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

[Josie Galbraith]

Hi all,

I've been looking for solutions to the issue of complete separation for the
data I'm analysing for my PhD. I gather from everything I've read that I
should use a Bayesian GLMM - using blme in R.  I'm completely new to the
Bayesian framework (and this mailing list!) so apologies if my questions
are basic.

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?

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:

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

     AIC      BIC   logLik deviance df.resid
   112.1    128.0    -51.0    102.1      173

Scaled residuals:
    Min      1Q  Median      3Q     Max
-0.4464 -0.3814 -0.2813 -0.1737  3.5800

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

Correlation of Fixed Effects:
            (Intr) FOODNF SEASON
FOODNF      -0.864
SEASONSprng -0.954  0.824
FOODNF:SEAS  0.787 -0.892 -0.824

Advice or thoughts as to whether I'm on the right track would be greatly
appreciated.
Cheers,
Josie


-- 
*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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