How should I choose the covariance prior for my bglmer model?
This is a model which has the singularity problem.
mod <- Y ~ X*Condition + (X*Condition|subject) # Y = logit variable # X = continuous variable # Condition = values A and B, dummy coded; the design is repeated # so all participants go through both Conditions # subject = random effects for different subjects summary(model) Random effects: Groups Name Variance Std.Dev. Corr subject (Intercept) 0.85052 0.9222 X 0.08427 0.2903 -1.00 ConditionB 0.54367 0.7373 -0.37 0.37 X:ConditionB 0.14812 0.3849 0.26 -0.26 -0.56 Number of obs: 39401, groups: subject, 219 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.49686 0.06909 36.14 < 2e-16 *** X -1.03854 0.03812 -27.24 < 2e-16 *** ConditionB -0.19707 0.06382 -3.09 0.00202 ** X:ConditionB 0.22809 0.05356 4.26 2.06e-05 ***
I would like to use the
blme package to run the model with a cov. prior. However, there are multiple options to choose from, e.g.
- What should guide my choice of cov.prior?
- Is it appropriate to use the
rePCAfunction to check for singularity, i.e. overfitting in
- Is it appropriate to use the likelihood ratio test with
anovafunction to reduce the overfitted