similar to: multcomp on significant interaction in coxme model

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 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)

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), #

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 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 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'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

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,

------------- 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

Hello, Why the correlation between the random effects is negative? library(coxme) rats1 <- coxme(Surv(time, status) ~ (1|litter), rats) random.effects(rats1)[[1]] #one value for each of the 50 litters print(rats1) rats2 <- lmekin(time ~ (1|litter), rats) fixed.effects(rats2) random.effects(rats2)[[1]] #one value for each of the 50 litters print(rats2)

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

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

Hello, I was hoping someone could answer a question for me that may either be statistical or script related. I don't come from a statistics background, so I am not positive if I am using the correct nomenclature or even the correct procedure. Is it possible to model "random coefficients" in a mixed effects cox-regression using coxme from the Kinship package? For example, using

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

Version 2.2 of coxme has been posted to CRAN, Windows versions and mirrors should appear in due course. This is a major update with three features of note: 1. A non-upwardly compatable change: Extractor functions: beta= fixed effects, b=random effects nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2 ------------------------------------------------------ beta

Hi all, I am using the coxme function to fit random effects survival models. The base survival models show a typical "J" shaped curve where survival drops sharply then plateaus over time interval. I want to include a time covariate which could account for this non-linear decrease in survival over time. However, I have not found a clear way to do this in the coxme models. Any

Hello, and thanks for your time I'm trying to extract standard errors to produce confidence intervals from a multivariable coxme model object so I can write a function that will print a summary for some reproducible research. As far as I can glean, the SE is produced on-the-fly by the print method. I'll dig into the source code if I have to, but I'd rather not have to. Any help

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

Hi, R experts I am currently using lmekin() function in coxme package to fit a mixed effect model for family based genetic data. How can I extract the p value from a lmekin object? When I print the object in R console, I can see the p value and Z value are just over there. But I can not extract them by the coef() function. kinfit$coefficient$fixed (kinfit is the name of the lmekin object)