search for: frailtypen

Dear R-listers, I have a very strange problem. I made a package (under Windows and Linux). The package passed the R CMD Check without problem. Then, I installed the package and executed a function which calls to a 'dll' mod<-frailtyPenal(Surv(time,status)~sex+age+cluster(id), + n.knots=8,kappa1=10000,data=kidney) mod Call: frailtyPenal(formula = Surv(time, status) ~ sex + age + cluster(id), data = kidney, n.knots = 8, kappa1 = 10000) Shared Gamma Frailty model parameter estimates using a Penalized L...

...slope): fitAdditive<-additivePenal(Surv(t1,t2,event)~cluster(group)+var1+slope(var1), correlation=TRUE,data=dataAdditive,n.knots=8,kappa1=10000) -- NESTED FRAILTY MODELS for hierarchically clustered data (with 2 levels of clustering) by including two random effects: fitnested<-frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup), data=dataNested,n.knots=8,kappa1=50000) -- JOINT FRAILTY MODELS in the context of joint modelling of recurrent events with terminal event: fitJoint<-frailtyPenal(Surv(time.entry,time.end,status)~cluster(id)+ terminal(status.t...

...slope): fitAdditive<-additivePenal(Surv(t1,t2,event)~cluster(group)+var1+slope(var1), correlation=TRUE,data=dataAdditive,n.knots=8,kappa1=10000) -- NESTED FRAILTY MODELS for hierarchically clustered data (with 2 levels of clustering) by including two random effects: fitnested<-frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup), data=dataNested,n.knots=8,kappa1=50000) -- JOINT FRAILTY MODELS in the context of joint modelling of recurrent events with terminal event: fitJoint<-frailtyPenal(Surv(time.entry,time.end,status)~cluster(id)+ terminal(status.t...

...on at 0x######## reference memory at 0x#######, the memory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart, TStop, Status)~cluster(Center)+subcluster(ID) Treatment,data=cgd.ag,Frailty=TRUE,n.knots=8,kappa1=50000, cross.validation=TRUE,recurrentAG=TRUE) -- View this message in context: http://r.789695.n4.nabble.com/memory-problem-tp2330510p...

  To   R group Help Desk     I am a user of R software.  I am facing a problem while using "frailtyPenal" command in R.2.11.1. When I use these command, R closes completely without any prior alert message. Can I know what would be the reason? My data size is 7050 records with atleast 25 variables.   Looking forward to hear from you.   Truely Kalaivani M.Kalaivani, M.Sc. Scientist-I Dept. of...

Hi everyone I am attempting to estimate a model with a frailty effect distributed as a lognormal variable.I am using the following code: frailtyPenal(formula, data, ..., RandDist = "LogN") I get the following error message: Error in frailtyPenal(Surv(,) ~ + , : unused argument(s) (RandDist = "") What can I do? Thanks for the help [[alternative HTML version deleted]]

...se the R package 'Frailtypack", proposed by Rondeau et al. The dataset was built using SAS software. I also tried to change the format using SPSS and Excell. My (reduced) dataset has following column names: ID entry time status family var1 I used following command: > frailtyPenal(Surv(time, status) ~var1 + cluster(family), Frailty=TRUE > ,n.knots=8, kappa1=1500, + cross.validation=FALSE) And got this error : Error in Surv(time, status) : Time variable is not numeric In addition: Warning message: In is.na(time) : is.na() applied to non-(list or vector) of type 'clo...

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 approach too? coxph(...frailty(id, dist='gamma')) estimates by means of the penaliz...

Hi, What is the function to calculate penalized cox regression? frailtyPenal in frailtypack R package imposes max 2 strata. I want to use a function that reduces all my variables without stratifying them in advance. Look forward to your reply carol --------------------------------- [[alternative HTML version deleted]]

Hello, I have a cohort with approx 1,200 patients at the ages of 30-65 that had their first myocardial infarction during 1992: · They were in a follow up until 2005. · About 400 of them died during this period of time (right censored) · Each one of them had up to 4 mi recurrent events. I am using the semi-parametric model in order to assess the relationship of