similar to: Bootcov for two stage bootstrap

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:

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.rq, but meanwhile this seems to be a good alternative.

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

Hi all, I am trying to get bootstrap resampled estimates of 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,

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,

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 individual observations. Is that right, and is

The rms ("Regression Modeling Strategies") package has undergone a massive update. The entire list of updates is at the bottom of this note. CRAN has the update for linux and will soon have it for Windows and Mac - check http://cran.r-project.org/web/packages/rms/ for availability. This rms update relies on a major update of the Hmisc package. The most user-visible changes are:

You might also want to recruit on the R news group... r-help at stat.math.ethz.ch. -Greg > -----Original Message----- > From: Jan Ivanouw [mailto:ivanouw at post8.tele.dk] > Sent: Wednesday, January 16, 2002 8:28 AM > To: s-news at lists.biostat.wustl.edu > Subject: [S] Study group on bootstrap > > > Hi > I am looking for participants for an e-mail

Hello, I'm working on 30 natural populations of Aster amellus L. a threatened plant species. My aim is to see if the population size influence on several fitness trait. I build a multi-factorial ANOVA. The independents variables are altitude, humidity, soil component, maintenance .... and population density. Unfortunately my data have some heteroscedasticity even after classical

I'm trying to adapt some S-Plus scripts to run in R (1.2.2, Windows). In one of these scripts, I've bootstrapped the prediction success rate under the discriminant function (lda). The bootstrap() functions are proprietary to S-Plus and there aren't exact equivalents in R. The closest is Canty's library boot based on the Davidson and Hinkley book. Unfortunately, I

Dear Faiz, Bootstrapping R^2 using Boot() is straightforward: Simply write a function that returns R^2, possibly in a vector with the regression coefficients, and use it as the f argument to Boot(). That will get you, e.g., bootstrapped confidence intervals for R^2. (Why you want that is another question.) See the example in ?Boot that shows how to bootstrap the estimated error variance (without

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

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

The Hmisc and Design libraries have been updated respectively to versions 1.4-2 and 1.1-1. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r . Web sites for the libraries are http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html and http://hesweb1.med.virginia.edu/biostat/s/Design.html . Thanks to Xiao Gang Fan for porting the libraries

The Hmisc and Design libraries have been updated respectively to versions 1.4-2 and 1.1-1. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r . Web sites for the libraries are http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html and http://hesweb1.med.virginia.edu/biostat/s/Design.html . Thanks to Xiao Gang Fan for porting the libraries

Good Morning: I posted an initial question a few days ago and I received some good advice from two R experts. I have re-examined the Davison-Hinkley text paying close attention to the examples of the boot() and boot.ci() in that text and the single example of a similar process in the MASS book (not the MASS package manual as I initially misunderstood). I think I understand how the stratified

I have a dataset at a hospital level (as opposed to the patient level) that contains number of patients experiencing events (call this number y), and the number of patients eligible for such events (call this number n). I am trying to model logit(y/n) = XBeta. In SAS this can be done in PROC LOGISTIC or GENMOD with a model statement such as: model y/n = <predictors>;. Can this be done

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

Hi folks, Using R Version 1.6.2 (2003-01-10) on SuSE Linux 7.2, I just installed Hmisc_1.5-3.tar.gz and Design_1.1-5.tar.gz These were taken from http://hesweb1.med.virginia.edu/biostat/s/library/r Checked the dependencies: Hmisc: grid, lattice, mva, acepack -- all already installed Design: Hmisc, survival -- survival already installed, so installed Hmisc first All seems to go

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code