangian for the bound is : ", format(x$Lagrangian), "\n") cat("\nNumber of Iterations:", format(trunc(x$iter)), "\n") invisible(x) } lasso2/R/gcv.q0000644000176200001440000000320607430222242012615 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA gcv <- function(object, ...) UseMethod("gcv") gcv.l1ce <- function(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, ...) { type <- match.arg(type) covdf <- vcov(object,type,gen.inverse.diag) rss <- deviance(object) n <- length(object$fitted) res <- rss/((1-covdf$df[1]/n)^2*n) cbind(bound=object$bound, gcv=res) } gcv.l1celist <- function(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, ...) { type <- match.arg(type) covdf <- vcov(object,type,gen.inverse.diag) rss <- deviance(object) rssdf <- do.call("rbind",lapply(covdf, "[[", "df"))[,1] n <- length(object[[1]]$fitted) res <- rss/((1-rssdf/n)^2*n) cbind(bound=aux(object), gcv = res) } lasso2/R/zzz.q0000644000176200001440000000146712052600014012673 0ustar liggesusers### Copyright (C) 1998, 1999 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### $Id: First.lib.template,v 1.18 1999/12/08 23:35:43 bturlach Exp $ ### --> ../COPYRIGHT for more details .onAttach <- function(lib, pkg) { packageStartupMessage(paste("R Package to solve regression problems while imposing", "\t an L1 constraint on the parameters. Based on S-plus Release 2.1", "Copyright (C) 1998, 1999", "Justin Lokhorst ", "Berwin A. Turlach ", "Bill Venables \n", "Copyright (C) 2002", "Martin Maechler ", sep="\n")) } lasso2/R/deviance.q0000644000176200001440000000121107430231063013607 0ustar liggesusers### Copyright (C) 1998, 1999 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details deviance.l1ce <- function(object, ...){ if(is.null(w <- object$weights)){ sum(object$residuals^2) }else{ sum(w * object$residuals^2) } } deviance.l1celist <- function(object, ...) { res2 <- do.call("cbind", lapply(object, "[[", "residuals"))^2 weights <- attr(object, "weights") apply(if(is.null(weights)) res2 else weights*res2, 2, sum) } deviance.gl1ce <- function(object, ...) object$deviance lasso2/R/coef.q0000644000176200001440000000144207424553602012764 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details coef.l1ce <- function(object, all=TRUE, constrained=FALSE, ...) { label <- if(constrained) "constrained.coefficients" else "coefficients" if(all) object[[label]] else object[[label]][abs(object[[label]]) > sqrt(.Machine$double.eps)] } coef.l1celist <- function(object, all=TRUE, constrained=FALSE, ...) { label <- if(constrained) "constrained.coefficients" else "coefficients" if(all) { do.call("rbind", lapply(object, "[[", label)) } else { junk <- do.call("rbind", lapply(object, "[[", label)) ind <- apply(abs(junk), 2, sum) junk[, ind != 0] } } lasso2/R/l1ce.q0000644000176200001440000002673313732044170012700 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details if(is.R()) { ### -- this is also used in gl1ce() hence ''package global'' : ## Orig 2.1 version (for S+) calls qr.rtr.inv(.) which is not in R; ## Is only used to return the (R'R)^{-1} where MM thinks should ## rather return the QR object (or just its $qr and $rank) !! ## It's only summary() or vcov() which needs this, and ## they really can compute it *then* instead of *now* qr.rtr.inv <- function(qr) { if(is.null(R <- qr$qr)) stop("argument is not a valid \"qr\" object") p <- qr$rank rinv <- backsolve(R[1:p, 1:p, drop = FALSE], diag(p)) r <- rinv %*% t(rinv) nm <- (dimnames(R)[[2]])[1:p] dimnames(r) <- list(nm, nm) r } } l1ce <- function(formula, data = parent.frame(), weights, subset, na.action, sweep.out = ~ 1, x = FALSE, y = FALSE, contrasts = NULL, standardize = TRUE, trace = FALSE, guess.constrained.coefficients = double(p), bound = 0.5, absolute.t = FALSE) { call <- match.call() m <- match.call(expand.dots = FALSE) m$sweep.out <- m$x <- m$y <- m$contrasts <- m$standardize <- m$guess.constrained.coefficients <- m$trace <- m$bound <- m$absolute.t <- NULL something.to.sweep.out <- !is.null(sweep.out) if(something.to.sweep.out) m$formula <- merge.formula(formula,sweep.out) m[[1]] <- as.name("model.frame") if(!missing(data) && !is.data.frame(data)) { m$data <- data <- as.data.frame(data) warning(paste(deparse(substitute(data)), "is not a dataframe")) } m <- eval(m, parent.frame())# not just 'data' weights <- model.extract(m, weights) Y <- model.extract(m, "response") Terms <- terms(formula, data = data) X <- model.matrix(Terms, m, contrasts) X.names <- dimnames(X)[[2]] trace <- as.logical(trace) X.to.C <- X Y.to.C <- Y if(weighted <- length(weights)) { Y.to.C <- Y.to.C * (w <- sqrt(weights)) X.to.C <- X.to.C * w } if(something.to.sweep.out) { sweep.out[[3]] <- sweep.out[[2]] sweep.out[[2]] <- formula[[2]] X.sweep.out <- model.matrix(terms(sweep.out, data = data), m, contrasts) X.sweep.out.names <- dimnames(X.sweep.out)[[2]] if(weighted) X.sweep.out <- X.sweep.out * w name.matches <- match(X.sweep.out.names,X.names) all.matched <- !any(is.na(name.matches)) if(!all.matched) warning("Variables in 'sweep.out' are not a subset of variables in 'formula'") name.matches <- name.matches[!is.na(name.matches)] if(some.matched <- length(name.matches)) { X.to.C <- X.to.C[,-name.matches,drop = FALSE] X.names <- X.names[-name.matches] if(!length(X.to.C)) stop("you cannot sweep out all the variables") } X.so.qr <- qr(X.sweep.out) if( X.so.qr$rank != ncol(X.sweep.out) ) warning("Matrix built from variables in 'sweep.out' is rank deficient") X.so.coefficients <- qr.coef (X.so.qr, Y.to.C) X.so.X <- qr.coef (X.so.qr, X.to.C) X.so.Y.fit <- qr.fitted(X.so.qr, Y.to.C) X.to.C <- qr.resid (X.so.qr, X.to.C) Y.to.C <- qr.resid (X.so.qr, Y.to.C) } X.to.C.stds <- apply(X.to.C,2,sd) if( all(X.to.C.stds < sqrt(.Machine$double.eps) ) ){ stop("Transformed X matrix contains only constant columns") } if(standardize) { if(any(i <- X.to.C.stds < sqrt(100 * .Machine$double.eps) * max(X.to.C.stds))) stop("Transformed variable matrix has constant column ", which(i), "; set standardize = FALSE") X.to.C <- sweep(X.to.C, 2, X.to.C.stds, "/") } n <- nrow(X.to.C) p <- ncol(X.to.C) if(!absolute.t) { rnk <- (X.to.C.qr <- qr(X.to.C))$rank if(rnk != p && p < n) warning("X Matrix (transformed variables) has rank ",rnk, " < p = ",p,", i.e., is deficient") else if (rnk == 0) stop("Matrix built from transformed variables is null matrix") t0 <- sum(abs(qr.coef(X.to.C.qr, Y.to.C))[1:rnk]) if (any(bound > 1)) stop("'bound'(s) must be between 0 and 1 if 'absolute.t' is false") bound <- (relative.bound <- bound) * t0 } if(any(bound < 0)) stop("'bound'(s) must be non negative") if( length(guess.constrained.coefficients) != p ) stop("invalid argument for 'guess.constrained.coefficients'") keep <- c("coefficients", "fitted.values", "residuals", "success", "Lagrangian", "bound") if (1 == (num.bound <- length(bound)) ) { ## 1 bound ---------------------- fit <- .C("lasso", X = as.double(X.to.C), n = n, p = p, bound = as.double(bound), coefficients = as.double(guess.constrained.coefficients), Y = as.double(Y.to.C), fitted.values = double(n), residuals = double(n), Lagrangian = double(1), success = integer(1), trace = trace, assub = FALSE, PACKAGE = "lasso2")[keep] if (fit$success < 0) stop("Oops, something went wrong in .C(\"lasso\",..): ",fit$success) ## else drop it: fit$success <- NULL fit$xtx <- crossprod(X.to.C) fit$xtr <- crossprod(X.to.C,fit$residuals) fit$constrained.coefficients <- fit$coefficients names(fit$coefficients) <- names(fit$constrained.coefficients) <- dimnames(X.to.C)[[2]] if(standardize) { fit$X.stds <- X.to.C.stds fit$coefficients <- fit$coefficients / X.to.C.stds } if(something.to.sweep.out) { fit$fitted.values <- fit$fitted.values + X.so.Y.fit X.so.coefficients <- X.so.coefficients - X.so.X %*% fit$coefficients tmp <- fit$coefficients fit$coefficients <- rep(0,ncol(X)) names(fit$coefficients) <- dimnames(X)[[2]] fit$coefficients[X.names] <- tmp names(X.so.coefficients) <- X.sweep.out.names ind <- names(fit$coefficients[name.matches]) ##orig 1999 code : fit$coefficients[name.matches] <- X.so.coeff[ind] fit$coefficients[name.matches] <- X.so.coefficients[ind] fit$sweep.out <- list(sweep.out = sweep.out, X.sweep.out.names = X.sweep.out.names, name.matches = name.matches, all.matched = all.matched, some.matched = some.matched, X.so.rtr.inv = qr.rtr.inv(X.so.qr), X.so.X = X.so.X) } if(weighted) { fit$weights <- weights if( any(weights == 0) ) { fit$fitted.values <- X %*% fit$coefficients fit$residuals <- Y - fit$fitted.values } else { fit$fitted.values <- fit$fitted.values / w fit$residuals <- fit$residuals / w } } fit$terms <- Terms fit$call <- call fit$contrasts <- attr(X, "contrasts") fit$assign <- attr(X, "assign") if(x) fit$x <- X if(y) fit$y <- Y if(!absolute.t) fit$relative.bound <- relative.bound structure(fit, class = "l1ce", na.message = attr(m, "na.message")) } else { ##--------- more than 1 bound --------------------------------- ordered.bound <- order(bound) guess.constraint.coefficients <- rep(guess.constrained.coefficients,num.bound) res <- .C("mult_lasso", X = as.double(X.to.C), n = n, p = p, bound = as.double(bound[ordered.bound]), l = num.bound, coefficients = as.double(guess.constraint.coefficients), Y = as.double(Y.to.C), fitted.values = double(n*num.bound), residuals = double(n*num.bound), Lagrangian = double(num.bound), success = integer(1), trace = trace, PACKAGE = "lasso2")[keep] if (res$success < 0) stop("Oops, something went wrong in .C(\"mult_lasso\",..): ", res$success) ## else drop it: res$success <- NULL total.fit <- vector("list", num.bound) ind1 <- 1:n ind2 <- 1:p for(i in 1:num.bound) { resi <- res$residuals[ind1] fit <- list(coefficients = res$coefficients[ind2], fitted.values = res$fitted.values[ind1], residuals = resi, bound = res$bound[i], Lagrangian = res$Lagrangian[i], xtr = crossprod(X.to.C, resi))#FIXED! names(fit$coefficients) <- dimnames(X.to.C)[[2]] fit$constrained.coefficients <- fit$coefficients if(standardize) fit$coefficients <- fit$coefficients / X.to.C.stds if(something.to.sweep.out) { fit$fitted.values <- fit$fitted.values + X.so.Y.fit X.so.coef <- X.so.coefficients - X.so.X %*% fit$coefficients tmp <- fit$coefficients fit$coefficients <- rep(0,ncol(X)) names(fit$coefficients) <- dimnames(X)[[2]] fit$coefficients[X.names] <- tmp names(X.so.coef) <- X.sweep.out.names ind <- names(fit$coefficients[name.matches]) fit$coefficients[name.matches] <- X.so.coef[ind] } if(weighted) { if( any(weights == 0) ) { fit$fitted.values <- X %*% fit$coefficients fit$residuals <- Y - fit$fitted.values } else { fit$fitted.values <- fit$fitted.values / w fit$residuals <- fit$residuals / w } } if(!absolute.t) fit$relative.bound <- relative.bound[ordered.bound[i]] total.fit[[ordered.bound[i]]] <- fit ind1 <- ind1 + n ind2 <- ind2 + p } if(standardize) attr(total.fit, "X.stds") <- X.to.C.stds if(something.to.sweep.out) { attr(total.fit, "sweep.out") <- list(sweep.out = sweep.out, X.sweep.out.names = X.sweep.out.names, name.matches = name.matches, all.matched = all.matched, some.matched = some.matched, X.so.rtr.inv = qr.rtr.inv(X.so.qr), X.so.X = X.so.X) } attr(total.fit, "xtx") <- crossprod(X.to.C) if(weighted) attr(total.fit, "weights") <- weights attr(total.fit, "terms") <- Terms attr(total.fit, "call") <- call attr(total.fit, "contrasts") <- attr(X, "contrasts") attr(total.fit, "assign") <- attr(X, "assign") if(x) attr(total.fit, "x") <- X if(y) attr(total.fit, "y") <- Y structure(total.fit, class = "l1celist", na.message = attr(m, "na.message")) }## multi-bound case } lasso2/R/gl1ce.q0000644000176200001440000002275313732044164013050 0ustar liggesusersif(getRversion() >= "2.15.1") utils::globalVariables(c("dev.res", "mustart")) ### Copyright (C) 1999 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details ###--- FIXME : data = sys.parent() for S+ ###--- ===== glm() in R uses a more general eval() ! gl1ce <- function(formula, data = parent.frame(), weights, subset, na.action, family = gaussian, control = glm.control(...), sweep.out = ~ 1, x = FALSE, y = TRUE, contrasts = NULL, standardize = TRUE, guess.constrained.coefficients = double(p), bound = 0.5, ...) { ret.x <- x ret.y <- y ## R's family functions partly *must* work with 'y' and ## binomial()$initialize has its own 'n' used in $aic() -- YUCK!! << Martin call <- match.call() mf <- match.call(expand.dots = FALSE) mf$sweep.out <- mf$x <- mf$y <- mf$contrasts <- mf$standardize <- mf$guess.constrained.coefficients <- mf$trace <- mf$bound <- mf$family <- mf$control <- mf$... <- NULL something.to.sweep.out <- !is.null(sweep.out) if(something.to.sweep.out) mf$formula <- merge.formula(formula, sweep.out) mf[[1]] <- as.name("model.frame") if(!missing(data) && !is.data.frame(data)) { mf$data <- data <- as.data.frame(data) warning(paste(deparse(substitute(data)), "is not a dataframe")) } mf <- eval(mf, parent.frame()) weights <- model.extract(mf, weights) y <- model.extract(mf, "response") Terms <- terms(formula, data = data) X <- model.matrix(Terms, mf, contrasts) nobs <- nrow(X)# needed for R's family functions (do not use 'n', see above!) X.names <- dimnames(X)[[2]] offset <- model.extract(mf,offset) if(!is.numeric(offset)) offset <- 0 # for R (at least) if(!length(weights)) weights <- rep(1, nobs)# not length(y) which might be 2 * nobs {binomial!} else if(any(weights < 0)) stop("Negative weights not allowed") else if(any(weights == 0)) stop("Some weigthts are 0.\n", "Rather, use 'subset= *' for observations that need to be excluded") do.trace <- as.logical(control$trace) if(!inherits(family, "family")) family <- family(family) ## cat("DEBUGGING gl1ce(): family =\n"); str(family) variance.function <- family$variance if(is.R()) { link <- family$linkfun inv.link <- family$linkinv mu.eta <- family$mu.eta # == mu.eta(eta) = 1/deriv(mu(eta)) ## this is straight from glm.fit() : valideta <- family$valideta if (is.null(valideta)) valideta <- function(eta) TRUE validmu <- family$validmu if (is.null(validmu)) validmu <- function(mu) TRUE ## eval(family$initialize) ## calculates mustart and may change y and weights and set n (!) eval(family$initialize) family$deviance <- function(mu, y, weights, residuals = FALSE) { dr <- dev.res(y, mu, weights) if(residuals) { d.res <- sqrt(pmax(dr, 0)) ifelse(y > mu, d.res, -d.res) } else sum(dr) } ee <- new.env() ## assign("dev.res", family$dev.res, envir = ee) assign("dev.res", family$dev.resids, envir = ee) environment(family$deviance) <- ee if (NCOL(y) > 1) stop("y must be univariate unless binomial") eta <- ## 'etastart' and 'start' (from glm.fit()) *are* NULL family$linkfun(mustart) mu <- inv.link(eta) eta <- eta - offset } else { ##-- S-plus -- link <- family$link inv.link <- family$inverse deriv.link <- family$deriv mu <- # binomial hack -- does NOT work when y is cbind(k, n-k) !! if(family$family[[1]] == "Binomial") (y + (1/2))/(weights + 1) else (y + mean(y))/2 eta <- link(mu) - offset } if(something.to.sweep.out) { X.sweep.out <- model.matrix(terms(sweep.out, data = data), mf, contrasts) X.sweep.out.names <- dimnames(X.sweep.out)[[2]] name.matches <- match(X.sweep.out.names, X.names) all.matched <- !any(is.na(name.matches)) if(!all.matched) warning("Variables in 'sweep.out' are not a subset of those in 'formula'") name.matches <- name.matches[!is.na(name.matches)] if(some.matched <- length(name.matches)) { X.to.C <- X[, - name.matches, drop = FALSE] X.names <- X.names[ - name.matches] if(length(X.to.C) == 0) stop("Do you really want to sweep out all the variables?") } else { X.to.C <- X } } else { X.to.C <- X } stopifnot(nobs == nrow(X.to.C)) p <- ncol(X.to.C) if(length(guess.constrained.coefficients) != p) stop("invalid argument for 'guess.constrained.coefficients'") keep <- c("coefficients", "fitted.values", "residuals", "success", "Lagrangian", "bound") check <- 1 Cvector <- double(if(something.to.sweep.out) ncol(X) else ncol(X.to.C)) continue <- TRUE j <- 0 Y.to.C <- y if(do.trace) { cat("glqce() -- tracing -- before entering IRLS loop:", "\n------- nobs =",nobs,", p =",p,"; summary(weights):\n") print(summary(weights)) } while(continue) { if(is.R()) { ## mu.eta() instead of deriv() iM <- mu.eta(eta) Y.to.C <- eta + (y - mu) / iM W <- sqrt(weights*iM^2 / variance.function(mu)) } else { ## S+ M <- deriv.link(mu) Y.to.C <- eta + (y - mu) * M W <- sqrt(weights/(M^2 * variance.function(mu))) } X.to.C.w <- X.to.C * W Y.to.C <- Y.to.C * W if(something.to.sweep.out) { X.sweep.out.w <- X.sweep.out * W X.so.qr <- qr(X.sweep.out.w) X.so.coefficients <- qr.coef(X.so.qr, Y.to.C) #beta X.to.C.w <- qr.resid(X.so.qr, X.to.C.w) #Z* Y.to.C <- qr.resid(X.so.qr, Y.to.C) #Y* } X.to.C.stds <- apply(X.to.C.w, 2, sd) if( all(X.to.C.stds < sqrt(.Machine$double.eps) ) ){ stop("Transformed X matrix contains only constant columns") } if(standardize) { if(any(X.to.C.stds < sqrt(.Machine$double.eps))) stop("Matrix build from transformed variables has a constant column") X.to.C.w <- sweep(X.to.C.w, 2, X.to.C.stds, "/") } Cvector.old <- Cvector fit <- .C("lasso", X = as.double(X.to.C.w), n = as.integer(nobs), p = as.integer(p), bound = as.double(bound), coefficients = as.double(guess.constrained.coefficients), Y = as.double(Y.to.C), fitted.values = double(nobs), residuals = double(nobs), Lagrangian = double(1), success = integer(1), trace = do.trace, assub = as.logical(FALSE), PACKAGE = "lasso2")[keep] if(fit$success < 0) stop("Uups, something went wrong in the C-routine") ## else fit$success <- NULL guess.constrained.coefficients <- gamma <- fit$coefficients if(something.to.sweep.out){ Cvector <- c(X.so.coefficients, gamma) eta <- as.vector((X.to.C.w %*% gamma + X.sweep.out.w %*% X.so.coefficients)/W) }else{ Cvector <- c(gamma) eta <- as.vector((X.to.C.w %*% gamma)/W) } fit$fitted.values <- mu <- inv.link(eta + offset) ## Our new values of mu and eta j <- j + 1 continue <- j < control$maxit && sum((Cvector - Cvector.old)^2) > control$epsilon *sum(Cvector^2) if(do.trace) { cat(">>> while(continue = ", continue,") { .. } : j = ",j, " summary(W) weights:\n") print(summary(W)) ; cat("\n") } } ## while(continue) if(j == control$maxit) warning("Maximal number of iterations reached.") fit$iter <- j deviance <- family$deviance(mu, y, weights) fit$linear.predictors <- eta fit$xtx <- crossprod(X.to.C) fit$xtr <- crossprod(X.to.C,fit$residuals) fit$constrained.coefficients <- fit$coefficients names(fit$coefficients) <- names(fit$constrained.coefficients) <- dimnames(X.to.C)[[2]] if(standardize) { fit$X.stds <- X.to.C.stds fit$coefficients <- fit$coefficients/X.to.C.stds } if(something.to.sweep.out) { X.so.X <- qr.coef(X.so.qr, X.to.C*W) X.so.coefficients <- X.so.coefficients - X.so.X %*% fit$coefficients tmp <- fit$coefficients fit$coefficients <- rep(0, ncol(X)) names(fit$coefficients) <- dimnames(X)[[2]] fit$coefficients[X.names] <- tmp names(X.so.coefficients) <- X.sweep.out.names ind <- names(fit$coefficients[name.matches]) fit$coefficients[name.matches] <- X.so.coefficients[ind]#fixed "coeff" ! fit$sweep.out <- list(sweep.out = sweep.out, X.sweep.out.names = X.sweep.out.names, name.matches = name.matches, all.matched = all.matched, some.matched = some.matched, X.so.rtr.inv = qr.rtr.inv(X.so.qr), X.so.X = X.so.X) } fit$weights <- W fit$prior.weights <- weights fit$terms <- Terms fit$call <- call fit$contrasts <- attr(X, "contrasts") fit$assign <- attr(X, "assign") fit$family <- family fit$deviance <- deviance if(ret.x) fit$x <- X if(ret.y) fit$y <- y fit$df.res <- nobs - p # must always have full rank here ## if(!absolute.t) ## fit$relative.bound <- relative.bound structure(fit, class = c("gl1ce", "l1ce"), na.message = attr(mf, "na.message")) } ## trivial accessor function: family.gl1ce <- function (object, ...) object$family lasso2/R/plot.q0000644000176200001440000000111607430731324013020 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details plot.l1celist <- function(x, plot=TRUE, all=TRUE, constrained=FALSE, type = "b", xlab = "bounds", ylab = "coeff | bounds", ...) { bounds <- aux(x) mat.of.coef <- coef(x, all=all, constrained = constrained) if(plot) { matplot(bounds[, 1], mat.of.coef, type = type, xlab = xlab, ylab = ylab, ...) rug(bounds[, 1]) } invisible(list(bounds=bounds, mat.of.coef=mat.of.coef)) } lasso2/R/labels.q0000644000176200001440000000261607430271045013311 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA labels.l1ce <- function(object, ...) { TL <- object$terms if(!is.null(TL)) { TL <- attr(TL, "term.labels") TA <- object$assign if(!is.null(TA)) { TA <- names(TA)#c TL <- TL[match(TA, TL, 0)] } } TL } labels.l1celist <- function(object, ...) { TL <- attr(object, "terms") if(!is.null(TL)) { TL <- attr(TL, "term.labels") TA <- object$assign if(!is.null(TA)) { TA <- names(TA) TL <- TL[match(TA, TL, 0)] } } TL } lasso2/R/formula.util.q0000644000176200001440000000202407767424526014502 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details is.formula <- function(x) inherits(x, "formula") merge.formula <- function(x, y, ...) { if(!is.formula(x) || length(x) != 3) stop("First argument is invalid") if(!is.formula(y)) stop("Second argument is invalid") if(length(list(...))) warning("extraneous arguments discarded") is.gEnv <- function(e) identical(e, .GlobalEnv) str <- paste(c(deparse(x[[2]]), "~", deparse(x[[3]]), "+", deparse(y[[length(y)]])), collapse = "") f <- as.formula(str) ## MM: try to keep environment (where reasonable) ex <- environment(x) ey <- environment(y) if(!is.gEnv(ex)) { environment(f) <- ex if(!is.gEnv(ey) && !identical(ex,ey)) { warning("`x' and `y' have different environments; x's is used") } } else if(!is.gEnv(ey)) environment(f) <- ey f } lasso2/R/vcov.q0000644000176200001440000000600107637062273013026 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### --> ../COPYRIGHT for more details ###-- vcov() is in base since 1.6.x : if(paste(R.version$major, R.version$minor, sep=".") < 1.6) vcov <- function(object, ...) UseMethod("vcov") vcov.l1ce <- function(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, ...) { type <- match.arg(type) xtx <- object$xtx cov.mat <- switch(type, OPT = opt.covmat(xtx, object$xtr, object$bound, object$Lagrangian), Tibshirani = tib.covmat(xtx, gen.inverse.diag, object$constrained.coefficients, object$Lagrangian) ) cov.mat <- solve(cov.mat) df <- tr(junk <- cov.mat %*% xtx) cov.mat <- junk %*% cov.mat if(!is.null(object$X.stds)) { X.stds <- as.vector(object$X.stds) p <- length(X.stds) cov.mat <- cov.mat/X.stds cov.mat <- cov.mat/X.stds[rep(1:p,rep(p,p))] } if(!is.null(object$sweep.out)) { if(!object$sweep.out$all.matched) warning("Variables in sweep.out were not a subset of variables in model. Results could be meaningless.") psi1 <- object$sweep.out$X.so.rtr.inv psi2 <- cov.mat psi12 <- object$sweep.out$X.so.X %*% psi2 psi1 <- psi1 + psi12 %*% t(object$sweep.out$X.so.X) p1 <- nrow(psi1) p2 <- nrow(psi2) ## 2 x 2 block matrix -- wouldn't rbind(cbind(),cbind()) be faster? FIXME cov.mat <- matrix(0,p1+p2,p1+p2) cov.mat[1:p1,1:p1] <- psi1 cov.mat[(p1+1):(p1+p2),(p1+1):(p1+p2)] <- psi2 cov.mat[(p1+1):(p1+p2),1:p1] <- -t(psi12) cov.mat[1:p1,(p1+1):(p1+p2)] <- -psi12 names2 <- names(object$coefficients) if(paste(R.version$major, R.version$minor, sep=".") < "1.4.1") { ## in R versions <= 1.4.0, most qr.* didn't have dimnames names1 <- object$sweep.out$X.sweep.out.names names1 <- c(names1, names2[! names2 %in% names1]) } else ## X.so.X has dimnames in R version >= 1.4.1 names1 <- do.call("c",dimnames(object$sweep.out$X.so.X)) dimnames(cov.mat) <- list(names1, names1) cov.mat <- cov.mat[names2,names2] df <- df+p1 } list(cov.unscaled=cov.mat, df = c(df,length(object$fitted)-df)) } vcov.l1celist <- function(object, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, ...) { type <- match.arg(type) res <- list() for(i in 1:length(object)) res[[i]] <- vcov(object[[i]],type,gen.inverse.diag) res } opt.covmat <- function(xtx, xtr, bound, Lagrangian) { if( bound == 0 ) stop("\"bound\" must be unequal zero when calculating covariance matrix") if( abs(Lagrangian) < sqrt(.Machine$double.eps) ){ xtx }else{ xtx + xtr%*%t(xtr)/(bound*Lagrangian) } } tib.covmat <- function(xtx, gen.inverse.diag, beta, Lagrangian) { ind <- abs(beta) > sqrt(.Machine$double.eps) beta[ind] <- 1/abs(beta[ind]) beta[!ind] <- gen.inverse.diag xtx + Lagrangian*diag(beta) } lasso2/R/summary.q0000644000176200001440000000567510010405430013535 0ustar liggesusers### Copyright (C) 1998, 1999 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA summary.l1ce <- function(object, correlation = TRUE, type = c("OPT", "Tibshirani"), gen.inverse.diag = 0, sigma = NULL, ...) { type <- match.arg(type) coef <- coef(object) cnames <- labels(coef) resid <- resid(object) ## fv <- fitted(object) covdf <- vcov(object,type,gen.inverse.diag) sigma.provided <- !missing(sigma) if(!sigma.provided) sigma <- sqrt(deviance(object)/covdf$df[2]) se <- as.vector(sqrt(diag(covdf$cov.unscaled))) correl <- if(correlation) { p <- length(se) correl <- covdf$cov.unscaled/se correl/se[rep(1:p,rep(p,p))] } ## else NULL coef <- array(coef, c(p, 4)) dimnames(coef) <- list(cnames, c("Value", "Std. Error", "Z score", "Pr(>|Z|)")) coef[, 2] <- se %o% sigma coef[, 3] <- coef[, 1]/coef[, 2] coef[, 4] <- 2*(1-pnorm(abs(coef[,3]))) keep <- c("call", "terms", "bound", "relative.bound", "Lagrangian") object <- object[keep[!is.na(match(keep,names(object)))]] object$residuals <- resid object$coefficients <- coef object$sigma <- sigma object$sigma.provided <- sigma.provided object$df <- covdf$df object$cov.unscaled <- covdf$cov.unscaled object$correlation <- correl class(object) <- "summary.l1ce" object } summary.gl1ce <- function(object, dispersion = NULL, correlation=FALSE, ...) { if(correlation) stop("The `correlation' argument is not yet implemented for gl1ce objects") ## else coef <- coef(object) if(is.null(cnames <- names(coef))) cnames <- c("(Intercept)", labels(object)) names(coef) <- cnames coef <- cbind(Value = coef)# same matrix structure for consistency keep <- c("call", "terms", "bound", "Lagrangian", "family", "iter") object <- c(object[keep[!is.na(match(keep, names(object)))]], list(residuals = residuals(object, type="deviance"), coefficients = coef)) class(object) <- "summary.gl1ce" object } lasso2/R/fitted.q0000644000176200001440000000203507023671614013325 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA fitted.l1ce <- function(object, ...){ object$fitted } fitted.l1celist <- function(object, ...){ do.call("cbind", lapply(object, "[[", "fitted")) } lasso2/R/misc.q0000644000176200001440000000420407454261553013006 0ustar liggesusers### Copyright (C) 1998 ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA "[[.l1celist" <- function(x, ..., drop = TRUE) { class(x) <- NULL val <- NextMethod("[[") if( !length(val) ) return (NULL) xattr <- attributes(x) nattr <- names(xattr) val[nattr] <- xattr[nattr] class(val) <- "l1ce" val } "[.l1celist" <- function(x, ..., drop = TRUE) { class(x) <- NULL val <- NextMethod("[") junk <- do.call("c", lapply(val,length)) val <- val[junk!=0] if(!length(val)) return (NULL) if(drop && length(val) == 1) { val <- val[[1]] xattr <- attributes(x) nattr <- names(xattr) val[nattr] <- xattr[nattr] class(val) <- "l1ce" } else { attributes(val) <- attributes(x) class(val) <- "l1celist" } val } aux <- function(object, ...) UseMethod("aux") aux.l1celist <- function(object, ...) { rbnd <- do.call("c", lapply(object, "[[", "relative.bound")) structure(cbind(rbnd, do.call("c", lapply(object, "[[", "bound")), do.call("c", lapply(object, "[[", "Lagrangian")) ), dimnames=list(NULL, c(if(!is.null(rbnd))"rel.bound", "abs.bound", "Lagrangian"))) } ## Trace tr <- function(mat) { dims <- dim(mat) if((length(dims) != 2) || dims[1] != dims[2]) stop("This function is only defined for square matrices") sum(diag(mat)) } lasso2/R/residuals.q0000644000176200001440000000436707430265714014055 0ustar liggesusers### Copyright (C) 1998, 1999 ### Justin Lokhorst ### Berwin A. Turlach ### Bill Venables ### ### This library is free software; you can redistribute it and/or ### modify it under the terms of the GNU Library General Public ### License as published by the Free Software Foundation; either ### version 2 of the License, or (at your option) any later version. ### ### This library is distributed in the hope that it will be useful, ### but WITHOUT ANY WARRANTY; without even the implied warranty of ### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU ### Library General Public License for more details. ### ### You should have received a copy of the GNU Library General Public ### License along with this library; if not, write to the Free Software ### Foundation, Inc., 59 Temple Place, Suite 330, Boston, ### MA 02111-1307 USA residuals.l1ce <- function(object, type = c("working", "pearson", "deviance"), ...) { type <- match.arg(type) switch(type, working = object$residuals, pearson =, deviance = if(is.null(object$weights)) object$residuals else residuals.glm(object, "pearson") ) } residuals.l1celist <- function(object, type = c("working", "pearson", "deviance"), ...) { type <- match.arg(type) resid <- do.call("cbind", lapply(object, "[[", "residuals")) weights <- attr(object, "weights") switch(type, working = resid, pearson =, deviance = if(is.null(weights)) resid else sqrt(weights)*resid ) } residuals.gl1ce <- function(object, type = c("deviance", "pearson", "working", "response"), ...) { type <- match.arg(type) 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[.l1celist Extract Parts of a 'l1celist' Object aux Extract Auxiliary Information From an Object aux.l1celist Use 'aux()' on a 'l1celist' object coef.l1ce Coefficients of an 'l1ce' Object coef.l1celist Coefficients of an 'l1celist' Object deviance.gl1ce Deviance Method for 'gl1ce' Objects deviance.l1ce Deviance Method for 'l1ce' and 'l1celist' Objects fitted.l1ce Fitted Values for 'l1ce', 'l1celist' and 'gl1ce' Objects gcv Generalised Cross-Validation Score gcv.l1ce 'gcv()' Methods for 'l1ce' and 'l1celist' Objects. gl1ce Generalized Regression With L1-constraint on the Parameters gl1ce.object Generalized L1 Constrained Estimation Model Object is.formula Tests for Formula Objects l1ce Regression Fitting With L1-constraint on the Parameters l1ce.object L1 Constrainted Estimation Model Object l1celist.object Object of Several L1 Constrained Estimation Models labels.l1ce 'Labels' Method for 'l1ce' and 'l1celist' Objects merge.formula Merge Formula With Right Hand Side of Second Formula plot.l1celist Plot Method for 'l1celist' Objects predict.gl1ce Prediction Method for a 'gl1ce' Object predict.l1ce Predict Method for 'l1ce' Objects print.l1ce Print Methods for 'l1ce', 'l1celist' and 'gl1ce' Objects qr.rtr.inv Reconstruct the Inverse of R'R from a QR Object residuals.gl1ce Compute Residuals for 'gl1ce' Objects residuals.l1ce Residuals of 'l1ce' or 'l1celist' Objects summary.gl1ce Summary Method for Generalized L1 Constrained Regression Models summary.l1ce Summary Method for "l1ce" Objects (Regression with L1 Constraint) tr Trace of a Matrix vcov.l1ce Variance-Covariance Matrix of 'l1ce' or 'l1celist' Objects lasso2/inst/0000755000176200001440000000000012234406423012432 5ustar liggesuserslasso2/inst/doc/0000755000176200001440000000000014127633221013200 5ustar liggesuserslasso2/inst/doc/Manual-vignette.pdf0000644000176200001440000053347014127633221016747 0ustar liggesusers%PDF-1.5 % 7 0 obj << /Length 65 /Filter /FlateDecode >> stream x3T0BC]=CS0eaUeg```bQĆHBA=sM\@!(Nq 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Turlach Bill Venables Copyright (C) 1999 Justin Lokhorst Copyright (C) 2002 Martin Maechler This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.