similar to: Longitudinal multivariate data analysis

Dear R help list, I am desperately looking for any reference explaining by examples how to write R codes in order to fit the parameters of a given model using maximum likelihood or any other criteria function. I know the general structure: First write a code for the maximum likelihood function and afterwards write a code to maximize it using optim and then invert the Hessian to get the

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

Hi all, I was trying to fit a Gamma hierarchical model using "glmer", but got weird error message that I could not understand. On the other hand, a similar call to the glmmPQL leads to results that are close to what I expect. I also tried to change tha "nAGQ" argument in "glmer", but it did not solve the problem. The model I was fitting has a simple structure - one

Dear All, I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The

Dear All, I have the following error message when I fitted lmer to a binary data with the "AGQ" option: Error in family$mu.eta(eta) : NAs are not allowed in subscripted assignments In addition: Warning message: IRLS iterations for PQL did not converge Any help? Thanks in advance, Abderrahim [[alternative HTML version deleted]]

Hi, I am wondering if we can make prediction on a linear mixed model by lmer() from lme4 package? Specifically I am fitting a very simple glmer() with binomial family distribution, and want to see if I can get the predicted probability like that in regular logistic regression?   fit<-glmer(y~x+(1|id),dat,family=binomial)   where y is the response variable (0, 1), and x is a continuous variable.

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:

Hello, I am fitting a gmler using poisson, and I was looking for a documentation to interpret correctly the output. I'm quite a beginner with these kind of models. I couldn't find something in the lme4 package manual. and on the internet neither... Thank you, Raphaelle -- View this message in context: http://www.nabble.com/glmer-documentation-tp21506036p21506036.html Sent from the R

I'm trying to figure out how to carry out a Poisson regression fit to longitudinal data with a gamma distribution with unknown shape and scale parameters. I've tried the 'lmer4' package's glmer() function, which fits the Poisson regression using: library('lme4') fit5<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) + (1|id), data=pdata,

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

Dear all, I have the following dataset with results from an experiment with individual bats that performed two tasks related to prey capture under different conditions: X variables: indiv - 5 individual bats used in the experiment; all of which performed both tasks task - 2 tasks that each individual bat had to perform dist - 5 repeated measures of individual bats at 5 different distances from

Hello everyone, I'm quite new to R and am trying to run a logistic model to look at how various measures of boldness in individual animals influences probability of capture, however I also want to include random effects and I'm not sure how to construct a model that incorporates both of these things. Data was collected from 6 different groups of 6 individuals with 10 replicates for

I am trying to delete directories recursively in 'smbclient' like I typically do with 'rm -r' on a Unix shell, but I am just not able to. Is that in fact possible in 'smbclient'? Youssef Eldakar Bibliotheca Alexandrina

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error:

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 there, I have a dataset from a longitudinal study with a lot of drop-out. I want to implement the inverse probability weighting method by Robins 1995 JASA paper "Analysis of semiparametric regression models for repeated outcomes in the presence of missing data". Does anyone know if there is a package to do it in R (or other software)? Thanks a lot! Lei

Thanks Martin to confirm that the issue is due to privilege access. How can I run a domain as non-root and be able to access the cpu information? -----Original Message----- From: Martin Kletzander [mailto:mkletzan@redhat.com] Sent: Tuesday, February 28, 2017 16:18 To: EL FATHI Youssef OBS/OINIS Cc: libvirt-users@redhat.com Subject: Re: [libvirt-users] Redhat 7: cgroup CPUACCT controller is not

* Please cc me if you reply as I am a digest subscriber * Hi, I am wondering how I can run a multilevel survival model in R? Below is some of my data. > head(bi0.test) childid famid lifedxm sex age delta 1 22.02 22 CONTROL MALES 21.36893 0 2 13.02 13 MAJOR MALES 21.18001 0 3 64.02 64 CONTROL MALES 20.09377 0 4 5.02 5 CONTROL FEMALES

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have