similar to: Hottelings T2-test for multivariate lingitudinal data

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

Hi I've simulated multivariate longitudinal. The data is a mixture of conitnous and categorical data. I've stored it in matrix form with the time dependent variables as colons. Now I want to create a monote missing data pattern starting of with MCAR-missingnes and different proportions of missingdata and then refine the function to handle MAR and NMAR. Is there anybody that could help or

Greetings ? A new package ?simstudy? is now available on CRAN. What started as a small number of functions that enabled me to quickly generate simple data sets for teaching and power/sample size calculations has grown into a more robust set of tools that allows users to simulate more complex data sets in order to explore modeling techniques or better understand data generating processes. The user

Greetings ? A new package ?simstudy? is now available on CRAN. What started as a small number of functions that enabled me to quickly generate simple data sets for teaching and power/sample size calculations has grown into a more robust set of tools that allows users to simulate more complex data sets in order to explore modeling techniques or better understand data generating processes. The user

Hi, I've tried to run the multiple imputation for longitudinal data using Pan in R. The trimmed data consist 10 individuals across 4 time points. Following the example in panex.R, I imputated the model with one y variable and only random intercept. It worked well. The next step for me is to imputate a model with one y variable and both random intercept and slope. The program ran well, but

Hi. Can anyone point me to any module in R which implements "Little's Chi Square test" for MCAR. The problem is that i have around 60 behavioural variables on a 6 point categorical scale which i need to test for MCAR and MAR. What i can make out from preliminary analysis is that moderate (0.30 to 0.60) correlations may be present in several variable pairs leading me to suspect

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]]

Hi, Can anyone recommend one or more good sample datasets for multivariate longitudinal data? (eg. a dataset containing a mixture of continuous, categorical and date/time variables) I'm looking for something which I can use for testing and examples for my ggplot graphics package. A selection of datasets which use different date time classes would also be useful, although I could probably

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:

L.S., Does anyone know if there is an R library which implements Little's MCAR test for completely at random missing values? It is implemented in SPSS and SAS, and widely mentioned in the literature. Thanks in advance! Sander van Kuijk -- View this message in context: http://r.789695.n4.nabble.com/Little-s-MCAR-test-tp2331137p2331137.html Sent from the R help mailing list archive at

I'm relatively new to R, and not particularly adept yet, but I was wondering if there was a simply way to simulate missing data that are MAR, MNAR and MCAR. I've got a good work-around for the MCAR data, but it's sort of hard to work with. Josh [[alternative HTML version deleted]]

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

Dear all I want to create dataset with MCAR type from my dataset. I have my dataset with 100 records, and I want to create dataset from this dataset to missing 5 records. How I can do it. THX Jumlong -- Jumlong Vongprasert Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

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

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 all, I have data that contain more than 30 variables and 600 observations, it?s a longitudinal data,data contains a lot of non normal data (despite trying to do some transformation i hav still nonnormal variables ) i have a lot of missing data, i want to impute these missing data , i wonder if There is some specifications and some manner to impute missing data for a data where

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.

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.

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