similar to: Fwd: Making rq and bootcov play nice

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:

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

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

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,

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

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,

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

Dear all, I am reading the cch function source code. But I can not understand the following codes. Please help me. What's the fitter here? fitter <- get(method) out <- fitter(tenter = tenter, texit = texit, cc = cc, id = id, X = X, ntot = nn, robust = robust) [[alternative HTML version deleted]]

hi, I applied coxph to my matrix of 300 samples and 215 variables and got the following error Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: Ran out of iterations and did not converge in: fitter(X, Y, strats, offset, init, control, weights = weights, 26% of time data is censored and here

Hi, I have a script that takes a subset of genes on a microarray and tries to fit a coxph model to the expression values for each gene. This seems to work fine but in some cases it produces warnings and/or errors. For example: Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: In fitter(X, Y,

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

Full_Name: Ben Cooper Version: 1.5.0 OS: linux Submission from: (NULL) (134.174.187.90) The help page for glm.nb (in MASS package) says that it takes "Any other arguments for the glm() function except family" One such argument is start "starting values for the parameters in the linear predictor." However, when called with starting values glm.nb returns: Error in

Hi, am very new to R and I've written an optim function, but can't get it to work least.squares.fitter<-function(start.params,gr,low.constraints,high.constraints,model.one.stepper,data,scale,ploton=F) { result<-optim(par=start.params,method=c('Nelder-Mead'),fn=least.squares.fit,lower=low.constraints,upper=high.constraints,data=data,scale=scale,ploton=ploton)

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")

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:

R 2.8.1 windows XP I am trying to learn how to use the boot function to perform a bootstrap of a regression. I have written a short trial program, shown below. Clearly I have done something wrong as the output of each of the 100 bootstrap values for the regression are exactly the same - there does not appear to be any bootstrap respampling!. What have I done wrong? # Define function to be run.

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

Good Afternoon, I'm trying to create a graph that displays the best way the following information. ??? For instance organized by bar graph, A, B, C Source X1000s X600s X500s X250s X100s X50s X10s X5s X3s X1s 1 A 47 63 75 116 125 129 131 131 131 131 2 B 37 64 45 11 25 19 61 131 186 186 3 C 17 62 25 66 12 29 91 171 186

R list: I am using a 'for' loop to run a number of different models (stratified by different variables) with coxph. The data becomes sparse when some strata are used causing the model to become unstable. The following error occurs and the analysis is terminated. >Error in fitter(X, Y, strats, offset, init, control, weights = weights, : (converted from warning) Loglik