[R-sig-ME] Random effects in R vs SAS

[Mollie Brooks]

> On 18 Aug 2023, at 07.06, Will Hopkins <willthekiwi using gmail.com> wrote:
> 
> I am a SAS user, and I have just finished a near-final draft of an article
> about identifying and specifying fixed and random effects for mixed models
> in SAS. At the moment, all I say about R is this: "To all users of the R
> stats package, my apologies: a few years ago, I found the mixed model in R
> too limiting, and I struggled with the coding. I hope someone with R smarts
> will consider adapting my programs and publishing them here." I really would
> like to know if the limitations (or at least, what I consider to be
> limitations) have been addressed, so I can be more specific in my article.
> Specifically can anyone answer these questions?
> 
> 
> 
> 1. Can you specify negative variance for random effects in R? (That doesn't
> apply to the variances representing residuals, which are never negative).
> 
> 
> 
> 2. Can you get trustworthy estimates of standard errors for the
> random-effect and residual variances in R? (By trustworthy, I guess I mean
> the same as SAS's, but it could mean that someone has shown that the
> confidence intervals derived with the SEs have good coverage, assuming
> normality for the variances representing random effects and chi-squared for
> variances representing residuals.) 
> 
> 
> 
> 3. Does R have the equivalent of the repeated statement in SAS, whereby you
> can specify a repeated measure with an unstructured or other covariance
> matrix? (e.g., repeated Time/subject=SubjectID type=un. This statement works
> more reliably than the random statement in SAS with small sample sizes, but
> it doesn't produce residual variances, not directly anyway.)
> 

I am not a SAS user, but I guess that their unstructured covariance matrix might be similar to the one described here
https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html

> 
> 
> 4. Does R have the equivalent of the group= option in SAS, whereby you can
> specify separate estimates of random-effect variances and covariances,
> and/or separate estimates of residuals, for different groups? (e.g., random
> SubjectID/group=Sex; repeated/group=Sex;)

This section of this website explains how a variety of random effects can be specified in many R packages
https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#model-specification


Mollie

> 
> 
> 
> The draft article is available at
> https://sportsci.org/2023/EffectsModels.htm. It's not published yet (i.e.,
> not linked to the Sportscience homepage yet). I would be grateful for any
> feedback. I can incorporate more about R and would love someone to provide R
> code for the programs I have published. See below for the title and
> abstract.
> 
> 
> 
> Will
> 
> 
> 
> How to Identify and Specify Fixed and Random Effects for Linear Mixed Models
> in the Statistical Analysis System
> 
> 
> 
> Most analyses require linear mixed modeling to properly account not only for
> mean effects but also for sources of variability and error in the data. In
> linear mixed models, means and their differences are specified with fixed
> effects, while variabilities and errors are specified with random effects
> (including residuals) and are summarized as standard deviations. In this
> tutorial article, I explain how variables represent effects in linear mixed
> models and how identifying clusters of observations in a dataset is the key
> to identifying the fixed and random effects. I also provide programs written
> in the language of the Statistical analysis system to simulate data and
> analyze them with the general linear mixed model, Proc Mixed, which is used
> when the dependent variable is continuous. The analyses include simple
> linear regression, reliability and time series, controlled trials, and
> combined within- and between-subject modeling. Finally, I explain how
> dependent variables representing counts or proportions require generalized
> linear mixed models, realized in SAS with Proc Glimmix, where the main
> difference is the specification of the residuals.
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


	[[alternative HTML version deleted]]