similar to: Bootstrap confidence intervals using bootcov from the rms package

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Hello R-help, Thanks for everyone's very helpful suggestions so far. I am now trying to use aregImpute for my missing data imputation. Here are the code and error messages. Any suggestions would be very much appreciated. Sincerely, Anders Corr ######################################## #Question for R-Help on aregImpute ######################################## #DOWNLOAD DATA (61Kb)

Hello, I would like to use the aregImpute and fit.mult.impute to impute missing values for my dataset and then conduct logistic regression analyses on the data, taking into account that we imputed values. I have no problems imputing the values using aregImpute, but I am getting an error at the fit.mult.impute stage. Here is some sample code (I actually have more observations and variables to

Hello, I'm working on a Cox Proportional Hazards model for a cancer data set that has missing values for the categorical variable "Grade" in less than 10% of the observations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using

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:

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

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

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.

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

From: <david_foreman at doctors.org.uk> Subject: [R] Re: extracting datasets from aregImpute objects To: <r-help at stat.math.ethz.ch> Message-ID: <1092391719_117440 at drn10msi01> Content-Type: text/plain; charset="us-ascii" I've tried doing this by specifying x=TRUE, which provides me with a single imputation, that has been useful. However, the help file

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

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

I am a new user of R for Windows, enthusiast about the many functions of the Design and Hmisc libraries. I combined the results of a Cox regression model after multiple imputation (of missing values in some covariates). Now I got my vector of coefficients (and of standard errors). My question is: How could I use directly that vector to run programs such as 'nomogram', 'calibrate',

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, 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 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 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 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,

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: