Re: [R] Multivariate Multilevel Model: is R the right software for this problem
Wrong list. Post to R-sig-mixed-models -- Bert On Thu, Apr 12, 2012 at 12:34 PM, Eiko Fried 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.
Re: [R] Multivariate Multilevel Model: is R the right software for this problem
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.
Re: [R] Multivariate Multilevel Model: is R the right software for this problem
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. Andrew Miles [[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.
Re: [R] Multivariate Multilevel Model: is R the right software for this problem
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. Andrew Miles Department of Sociology Duke University On Apr 6, 2012, at 9:48 AM, Eiko Fried wrote: > 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. [[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.
[R] Multivariate Multilevel Model: is R the right software for this problem
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.
