[R-sig-ME] glmer and lme4 - Quick question

[Joseph Maina]

Hi,
I am running glmer in a model selection framework with >30 explanatory variables, where I am first  generating all possible combinations of variables but with a multicollinearity test results coinstraint,  before fitting the gmler. I am also including random effects of ‘Year' (~20) and Regions (~13). The objective is to find the best model and determine the relative influence among predictors, and also to predict the model over space to global pixels.

My question regards the model structure of lme4 that I should adopt. I am currently fitting my model in the following structure:
m1<-glmer(y  ~ x1 + x2 + x3 + (1 |Region) + (1 | Year) +(1|Region), family=binomial('logit'),data=all.data)

However, I have been advised that in order to have a varying intercept and slope among my Regions (one of the random effects), I should fit my model as follows:
m1<-glmer(y ~ x1+ (0+x1|Region) + x2 + (0+x2|Region) + x3 + (0+x3|Region) + (1 | Year) +(1|Region), family=binomial('logit'),data=all.data)

The latter is a slightly complex structure and I am running into convergence issues. I was wondering what are the merits of using either structure?.  Also in the second structure, I am not sure what ‘0+’ means or what value it adds to the analyses. I also found that when using the first model structure if I take out the ‘Region’ random effect, the estimates for some of the variables change signs, and therefore could have an implicaition on the interpretation. 

Thanks,

Joseph


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