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

[Simon Harmel]

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> 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]]
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>

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