Some thoughts...

Cant help to much with the ML specific stuff, this is the sort of problem I
would tend to address in a Bayesian analysis.
Which you might consider trying, considering the low number of groups you
have in your random affects.
Without seeing your data, I'm not sure a Poisson regression is the best
approach. Whilst your data will be discrete, I suspect it is bounded by the
maximum number of seedlings initially present at a site. I would approach
this a logistic regression on the survival of individuals if you have that
data.

Using Year as a random effect could cause you some problems as your probably
not dealing with independent data in each time period i.e. your hazard
function is likely to be time dependent (see Zens & Peart 2003 TREE vol 18,
pp366-373).

AIC like other infomation critera (BIC,DIC) cannot really be interpreted by
itself. You use it as a model selection tool. Comparing two alternative
models you would pick the one with the lower AIC value.

The 1 in your lmer call, refers to the intercept. i.e. your model is a
random/varying intercepts model where you have two intercept terms, one that
is allowed to vary by site and the other by year.

PS. I am working a very similar topic in southern Australia. fell free to
contact me off list to talk more.

On Sun, Sep 13, 2009 at 9:55 PM, Raldo Kruger <raldo.kruger@gmail.com>wrote:

> Hi,
>
> I'm new to R and GLMMs, and I've been unable to find the answers to my
> questions by trawling through the R help archives. I'm hoping someone
> here can help me.
> I'm running an analysis on Seedling survival (count data=Poisson
> distribution) on restoration sites, and my main interest is in
> determining whether the Nutrients (N) and water absorbing polymer Gel
> (G) additions to the soil substrate contribute positively to the
> survival of the seedlings, over a 3 year time period (for simplicity
> I'm just using 3 time periods, each in the same season for the 3
> successive years).
> Fixed factors: Nutrients (0 and 1), Gel (0 and 1)
> Random factors: Site (4 non replicate sites), Year (3 time periods)
> Response variable: Seedling numbers (counts) / 0.25m2 plot
>
> According to the decision tree on page 131 in Bolker et al. (2008, in
> TREE; thanks, very useful paper!), most of my data sets should be
> analysed with Laplace or GHQ model with Wald t or F statistic (since
> it is non-normal, can’t be transformed to normality, has a mean <  5,
> has less than 3 random effects, and is overdispersed). I’m using the
> glmer {lme4} function, since it allows for Laplace or GHQ, as well as
> more than one random factor (glmmML {glmmML) and glmPQL {MASS}
> apparently does not), as follows:
> > m1<-glmer(Seedlings~N*G*(1|Year)*(1|Site), data=ex5m,
> family=poisson(link="log"))
>
> My questions are:
> 1)      The model returns Z values, and I’m unable to find an argument in
> the function where this can be changed to return a t or F value (as
> Bolker et al. suggests I should use for my data).
> 2)      I’m unsure what the AIC or QAIC value means, other than knowing
> that it should be as low as possible. Is there a rule of thumb of what
> is a good AIC value? Mine are in the region of 2230.
> 3)      The default in glmer {lme4) for the argument  nAGQ = 1, which uses
> the Laplace approximation. When nAGQ >1, it uses the GHQ method, but
> I’m unsure how to determine the correct number of Gauss-Hermite points
> to enter in the argument when using this method.  How is this
> determined?
> 4)      Some of my data sets have means >5, and are also overdispersed, and
> according to Bolker et al. should be analysed using a GLMM with PQL
> and a Wald t or F. However, the glmmPQL {glmmPQL} does not accept more
> than one random factor, and I have two, so how do I deal with that?
> 5) Lastly, what does the "1" imply in the random factor term, e.g.
> (1|Site), and how does this affect the analysis?
>
> Many thanks,
> Raldo Kruger
> MSc Student
> Unversity of Cape Town
> South Africa
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 
Will Morris
Masters of Philosophy candidate
Vesk Plant Ecology Lab
The School of Botany
The University of Melbourne
Australia
Phone: +61 3 8344 0120
http://www.botany.unimelb.edu.au/vesk/

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