[R-sig-ME] Error means squares in GLMER and LMER

[Ben Bolker]

For marginal means, use the emmeans package.

If you use the lmerTest package, you can get Satterthwaite or
Kenward-Roger df: you can use lme (from the nlme package), or
<https://github.com/bbolker/mixedmodels-misc/blob/master/R/calcDenDF.R>,
to get df via a simple "parameter-counting" exercise.

The problem is that the "F statistics" are quite poorly defined for
GLMMs. Can you show us the contrasting results you're getting for SPSS
and glmer?  Do you know how SPSS is computing the F statistics?  (This
<https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.modeler.help/idh_glmm_build_options.htm>
makes it seem like it might be using Satterthwaite approximations ...)
On Thu, Nov 22, 2018 at 12:09 PM Kornbrot, Diana
<d.e.kornbrot using herts.ac.uk> wrote:
>
> So I am comparing standard ANOVA on raw  frequencies (or equivalently probabilities) with GLMM for  binomial  proportion with both  logit and probit   link
> ALL analyses have  been completed in SPSS using MIXED for: response   = identity ,  link = normal ; response =  proportion=freq/Nmax, link =  probit; response = proportion link  = logit)
> I want   to show  how to  do identical  analyses in R using lmer  for are freq and  glmer for proportions
> So I wasn’t SAME results  from R and SPSS (and a  diamond  necklace for Christmas,  celebrated as  an EU citizen in UK - I am a demanding woman)
> Results are NOT quite  the same.
> I am  checking using  the raw  probabilities as raw response with lmer before  moving  on to  glmer  for proportions
> Check 1, in SPSS for raw  freq or  probability REPEATED  give  same  result   as  MIXED (response = identity, link = normal). where  there are differences it is REPEATED WITHIN  comparisons  not MULTIVARIATE.
> Check 2. Compare R,  lmer with SPSS  mixed
> if repeated groups are w1,  w2,  etc  and between groups are b1,  b2  etc, I use:
> result <- lmer(freq~b1*b2*w1*w2 + (w1|subject)  + (w2|subject),  data  =  test)
> anova(result)
> Fand df from R  and SPSS do  not always match,  even  when  they do  match on sums  of squares
> I am trying  to  work out WHY there is a mismatch
> Thought that  knowing what was in the DENOMINATOR of the F values - which i perhaps  wrongly termed  error sums  of squares, might help
>
> I  want F  for usual reasons:   to test significance  and estimate  effect  size.
> I also want  all  my packages  to  give  me the SAME F and df2 and to  UNDERSTAND what is happening
>
> Sorry this is  so long,  but  hope it is now clearer
>
> best
>
> Diana
>
> and as  an extra  treat  would like marginal means from object  of type lmer
>
> Dear Diana,
> If indeed what you're looking for is what Rolf mentioned, you might find Nakagawa & Schielzeth (2013) helpful.
> It's titled "A general and simple method for obtaining R2 from generalized linear mixed-effects models". There is no dispute about the method's generality, but simple is a relative term...
> Here's the link: https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x
>
> Hope this helps,
> --
> Roi Maor
> PhD candidate
> School of Zoology, Tel Aviv University
> Centre for Biodiversity and Environment Research, UCL
>
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