similar to: Multivariate longitudinal sample data

Dear all, I am looking for an R package that fits multivariate gaussian or non-gaussian longitudinal outcomes. I am especially interested to non-gaussian outcomes since the outcomes I've got are discrete (some are binomial and some are count data). Many thanks in advance, Abderrahim [[alternative HTML version deleted]]

We're pleased to announce a one day course covering static and dynamic graphics using R, ggplot and GGobi. The course will be held just before the JSM, on Saturday, 28 July 2007, in Salt Lake City. The course will be presented by Dianne Cook and Hadley Wickham. In the course you will learn: * How to build presentation quality static graphics using the R package, ggplot. We will cover plot

We're pleased to announce a one day course covering static and dynamic graphics using R, ggplot and GGobi. The course will be held just before the JSM, on Saturday, 28 July 2007, in Salt Lake City. The course will be presented by Dianne Cook and Hadley Wickham. In the course you will learn: * How to build presentation quality static graphics using the R package, ggplot. We will cover plot

Hi all, Eons ago (~2007) Hadley Wickham extremely generously helped me developed a means for plotting temporal activity w/ ggplot. I need to revisit this and have it working again.? I failed to copy the entire legacy code over to my traveling laptop from my workstation, but found a partial bit of the code in an old 2009 Gmail attachment.? I am slated to do a graduate seminar at UF G'ville

Hi all: I have a question about the sample size calculation. It's a pilot study,which includes 2 groups(low,high),3 time point(3,6,9 monthes).Each person has 3 results which according to the 3 time points.So it's a longitudinal study. I want to calculate the minimum sample size from the pilot study, but can't find the solution since the data is highly skewed and it's a

Dear R-users I've simulated a longitudinal multivariate normal data set from which I've simulated missing-patterns such as MCAR MAR and a simple kind of non-MAR. I've imputated the values so I now have 'complete' data sets. I'm trying to perform a T2-test as done in the multivariate case under th enormal assumption. Is there something I've to think about when performing

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

HI, I am relatively new to R and would appreciate some help or directions for this. I am trying to model 3 longitudinal outcomes jointly and to identify some predictors for these 3 joint outcomes (all continuous). I am trying to find some codes that I may modify to do this but cannot seem to find anything. -- View this message in context:

Dear List,   I have some longitudinal data, each patient was followed at times 0, 12, 16, 24 weeks and measure severity of a illness (0-worse, 1-same, 2-better). So, longitudinal response is categorical.  I was wondering whether lmer in R can fit a model for this type of data. If so, how we code? Or any other function in R that can fit this type of longitudinal data? Any suggestion would be

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

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(),

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,

Dear R-Users! Is there a possibility in R to do analyze longitudinal survey data (repeated measures in a survey)? I know that for longitudinal data I can use lme() to incorporate the correlation structure within individual and I know that there is the package survey for analyzing survey data. How can I combine both? I am trying to calculate design-based estimates. However, if I use svyglm() from

i am studying Longitudinal Data Analysis and want to carry it with R.anyone knows any materials about Longitudinal Data Analysis with R in the internet which i can download? thank you.

Hi, Can one tell me which procedure will fit an ordinal logistic regression model for longitudinal data set. To be precise, I have both dichotomous and polytomous items. Also, I would like to specify different covariance structures (unstructured, ar1 etc) for trial runs. Thanks -- View this message in context:

Hello all, I am working on a longitudinal study of children in the UK and trying the PAN package for imputation of missing data, since it fulfils the critical criteria of taking into account individual subject trend over time as well as population trend over time. In order to validate the procedure I have started by deleting some known values ?we have 6 annual measures of height on 300 children

Longitudinal and Multilevel Data in R and Stan ICPSR short course: May 28 to June 1, 2018 May 28: Introduction to R by John Fox May 29 to June 1: Longitudinal and Multilevel Data in R and Stan by Georges Monette Sponsored and organized by ICPSR, University of Michigan and held at York University in Toronto, Ontario Course description:https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0226

hi, I'm a total noob who is having to ramp up to full speed very quickly due to an unfortunate abrupt staffing change at my job :) I have longitudinal data that looks like this: PID OBSDATE DaysAgo CleanValue NAME 1 1410164934000610 8/17/2004 13:03:38 1345 6.2 HGBA1C 2 1410164934000610 11/16/2004 10:39:51 1254 7.1

Dear R-help subscribers, I use xyplot to plot longitudinal data as follows: score<-runif(100,-4,5) group<-sample(1:4,100,rep=T) subject<-rep(1:25,4) age<-rep(runif(4,1,40),25) df<-data.frame(score,group,age,subject) xyplot(score~age|group, group=subject, panel=function(...){ panel.loess(...,lwd=4) panel.superpose(...)} ,data=df) this produced a plot with four panels one for each