search for: coxmeful

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #

Hi there, I have a question on fitting data by coxme. In particular I want to fit a random intercept and random slope cox model. Using the rats dataset as an example, I generated another covariate x2 and want to specify a random slope for x2. Here is my code: x2=matrix(rep(runif(50), 3), 50, 3) x2=as.vector(t(x2)) rats2=cbind(rats, x2) But when I used the coxme function as follows, it gave

Dear all, I have encountered the following problem where coxme seems to allow model with only random effect in R 2.11.1 but not in R 2.13.0. Following is the error message using rat example data. Any comment on this is appreciated. In R2.13 > library(coxme) > rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats) > rat0 <- coxme(Surv(time, status) ~ (1|litter), rats)

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

Hi I'm encountering an error/warning when doing multiple comparisons with the package multcomp on a coxme model. My data: I'm looking at the removal of brood from the nest according to three treatments I applied on the brood. The brood and the workers caring about the brood in the nest, belonged to different colonies. Factor: treatment (3 levels: tx,uv,meta) Random effect 1: origin of

Dear all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) >

Dear R experts; I am trying to extract the p values from a coxme object (package coxme). I can see the value in the model output, but I wanted to have the result with a higher number of decimal places. I have searched the mailing list and followed equivalent suggestions for nlme/lme objects, but I wasn't successful. Thanks; Catarina [[alternative HTML version deleted]]

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Hi I would really appreciate some help with my code for coxme... My data set I'm interested in survival of animals after an experiment with 4 treatments, which was performed on males and females. I also have two random factors: Response variable: survival (death) Factor 1: treatment (4 levels) Factor 2: sex (male / female) Random effects 1: person nested within day (2 people did

R version 2.9.0 running on Centos (Red Hat linux). I have a pair of packages coxme and bdsmatrix. The latter is installed in my local library (I don't have permission for global install at work.) That is, it is in the location pointed to in R_LIBS_USER. In R, the command library(bdsmatrix) works fine. Coxme depends on bdsmatrix, in fact uses some cross-calls to it's C routines, which

Hello, I am running R 2.6.1 on windows xp I am trying to fit a cox proportional hazard model with a shared Gaussian frailty term using coxme My model is specified as: nofit1<-coxme(Surv(Age,cen1new)~ Sex+bo2+bo3,random=~1|isl,data=mydat) With x1-x3 being dummy variables, and isl being the community level variable with 4 levels. Does anyone know if there is a way to get the standard error

Almost all of the coxme package and an increasing amount of the survival package are now written in noweb, i.e., .Rnw files. It would be nice to process these using the Sweave function + a special driver, which I can do using a modified version of Sweave. The primary change is to allow the following type of construction <<coxme>> coxme <- function(formula, data, subset, blah blah

Dear R users, I'm using the function coxme of the package coxme in order to build Cox models with complex random effects. Unfortunately, I sometimes get surprising estimations of the variances of the random effects. I ran models with different fixed covariates but always with the same 3 random effects defined by the argument varlist=coxmeMlist(list(mat1,mat2,mat3), rescale = F, pdcheck = F,

Hi, I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below). #Model1 >spring.cox <- coxme (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2+(R3|Individual), spring) I've already found some explications in

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

------------- Begin Forwarded Message ------------- Date: Wed, 5 Jul 2006 09:09:14 -0500 (CDT) From: Terry Therneau <therneau at mayo.edu> Subject: RE: Problem with coxme To: jhz22 at medschl.cam.ac.uk Cc: R-help at stat.mat.ethz.ch, liulei at virginia.edu, spencer.graves at pdf.com Content-MD5: BXKVsHtW/1I9mIUqrXBU0g== The original question involved a strange error message from coxme

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats

Hello, I have a question concerning fitting a cox model with a random intercept, also known as a frailty model. I am using both the coxme package, and the frailty statement in coxph. Often 'shared' frailty models are implemented in practice, to group people who are from a cluster to account for homogeneity in outcomes for people from the same cluster. I am more interested in the classic