[R-sig-ME] convergence issues on lme4 and incoherent error messages
Cristiano Alessandro
cr|@@|e@@@ndro @end|ng |rom gm@||@com
Fri Jun 14 17:23:47 CEST 2019
Hi all,
thanks a lot for all your help!
@Rene'. I am not sure I can follow all you said.
"If, as you say, there are measurements missing in some design-cells for
some subjects, then, actually, estimating the variance of fixed effects
between subjects (just another word for by-subject random slopes) becomes
partially the same as measuring the fixed effect itself"
Woundn't this be only true if for some cells there is no data for any
subject? In tat case, yes, there is no way of estimating the variance of
the corresponding random effects, and therefore it would be equivalent to
estimating the fixed effect only. But here there are data for some of the
subjects; the only random effects estimated as zero are those corresponding
to the subjects with no data. Also, it is important to note that I get zero
random effects only if I use a diagonal var-cov matrix. If I used, for
example, compound symmetry, that is not the case.
@David. That is right; the problem arises only when I introduce random
slopes on mPair (and I should do that), which is a factor with 6 levels as
you said. I am not interested in the 'cycle' variable, and therefore I am
not using it for fixed nor random effects.
Best
Cristiano
On Fri, Jun 14, 2019 at 4:09 AM René <bimonosom using gmail.com> wrote:
> Ah now I think of the following:
> Estimating by-subject random slopes necessarily requires that the random
> slope (i.e. in all within-subject design cells) is measured on each
> subject. If, as you say, there are measurements missing in some
> design-cells for some subjects, then, actually, estimating the variance of
> fixed effects between subjects (just another word for by-subject random
> slopes) becomes partially the same as measuring the fixed effect itself,
> which is 'bad'. Furthermore, this might be similarly troubling when
> estimating by-subject intercepts, but for a slightly different reason,
> namely, (lets make it extreme) if half of the subjects have measures in all
> design cells, while the other half has only measures in some-design cells,
> then what would you expect how the intercepts are distributed (i.e. the
> subjects average response deviation from the grand mean), if there are
> systematic differences between the means in the design-cells? The a priori
> answer is, "probably not Gaussian", which is again 'bad' :))
>
> I would suggest to adjust the model-definition to reflect the fact that
> there are cell-measurements missing for some subjects (regardless of
> whether a model converges or not, but just because, this would be the only
> way to meaningfully interpret the model).
> I think this should work:
> Let's take the model from the last link you posted
>
> cc_marg ~ mPair*spd_des + diag(mPair:spd_des|ratID)
>
> Define a (-numeric-) variable (say "cell_exists") in the data frame which
> codes whether a subject (for all observations by that subject) has
> measurements in all cells (coded as 1), or not (coded as 0), such that all
> subjects of which you speak have missing data in some cells are 0.
> Then:
>
> cc_marg ~ mPair*spd_des + diag(0+cell_exists:mPair:spd_des|ratID)
>
> Will estimate (no intercepts and) only random slopes for subjects with
> cell_exists=1
> And to achieve the same for intercept (lets have a second variable which
> is identically coded as cell_exists to be as clear as possible:
> cell_exists_intercept)
>
> cc_marg ~ mPair*spd_des + diag(0+ cell_exists_intercept +cell_exists:mPair:spd_des|ratID)
>
> And the intercept then would be the "cell_exists_intercept".
> This should deal with the missing stuff :)
> But don't ask me how to call the random effects in the end :)) (random
> slopes for a sub-sample of subjects maybe), or the residuals (mixture
> between individual level model errors, and random intercept and slope
> variance for those subjects with incomplete data).
>
> Hope this helps (I guess there will be a solution eventually, there is not
> much left to do, except going Bayesian :))
> Best, René
>
>
>
> Am Fr., 14. Juni 2019 um 03:36 Uhr schrieb David Duffy <
> David.Duffy using qimrberghofer.edu.au>:
>
>> FWIW, on my machine,
>>
>> lmer(cc_marg ~ mPair*spd_des + (1|cycle) + (1|ratID), data=dat)
>>
>> runs without complaint. It's only when I add in mPair as fixed and random
>> that I get problems. I notice that cycle has a *lot* of levels,and the
>> distribution of cc_marg is pretty skewed. I always have trouble
>> understanding measurement models in a lmer formula - mPair are six
>> different measures, is that right? If that is the case, you might
>> cross-check your results by running in MCMCglmm as an explicit multivariate
>> model, and getting the same answers.
>>
>> Cheers, David Duffy.
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>
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