search for: bootcov

I have a quick question, and I apologize in advance if, in asking, I expose my woeful ignorance of R and its packages. I am trying to use the bootcov function to estimate the standard errors for some regression quantiles using a cluster bootstrap. However, it seems that bootcov passes arguments that rq.fit doesn't like, preventing the command from executing. Here is an example: e<-bootcov(rq(y~x),clust,B=10,fitter=rq.fit) (where clust...

Hi, I am trying to figure out exactly what the bootcov() function in the Design package is doing within the context of clustered data. From reading the documentation/source code it appears that using bootcov() with the cluster argument constructs standard errors by resampling whole clusters of observations with replacement rather than resampling indivi...

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the er...

John, You can make a local version of bootcov which either: deletes these arguments from the call to fitter, or modify the switch statement to include rq.fit, the latter would need to also modify rq() to return a fitFunction component, so the first option is simpler. One of these days I'll incorporate clustered se's into summary....

Dear users, I'm trying to implement the nonparametric "two-stage" bootstrap (Davison and Hinkley 1997, pag 100-102) in R. As far as I understood, 'bootcov' is the most appropriate method to implement NONPARAMETRIC bootstrap in R when you have clustered data (?). I read the 'bootcov' manual but I still have a few questions: 1 - When the variable 'cluster' is introduced, then only clusters will be resampled (with replacement)? 2 -...

Dear list, I made a logistic regression model (MyModel) using lrm and penalization by pentrace for data of 104 patients, which consists of 5 explanatory variables and one binary outcome (poor/good). Then, I found bootcov and robcov function in rms package for calculation of confidence range of coefficients and odds ratio by bootstrap covariance matrix and Huber-White sandwich method, respectively. > MyModel.boot <- bootcov(MyModel, B=1000, coef.reps=T) > MyModel.robcov <- robcov(MyModel) > anova(My...

...f covariates in a Cox model using cph (Design library). Using the following I get the error: > ddist2.abr <- datadist(data2.abr) > options(datadist='ddist2.abr') > cph1.abr <- cph(Surv(strt3.abr,loc3.abr)~cov.a.abr+cov.b.abr, data=data2.abr, x=T, y=T) > boot.cph1 <- bootcov(cph1.abr, B=100, coef.reps=TRUE, pr=T) 1 Error in oosl(f, matxv(X, cof), Y) : not implemented for cph models > Removing coef.reps argument works fine, but I really need the coefficients if at all possible. I cant find anything in the help files suggesting that I cant use coef.reps in a cph mode...

...smusmc.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 bootstrap coefficients were present * For plot.Predict made default gray scale shaded confidence bands darker * For bootcov exposed eps argument to fitters and default to lower value * Fix...

Hi, Sorry for repeated question. I performed logistic regression using lrm and penalized it with pentrace function. I wanted to get confidence intervals of odds ratio of each predictor and summary(MyModel) gave them. I also tried to get bootstrapping standard errors in the logistic regression. bootcov function in rms package provided them. Then, I found that the confidence intervals provided by bootstrapping (bootcov) was narrower than CIs provided by usual variance-covariance matrix in the followings. My data has no cluster structure. I am wondering which confidence interval is better. I guess...

Hi, I am trying to convert a colleague from using SPSS to R, but am having trouble generating a result that is similar enough to a bootstrapped cox regression analysis that was run in SPSS. I tried unsuccessfully with bootcens, but have had some success with the bootcov function in the rms package, which at least generates confidence intervals similar to what is observed in SPSS. However, the p-values associated with each predictor in the model are not really close in many instances. Here is the code I am using: formula=Surv(months, recidivate) ~ fac1 + fac2 +...

...in's degrees of freedom for t-statistics for scalar parameter tests, as well as to compute a missing information index. In Design, glmD works much more reliably. plotmath is now used by plot.Design. Bill Pikounis' <v_bill_pikounis at merck.com> improvements have been incorporated in bootcov to allow both cluster and group to be specified. offsets now work in lrm when NAs are present, and predict.Design now works with offsets. bootcov now works with glmD, and summary and print work for glmD (thanks: Fredrik Lundgren <fredrik.lundgren at norrkoping.mail.telia.com>). validate....

...in's degrees of freedom for t-statistics for scalar parameter tests, as well as to compute a missing information index. In Design, glmD works much more reliably. plotmath is now used by plot.Design. Bill Pikounis' <v_bill_pikounis at merck.com> improvements have been incorporated in bootcov to allow both cluster and group to be specified. offsets now work in lrm when NAs are present, and predict.Design now works with offsets. bootcov now works with glmD, and summary and print work for glmD (thanks: Fredrik Lundgren <fredrik.lundgren at norrkoping.mail.telia.com>). validate....

...ight these two responses by the number of 'successes' and 'failures' for that hospital, respectively? I would like to avoid the restructuring, and I understand that the use of the weight function is not compatible with a lot of the validation functions available in Hmisc (validate, bootcov, etc.). Cody Hamilton, Ph.D Institute for Health Care Research and Improvement Baylor Health Care System (214) 265-3618 This e-mail, facsimile, or letter and any files or attachments transmitted with it contains information that is confidential and privileged. This information is intende...

Hi, I am trying to convert a colleague from using SPSS to R, but am having trouble generating a result that is similar enough to a bootstrapped cox regression analysis that was run in SPSS. I tried unsuccessfully with bootcens, but have had some success with the bootcov function in the rms package, which at least generates confidence intervals similar to what is observed in SPSS. However, the p-values associated with each predictor in the model are not really close in many instances. Here is the code I am using: formula=Surv(months, recidivate) ~ fac1 + fac2 +...

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

...ectly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use: export(asis,bj,bjplot,bootBCa,bootcov,bootplot,bplot,calibrate,cph,catg,combineRelatedPredictors,confplot,contrast,coxphFit,cph,cr.setup,datadist,effective.df,fastbw,formatNP,gendata,gIndex,GiniMd,Glm,Gls,groupkm,Hazard,hazard.ratio.plot,histdensity,"%ia%",ie.setup,interactions.containing,legend.nomabbrev,lm.pfit,lrm,lrtest,l...

Hello, I'm an MD working in an eye clinic. I'm learning by myself to use R for use in my research works and for implementation in a software project. There are some authors who recomends the use of Cox regression as a substitute for Logistic regression (<a href="http://www.biomedcentral.com/1471-2288/3/21.pdf"> Barros AJD, Hirakata VN. BMCMedical Research Methodology, 2003;

....create.modify.check text html latex example missing link(s): hist.data.frame merge.levels dataRep text html latex example missing link(s): crosstabs deff text html latex example missing link(s): bootcov robcov impute text html latex example missing link(s): na.include labcurve text html latex example missing link(s): key mtitle text html latex example missing link(s): psta...

...age. More information is at http://biostat.mc.vanderbilt.edu/Rrms Changes in version 3.2-0 (2011-02-14) * Changed to be compatible with survival 2.36-3 which is now required * Added logLik.rms and AIC.rms functions to be compatible with standard R * Fixed oos.loglik.Glm * Fixed bootcov related to nfit='Glm' * Fixed (probably) old bug in latexrms with strat predictors Frank -- Frank E Harrell Jr Professor and Chairman School of Medicine Department of Biostatistics Vanderbilt University _______________________________________________ R-packag...

Hello, Does R have any capabilities (or are there any add on packages) which can do estimation of standard statistical models (means, regression, logistic regression, etc) which take into account not only weights (e.g. post-stratification weights) but also the sample design, such as stratification and clustering information (to compute a robust taylor linearized variance estimator, for