Random effects variances in R and SPSS not matching

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 at phillipalday.com
<mailto:me at 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 at r-project.org

    <mailto:R-sig-mixed-models at r-project.org> mailing list

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