Hi,


> >
> > Hi,
>
> > First I'd like to apologize for probably very novice questions: I am
> > new to this list as well as rather new in the field of mixed
> > model. Jobwise, I am aquatic/river ecologist (actually PhD student)
> > and not a statistician/mathematician and so most of my questions are
> > probably related to the topic of rivers/fish.
>
>   Actually, this is actually another not-really-about-mixed-models question
> (see below).
>

Thank you anyway for clarification of my questions. They really helped me.


>
> > I want use a GLMM with presence/absence data (logit-model) as response.
> The
> > model contains the binary response (presence/absence of a species at a
> > site) and 3 continuous predictors (fixed effects: habitat quality,
> > dispersal metric, metric of influence of barriers) as well as two random
> > effects (in my case species and sites).  For that purpose I am using the
> > R-function (g)lme from the package lme4.
>
>   That's (g)lmer ...
>

of course (g)lmer !




> > Now several questions appeared:
> >
> > 1) My predictors are partly highly skewed:
> > > summary(Pred1)
> >     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
> >   0.0000   0.0000   0.1143   9.0720   3.8400 616.4000
> > > summary(Pred2)
> >      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
> >  -7.44400  -0.00031   0.00000   0.41560   0.00019 255.70000
> > > summary(Pred3)
> >    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
> >  0.0000  0.1221  0.3716  0.3734  0.5914  0.9626
> >
> > In a standard regression model they would definitely need transformation
> > (e.g. log).
> > Is that the first step in a logistic mixed model too?
>
>   This is a common misconception.  The distribution of the
> predictors _is not relevant_ to the correctness of a linear or
> generalized (or LMM or GLMM) model (or additivity of
> predictor effects); you might want to transform
> the predictors in order to improve the _linearity_ of the model
> (on the linear predictor scale, i.e. the logit scale in this case),
> but there is _a priori_ nothing wrong with a skewed distribution
> of predictors.
>

So there are no problems with a skewed distribution of predictor and
if I read some post in this mailing list correctly there is also nothing
like overdispersion in the case of logistic binary response
(presence/absence)
models I need to care about.



>
> > 2) As I am interested in a way to compare the relative size effects of
> the
> > three predictors I had been referred to the simple approach of Schielzeth
> > 2010 (thank you for that tip Mr. Bolker!). In this article it is
> > recommended to scale the predictors for comparison of their importance
> > and/or to scale and center in case one also wants to model interactions
> > between continuous predictor variables.
> > So my question how would that interfere with any transformation of the
> > variables as described in my first question? And what is the correct
> order:
> > first transformation than scaling?
>
>   If you find it useful to transform your predictors, it will
> probably be easier to transform first and then center/scale.


> > 3) After reading some list posts like this here:
> > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015591.html I
> > learnt that comparing two lmer-models (likelihood ratio test with
> anova())
> > is the way to find out if a predictor or interaction is significant in a
> > model. Thus this method (comparing complex with less complex) models can
> > lead to the most parsimonious model comprising only of the most important
> > predictors and interactions. After reading Schielzeth 2010, I think this
> > comparison test should be made with centered and scaled variables (and if
> > needed transformed before)?
>
>   In principle, centering and scaling variables (only) should not
> affect the overall fit/log-likelihood of a model, it just eases
> interpretation.  So the answer is "it doesn't matter".
>
> > 4) This brings me than to my final interpretation of the results. In
> > relation to that I came across the post as mentioned before and a
> tutorial:
> > http://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm  I
> think
> > that tutorial really helped me to understand the meaning of odds etc. But
> > how can odds be interpreted if the predictors were transformed before?
>
>   I don't think transforming the _predictors_ changes the interpretation
> of the _response variable_ ...
>
> > First I'd calculate the final parsimonious model once with transformed
> and
> > scaled predictors to get an idea about the relative impact of each
> > independent predictor. Second, the same model with transformed but with
> > unscaled predictors can provide absolute parameter estimates for the
> odds.
>
>   Something like that.
>


Thank you again, So I think I can proceed that way.

cheers,
Johannes

	[[alternative HTML version deleted]]

