[R-sig-ME] Random effects variances in R and SPSS not matching
bbo|ker @end|ng |rom gm@||@com
Wed Mar 31 22:56:53 CEST 2021
On 3/31/21 4:36 PM, Phillip Alday 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,
> 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
If you only have one observation per name per student, then this
model will be overfitted. You could use glmmTMB with dispformula = ~0,
or blme::blmer with a tight/small prior on the residual variance
Based on this
it looks like you can fix residual variance to a small value (not
exactly zero!) in lme, e.g.
control = lmerControl(sigma=1e-6)
After experimenting with your example below, I can't set sigma to a
value < 1 without a false convergence error. Setting it to 1 gives
values much closer to your report (118.9, 126.9). Using opt="optim" in
the lmerControl and sigma=1e-5 gives the results you're looking for; so
does using returnObject=TRUE (which tells lme to give you the answer
even though there was a convergence warning).
There's also probably a way to rearrange this with a simpler random
effect, but I haven't quite figured it out yet.
> 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')
>> 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 mailing list
> R-sig-mixed-models using r-project.org mailing list
More information about the R-sig-mixed-models