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

[Phillip Alday]

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]]
>     >
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