[R-sig-ME] distribution of random effects glmmTMB - covariance structure
Vidal, Tiffany (FWE )
tiff@ny@vid@l @ending from @t@te@m@@u@
Thu Sep 6 21:44:45 CEST 2018
Thank you for this discussion and suggested materials. This is making much more sense now, and part of the problem could certainly be that the model is not fully specified yet, but was written to try to understand how the different covariance structures were operating. I'll dig into this more - thanks for the guidance!
From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces using r-project.org] On Behalf Of D. Rizopoulos
Sent: Thursday, September 06, 2018 3:30 PM
To: Ben Bolker; r-sig-mixed-models using r-project.org
Subject: Re: [R-sig-ME] distribution of random effects glmmTMB - covariance structure
Well, AFAIK checking the normality assumption of the prior distribution of the random effects using the EB estimates can be problematic. This is because they have different distributions that depend on the design matrices of the fixed and random effects of each subject. And, also because there is the effect of shrinkage that has an impact on their distribution.
For more on these points, a nice overview is given in Section 7.8 of book of Verbeke and Molenberghs (2000), "Linear Mixed Models for Longitudinal Data", Springer-Verlag.
In any case, if we're talking about linear mixed models, it has be shown that misspecifying the prior distribution of the random effects has very little impact in parameter estimates and standard errors for the fixed effects.
On 9/6/2018 9:05 PM, Ben Bolker wrote:
> While the distribution of conditional modes is certainly not
> assumed to be exactly N(0,s^2), informally, if the observed
> distribution of conditional modes is far from zero-centered Gaussian,
> I might worry about misspecification of the model. I know of the
> existence of a literature on the diagnosis and effects of model
> misspecification (especially of the distribution of conditional modes) in (G)LMMs -- e.g.
> go to
> https://urldefense.proofpoint.com/v2/url?u=http-3A__bbolker.github.io_mixedmodels-2Dmisc_glmmbib.html&d=DwICAg&c=lDF7oMaPKXpkYvev9V-fVahWL0QWnGCCAfCDz1Bns_w&r=sqewvGWc5AUwYJSPkw7hFHEzecJLoIBs7pn2DqRZwbw&m=1wyRjyOI7n3_7Xn0v20mIyploRKDemTvOsjO0QUR1TM&s=ZPfMOcctjXZQxfX9fEQ8D2iZ5pDIvEopo1I67gQa7Nc&e= and search for "misspec" -- but I don't know its contents well at all.
> (1) adding group-level covariates (to explain some of the non-Normal
> among-group variability) can help, if you have any such information
> (2) one more question about your random-effect specification. Is
> time being treated as categorical or continuous?
> If categorical:
> - if there are n time points, us(time+0|Subject) will have
> n*(n+1)/2 parameters, which could get out of hand (you'll be trying to
> estimate the full variance-covariance matrix among all n observations
> for each subject -- you'll need lots of subjects to make this work).
> Could be worth trying an ar1() model instead?
> - allowing for a *continuous*, fixed effect of time in addition
> to the random effect could help (again, by explaining some of the
> systematic variability)
> - if continuous: I'm not sure why you would suppress the intercept
> On 2018-09-06 02:42 PM, D. Rizopoulos wrote:
>> Logically, the ranef() gives you the empirical Bayes estimates of the
>> random effects. Note that the distribution (and as a result the
>> variance and covariances) of these is not the same as the
>> distribution you specified in the formula of the model. Namely, the
>> distribution you define is the _prior_ distribution of the random
>> effects, whereas the empirical Bayes estimates are coming from the
>> posterior of the random effects.
>> In math terms, the choice of us() of diag() specifies the
>> distribution [b] of the random effects, whereas from ranef() you get
>> the modes or means of the posterior distribution
>> [b | y] which is proportional to [y | b] * [b],
>> where y denotes you Count outcome, and [y | b] denotes the
>> distribution of your outcome.
>> On 9/6/2018 7:59 PM, Vidal, Tiffany (FWE ) wrote:
>>> I'm unclear about the distributional assumptions regarding the random effects in glmmTMB, using different covariance structures. It is my understanding that the default is unstructured covariance structure. When estimating a vector of random effects, what is the assumption about the distribution of the factor levels within each grouping? I'm usually assuming normality with a mean of 0 and estimated variance. This doesn't seem to hold looking at the ranef(mod) for the different grouping variables.
>>> For example:
>>> mod <- glmmTMB(Count ~ us(time + 0|Subject)) or mod <- glmmTMB(Count
>>> ~ diag(time + 0|Subject))
>>> Here, I'm modeling (I think) variability among subjects through time (e.g., a different subject variance in each time step), and assuming that the repeated measures within each individual subject at time t, come from some distribution. If the assumed distribution was normal with a mean of 0, I would expect the sum of the Subject BLUPs in each year to approximate 0, but that doesn't appear to be the case. Any clarification on this would be appreciated.
>>> Thank you,
>>> [[alternative HTML version deleted]]
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