Wrong list. Post to R-sig-mixed-models -- Bert
On Thu, Apr 12, 2012 at 12:34 PM, Eiko Fried <tor...@gmail.com> wrote: > Very interesting book! > However, it doesn't cover multivariate models (I have 9 moderately > correlated, categorical dependent variables). > > Again, I'm trying to find out whether 5 time-varying variables > (dichotomous; five different life events "yes"/"no"; subjects can have > several life events at the same time) cause differential profiles of my 9 > depression variables in a longitudinal sample, controlling for > time-invariant covariates - exploratory. > > Is this possible in R? If so, how? I thought about multilevel multivariate > mixed-effects models (random effect = subjects), but hardly find literature > for R. > > Thanks a bunch! > Eiko > > > > I recommend looking at chapter 6 of Paul Allison's *Fixed Effects >> Regression Models*. This chapter outlines how you can use a structural >> equation modeling framework to estimate a multi-level model (a random >> effects model). This approach is slower than just using MLM software like >> lmer() in the lme4 package, but has the advantage of being able to specify >> correlations between errors across time, the ability to control for >> time-invariant effects of time-invariant variables, and allows you to use >> the missing data maximum likelihood that comes in structural equation >> modeling packages. >> >> Hello, >> >> I've been trying to answer a problem I have had for some months now and >> came across multivariate multilevel modeling. I know MPLUS and SPSS quite >> well but these programs could not solve this specific difficulty. >> >> My problem: >> 9 correlated dependent variables (medical symptoms; categorical, 0-3), 5 >> measurement points, 10 time-varying covariates (life events; dichotomous, >> 0-1), N ~ 900. Up to 35% missing values on some variables, especially at >> later measurement points. >> >> My exploratory question is whether there is an interaction effect between >> life events and symptoms - and if so, what the effect is exactly. E.g. life >> event 1 could lead to more symptoms A B D whereas life event 2 could lead >> to more symptoms A C D and less symptoms E. >> >> My question is: would MMM in R be a viable option for this? If so, could >> you recommend literature? >> >> Thank you >> --T >> >> >> > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
