Displaying 20 results from an estimated 3000 matches similar to: "New Package 'JM' for the Joint Modelling of Longitudinal and Survival Data"
2012 Sep 18
0
New Package 'JMbayes' for the Joint Modeling of Longitudinal and Survival Data under a Bayesian approach
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(),
2012 Sep 18
0
New Package 'JMbayes' for the Joint Modeling of Longitudinal and Survival Data under a Bayesian approach
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(),
2010 Mar 18
0
package JM -- version 0.6-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint 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
2010 Mar 18
0
package JM -- version 0.6-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint 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
2010 Dec 15
0
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 a time-dependent
covariate measured with error.
2010 Dec 15
0
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 a time-dependent
covariate measured with error.
2009 Jun 19
0
package JM -- version 0.3-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint 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
2009 Jun 19
0
package JM -- version 0.3-0
Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint 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
2011 Sep 28
0
package JM -- version 0.9-0
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
2011 Sep 28
0
package JM -- version 0.9-0
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
2012 Oct 05
0
jointModel error messages
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!
2010 Mar 15
3
the problem about sample size
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
2012 Jul 10
0
package JM -- version 1.0-0
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
2012 Jul 10
0
package JM -- version 1.0-0
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
2008 Aug 07
4
Obtaining the first /or last record of a subject in a longitudinal study
Dear R users,
I was wondering if anyone knows how to obtain(subset) the first and/or the
last record of a subject in a longitudinal setup.
Normally in SAS one uses first.variable1 and last.variable1. So my question
is that is there an R way of doing this.
Regards,
--
Luwis Diya, Phd student (Biostatistics),
Biostatistical Center,
School Of Public Health,
Catholic University of Leuven,
U.Z. St
2008 Jul 06
1
What is my replication unit? Lmer for binary longitudinal data with blocks and two treaments.
First I would like to say thank you for taking the time to read it.Here is my
problem.
I am running a lmer analysis for binary longitudinal (repeated measures)
data.
Basically, I manipulated fruits and vegetation to two levels each(present
and absent) and I am trying to access how these factors affect mice foraging
behavior. The design consist of 12 plots, divided in 3 blocks. So each
block
2008 Apr 07
0
Translating NLMIXED in nlme
Dear All,
reading an article by Rodolphe Thiebaut and Helene Jacqmin-Gadda ("Mixed
models for longitudinal
left-censored repeated measures") I have found this program in SAS
proc nlmixed data=TEST QTOL=1E-6;
parms sigsq1=0.44 ro=0.09 sigsq2=0.07 sigsqe=0.18 alpha=3.08 beta=0.43;
bounds $B!](B1< ro < 1, sigsq1 sigsq2 sigsqe >= 0;
pi=2*arsin(1);
mu=alpha+beta*TIME+a i+b i*TIME;
2009 May 06
0
Quantile Regression for Longitudinal Data. Warning message: In rq.fit.sfn
Dear Dimitris, I have exactly the same problem
than you, Do you get some solution?
Thanks, Lola
Lola Gadea
Profesora titular de Economía Aplicada/Lecturer in Applied Economics
Universidad de Zaragoza/University of Zaragoza (Spain)
lgadea@unizar.es
<http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html
Grupo de
2008 Sep 30
1
Quantile Regression for Longitudinal Data. Warning message: In rq.fit.sfn
Hi,
I am trying to estimate a quantile regression using panel data. I am trying
to use the model that is described in Dr. Koenker's article. So I use the
code the that is posted in the following link:
http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R
While this code run perfectly, it does not work for my data providing a
warning message:
In rq.fit.sfn(D, y, rhs = a) : tiny
2004 Oct 25
0
答复: Multiple formula in one block
Hi Dimitris:
Thanks for your help, I will try.
BR
Yiyao
-----ÔʼÓʼþ-----
·¢¼þÈË: Dimitris Rizopoulos [mailto:dimitris.rizopoulos at med.kuleuven.ac.
be]
·¢ËÍʱ¼ä: 2004Äê10ÔÂ25ÈÕ 15:39
ÊÕ¼þÈË: YiYao_Jiang
³ËÍ: r-help at stat.math.ethz.ch
Ö÷Ìâ: Re: [R] Multiple formula in one block
Hi YiYao,
you need the `?panel.abline()' function, somehing like:
panel=function(x, breaks,