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Hi; I want to use bootstrap .632 with predab.resample, but the description is not clear for me. Can any body give me an example please. Thanks; Azad. [[alternative HTML version deleted]]

...#fine when no subset is used, but the following two don't work: > validate(ovarian.cph, subset=ovarian$ecog.ps==2) Error in order(c(1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, : argument lengths differ > validate(ovarian.cph, subset=ovarian[ovarian$ecog.ps==2,]) Error in predab.resample(fit, method = method, fit = cox.fit, measure = discrim, : length of subset does not match # rows used in fit Any suggestions? Also, is it possible to use the subset option with calibrate as well? It would be good to be able to plot the cox fit vs. the KM fit for different age gr...

...(or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable selection is carried out (using AIC as criterion). validate calls predab.resample, which in turn calls fastbw (from the Design package, by Harrell). fastbw " Performs a slightly inefficient but numerically stable version of fast backward elimination on factors, using a method based on Lawless and Singhal (1978). This method uses the fitted complete model (...)&quo...

...Fixed inconsistency in predictrms where predict() for Cox models used a design matrix that was centered on medians and modes rather than means (thanks: David van Klaveren <d.vanklaveren.1@erasmusmc.nl>) * Added mean absolute prediction error to Rq output * Made pr argument passed to predab.resample more encompassing * Fixed logLik method for ols * Made contrast.rms and summary.rms automatically compute bootstrap nonparametric confidence limits if fit was run through bootcov * Fixed bug in Predict where conf.type=''simultaneous'' was being ignored if boot...

...ined if conf='bands' and labelc was FALSE) * In survfit.cph, fixed bug by which n wasn't always defined * In cph, put survival::: on exact fit call * Quit ignoring zlim argument in bplot; added xlabrot argument * Added caption argument for latex.anova.rms * Changed predab to not print summaries of variables selected if bw=TRUE * Changed predab to pass force argument to fastbw * fastbw: implemented force argument * Added force argument to validate.lrm, validate.bj, calibrate.default, calibrate.cph, calibrate.psm, validate.bj, validate.cph, validate.ols...

...ined if conf='bands' and labelc was FALSE) * In survfit.cph, fixed bug by which n wasn't always defined * In cph, put survival::: on exact fit call * Quit ignoring zlim argument in bplot; added xlabrot argument * Added caption argument for latex.anova.rms * Changed predab to not print summaries of variables selected if bw=TRUE * Changed predab to pass force argument to fastbw * fastbw: implemented force argument * Added force argument to validate.lrm, validate.bj, calibrate.default, calibrate.cph, calibrate.psm, validate.bj, validate.cph, validate.ols...

...azard.ratio.plot,histdensity,"%ia%",ie.setup,interactions.containing,legend.nomabbrev,lm.pfit,lrm,lrtest,lsp,matinv,matrx,Newlabels,Newlevels,nomogram,num.intercepts,ols,ols.influence,oos.loglik,pantext,Penalty.matrix,Penalty.setup,pentrace,perimeter,perlcode,plot.xmean.ordinaly,pol,pphsm,predab.resample,Predict,psm,rcs,related.predictors,reVector,robcov,Rq,sascode,scored,sensuc,setPb,show.influence,specs,strat,Surv,"[.Surv",survdiffplot,survest,Survival,survplot,univarLR,validate,val.prob,val.probg,val.surv,vif,which.influence) importFrom(Hmisc) S3method(anova, rms) S3method(la...

...tion of the covariance matrix (he is referring to discriminant analysis), and thus he uses essentially stratified bootstrap samples. Interestingly, the "boot" function (boot library) says "For nonparametric multi-sample problems stratified resampling is used.". As well, the predab.resample (Design library) says "group: a grouping variable used to stratify the sample upon bootstrapping. This allows one to handle k-sample problems, (...)". That the authors of boot and Design are using stratified resampling indicates to me that this might be the obvious, unproble...

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