similar to: memory problem

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

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

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Dear, I want to analyze two-level survival data using a shared frailty model, for which I want to use 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: >

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 everyone: I need to count the number of banks of each firm in my data. The firm is identified by the fiscal number. The banks of each firm appears like this: Firm Banks 500600700 Citybank 500600700 CGD 500600700 BES 500600800 Citybank 500600800 Bank1 500600900 CGD I want to obtain the following dataframe: Firm

  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

> On Apr 16, 2018, at 3:20 PM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Apr 16, 2018, at 2:58 PM, Therneau, Terry M., Ph.D. via R-devel <r-devel at r-project.org> wrote: >> >> A user asked me about this and I can't figure it out. >> >> tmt% R >> R Under development (unstable) (2018-04-09 r74565) --

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-helpers, I'm plotting geophysical data in the form of contours using "filled.contour". The display would be much more effective if the areas with negative values could be color coded by -- say -- "cold colors" in the range of blue to green, and conversely the areas with positive values got plotted with "warm colors", from yellow to red. Right now if I use

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)

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]]

Dear R users, We are pleased to tell you that "FRAILTYPACK" has been updated. "FRAILTYPACK" stands now for general frailty models estimated with a semi-parametrical penalized likelihood, but also with a parametrical approach. In case of comments/corrections/remarks/suggestions -- which are very welcome --please contact the maintainer directly. Kind regards, The

Dear R users, We are pleased to tell you that "FRAILTYPACK" has been updated. "FRAILTYPACK" stands now for general frailty models estimated with a semi-parametrical penalized likelihood, but also with a parametrical approach. In case of comments/corrections/remarks/suggestions -- which are very welcome --please contact the maintainer directly. Kind regards, The

A user asked me about this and I can't figure it out. tmt% R R Under development (unstable) (2018-04-09 r74565) -- "Unsuffered Consequences" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) > library(survival) > data(cgd0) Warning message: In data(cgd0) : data set ?cgd0? not found ---- The data set is present and can be

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

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]]

I've got a fix for the panic from the cd(4)/da(4) drivers when INVARIANTS is turned on in -stable. The fix is to create a task queue that runs in a thread context and use that to create the sysctl variables needed by cd(4) and da(4). The eventual fix will be to move the CAM transport layer probe code into a kernel thread. Anyway, these patches work for me, but if I could get some feedback

I am trying to extract only the winters (defined to be 01-Dec through 28-Feb) of daily data from 1948-2002. There are 90 days in each winter season. I wrote the following code to gather the winter dates into a single vector: DJF <- NULL for(year in 1949:1999) { temp.begin <- strptime(paste("01/12", year-1, sep="/"), "%d/%m/%Y") temp.end <-