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

[Thierry Onkelinx]

Dear Joseph,

Have a look at http://glmm.wikidot.com/faq and search for "model
specification".

Convergence warnings might indicate that your model is too complex for the
data.

I would consider something like L1-penalisation for the model selection.
Have a look at the glmmLasso package.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2015-06-24 2:10 GMT+02:00 Joseph Maina <mainajm op gmail.com>:

> 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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