similar to: package JM -- version 0.8-0

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent

Dear R-users, I'd like to announce the release of version 1.0-0 of package JM (already available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent

Dear R-users, I'd like to announce the release of version 1.0-0 of package JM (already available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

I contacted the package developer and that lead to me removing events at time 0 (or subjects with only 1 longitudinal measurement). I then still had the error message "Can't fit a Cox model with 0 failures" which I have managed to avoid by adding 1.8*10^(-15) to all my survival times, any number greater than this also works but nothing smaller! Any explanation of this would help!

Hi all: I am a user of "JM" package. Here's the problem of "sample size". The warning is: Error in jointModel(fitLME, fitSURV_death, timeVar = "time", method = "piecewise-PH-GH") : sample sizes in the longitudinal and event processes differ. According to the suggestion of "missing data",I use the same data set(data_JM) without any

Dear R-users, I would like to announce the release of the new package JMbayes available from CRAN (http://CRAN.R-project.org/package=JMbayes). This package fits shared parameter models for the joint modeling of normal longitudinal responses and event times under a Bayesian approach using JAGS, WinBUGS or OpenBUGS. The package has a single model-fitting function called jointModelBayes(),

Dear R-users, I would like to announce the release of the new package JMbayes available from CRAN (http://CRAN.R-project.org/package=JMbayes). This package fits shared parameter models for the joint modeling of normal longitudinal responses and event times under a Bayesian approach using JAGS, WinBUGS or OpenBUGS. The package has a single model-fitting function called jointModelBayes(),

Hello, I need to identify all elements which have a sequence of 3 consecutive upper case letters, anywhere in the string. I tested my grep expression on this site: http://regexlib.com/RETester.aspx But when I try it in R, it does not filter anything. str <-c("AGH", "this WOUld be good", "Not Good at All") str[grep('[A-Z]{3}',str)] #looking for a

Dear Graham. Any updates regarding your message about JointModel error messages? I am encountering similar errors. Thank you. Havi [[alternative HTML version deleted]]

Hello, I've noticed a strange delay while booting a CentOS 7 guest on a CentOS 7 host with slow disks (7200RPM) with write cache off. The guest and host are freshly installed Centos 7 (host was fully patched before guest install). Guest is installed on an lvm pool residing on an md raid1 with two SATA 7200 RPM drives with their write caches off. The delay is on mounting /boot, the dmesg

Hello, I am using the package gamlss in R to plot repeated measurements. The command I am using is par.plot(). It works great except one thing about the label of the axises. I tried to label both x and y axises using ylab and xlab options. But the plot only gives variable variables. The labels did not show up. Below is the code I used. Any comments are appreciated! Thanks. library(gamlss)

Hi all, I a semi-beginner with R and I am working with the plm package to examine a longitudinal dataset. Each individual in this dataset has a longitudinal weight for the probability that he or she remains in the sample. Unfortunately, I have not found an argument to use weights in the plm function? I tried ?weights=? like in standard lm or in nlme or lm4 but it does not work. I asked the

Hi again! Following up my previous posting below (to which no response as yet), I have located a report which situates this type of question in a longitudinal modelling context. http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps Generalized Linear Models with Longitudinal Covariates Daowen Zhang & Xihong Lin (This work seems to originally date from around 1999). They consider an outcome Y,