glmer and lme4 - Quick question

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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