[R-sig-ME] Random effects variances in R and SPSS not matching

[Simon Harmel]

Thank you so very much for your assistance.

On Wed, Mar 31, 2021 at 4:18 PM Phillip Alday <me using phillipalday.com> wrote:

> I don't think the optimum is well defined:
>
> > library("lattice")
> > library("lme4")
> > m2.4 <- lmer(value ~0 + name + (0+name| Student), data = dat,
> REML=FALSE,
> control=lmerControl(optimizer="bobyqa",check.nobs.vs.nRE="warning"))
> Warning messages:
> 1: number of observations (=1600) <= number of random effects (=1600)
> for term (0 + name | Student); the random-effects parameters and the
> residual variance (or scale parameter) are probably unidentifiable
> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>   Model is nearly unidentifiable: large eigenvalue ratio
>  - Rescale variables?
> > p <- profile(m2.4)
> There were 50 or more warnings (use warnings() to see the first 50)
> > xyplot(p)
>
> Those are some very bad profiles! Also, this goes back to the lme4
> safety-check that I had to disable. The fundamental problem is that
> there isn't enough data to completely distinguish the residual variance
> from the RE, so you get difference answers for the RE variance depending
> on how much you attribute to the residual variance.
>
> I also tried to do this with MCMC and flat priors (always a bad idea,
> but...) and also ran into bad convergence issues.
>
> Phillip
>
>
>
> On 31/3/21 10:43 pm, Simon Harmel wrote:
> > Thank you. I'll be happy to give more info. SPSS model syntax is shown
> > on in Table 14.5, p. 585 (type `606` in page slot) of this book
> > (http://docshare02.docshare.tips/files/31719/317194846.pdf).
> >
> > The SPSS output is shown on p. 588 (type `606` in page slot).
> >
> > I should add the covariance between `Y1` and `Y2` exactly match. and the
> > log-likelihood seems to be almost identical. But variances differ by a
> > lot. SPSS is using "ML".
> >
> > Please let me know if I can provide any further information.
> >
> > Thank you for your prompt reply,
> > Simon
> >
> >
> >
> >
> > On Wed, Mar 31, 2021 at 3:36 PM Phillip Alday <me using phillipalday.com
> > <mailto:me using phillipalday.com>> wrote:
> >
> >     Without more information, we don't know for sure that the models are
> the
> >     same in both languages.
> >
> >     It's too much of a time sink for a human to change model details
> >     randomly until the output matches some expected output, but you could
> >     probably do something with genetic programming or simulated
> annealing to
> >     do that....
> >
> >     But if you can get more information, I would start by making sure
> >     - that the contrasts are truly the same
> >     - assumed covariance structures are the same
> >     - that one language isn't dropping some observations that the other
> is
> >     keeping (check the reporting number of observations levels of the
> >     grouping var)
> >     - the estimation method is the same across languages (ML,REML;
> hopefully
> >     SPSS isn't using something like quasi-likelihood)
> >     - different optimizers (if available) give the same  result across
> >     languages (i.e. make sure you're not in a local optimum)
> >     - cross checking the result against yet another software package
> >
> >     For example, cross-checking against lme4 immediately hints that this
> >     model might not be advisable / have a well-defined optimum:
> >
> >     > m2.4 <- lmer(value ~0 + name + (0 + name| Student), data = dat,
> >     REML=FALSE)
> >     Error: number of observations (=1600) <= number of random effects
> >     (=1600) for term (0 + name | Student); the random-effects parameters
> and
> >     the residual variance (or scale parameter) are probably
> unidentifiable
> >
> >     Phillip
> >
> >     On 31/3/21 10:15 pm, Simon Harmel wrote:
> >     > Dear All,
> >     >
> >     > For my reproducible model below, SPSS gives the variance component
> of
> >     > 119.95 for Y1, and 127.90 for Y2.
> >     >
> >     > But in `nlme::lme()` my variance components are 105.78 for Y1 and
> >     113.73
> >     > for Y2.
> >     >
> >     > Can we make the `lme()` reproduce the SPSS's variance components?
> >     >
> >     > #======= Data and R code:
> >     > dat <-
> >     read.csv('https://raw.githubusercontent.com/hkil/m/master/mv.l.csv')
> >     >
> >     > library(nlme)
> >     >
> >     > m2 <- lme(value ~0 + name, random = ~0 + name| Student, data =
> >     dat, method
> >     > = "ML")
> >     >
> >     > Random effects variance covariance matrix
> >     >              nameY1   nameY2
> >     > nameY1 105.780  60.869
> >     > nameY2  60.869 113.730
> >     >
> >     >       [[alternative HTML version deleted]]
> >     >
> >     > _______________________________________________
> >     > R-sig-mixed-models using r-project.org
> >     <mailto:R-sig-mixed-models using r-project.org> mailing list
> >     > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >     >
> >
>

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