[R-sig-ME] Choosing appropriate priors for bglmer mixed models in blme

[Josie Galbraith]

Thanks Ben,
I didn't have problems with singular estimates of variance components with
this data set.  However, I have a few other pathogens/parasites that I'm
looking at (I'm running separate models for each), and after looking at all
of them some do have zero variances for the random effect, either in
addition to large parameter estimates or alongside reasonable parameter
estimates.
Should I be also be imposing a covariance prior in either of these cases?

As a related aside, my data are collected from individual birds - captured
over 4 sampling rounds (6 months apart).  While the majority of
observations are independent, there is a small proportion of birds that
were recaptured in a subsequent sampling round (between 2–15% of
observations, depending on which response variable).  I have modelled my
data both both with and without bird ID as a random effect.  Including it
seems to cause more problems with zero variances.  Is this because too few
of the birds have actually been resampled?

Cheers,
Josie



> Josie Galbraith <josie.galbraith at ...> writes:
>
> >
>
> [snip]
>
> >
> > I'm after some advice on how to choose which priors to use.  I gather I
> > need to impose a weak prior on the fixed effects of my model but no
> > covariance priors - is this correct?  Can I use a default prior (i.e. t,
> or
> > normal defaults in the blme package) or does it depend on my data?  What
> is
> > considered a suitably weak prior?
>
>    If all you're trying to do is deal with complete separation (and not,
> e.g. singular estimates of variance components [typically indicated
> by zero variances or +/- 1 correlations, although I'm not sure those
> are necessary conditions for singularity]), then it should be OK
> to put the prior only on the fixed effects.  Generally speaking a
> weak prior is one with a standard deviation that is large relative
> to the expected scale of the effect (e.g. we might say sigma=10 is
> large, but it won't be if the units of measurement are very small
> so that a typical value of the mean is 100,000 ...)
>
> > I am running binomial models for epidemiology data (response variable is
> > presence/absence of lesions), with 2 fixed effects (FOOD: F/NF; SEASON:
> > Autumn/Spring) and a random effect (SITE: 8 levels).  The main goal of
> > these models is to test for an effect of the treatment 'FOOD.'  I'm
> > guessing from what I've read, that my model should be something like the
> > following:
>
>
> This seems fairly reasonable at first glance.  Where were you seeing
> the complete separation, though?   I would normally expect to
> see at least one of the parameters still being reasonably large
> if that's the case.
>
> > bglmer (LESION ~ FOOD*SEASON +(1|SITE), data = SEYE.df, family =
> binomial,
> > fixef.prior = normal, cov.prior = NULL)
> >
> > This is the output when I run the model:
> >
> > Fixef prior: normal(sd = c(10, 2.5, ...), corr = c(0 ...), common.scale =
> > FALSE)
> > Prior dev  : 18.2419
> >
> > Generalized linear mixed model fit by maximum likelihood (Laplace
> > Approximation) [
> > bglmerMod]
> >  Family: binomial ( logit )
> > Formula: LESION ~ FOOD * SEASON + (1 | SITE)
> >    Data: SEYE.df
> >
>
> [snip]
>
> > Random effects:
> >  Groups Name        Variance Std.Dev.
> >  SITE   (Intercept) 0.3064   0.5535
> > Number of obs: 178, groups: SITE, 8
> >
> > Fixed effects:
> >                     Estimate Std. Error z value Pr(>|z|)
> > (Intercept)          -3.7664     1.4551  -2.588  0.00964 **
> > FOODNF                0.5462     1.6838   0.324  0.74567
> > SEASONSpring          1.7529     1.4721   1.191  0.23378
> > FOODNF:SEASONSpring  -0.8151     1.7855  -0.456  0.64803
> > ---
> > Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
> >
>
> [snip]
>
> ------------------------------
>


-- 
*Josie Galbraith* MSc (hons)

PhD candidate
*University of Auckland *
Joint Graduate School in Biodiversity and Biosecurity ● School of
Biological Sciences ● Tamaki Campus ● Private Bag 92019 ● Auckland 1142* ●
P:* 09-373 7599 ext. 83132* ● E:* josie.galbraith at gmail.com* ● W: * UoA Web
Profile <https://unidirectory.auckland.ac.nz/profile/jgal026> and
*www.birdfeedingnz.weebly.com/* <http://birdfeedingnz.weebly.com/>

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