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errors with lme4

Ben Bolker 
[cc'ing to r-sig-mixed-models list]
On 11-11-24 03:25 PM, Alessio Unisi wrote:
Knowing *neither* R *nor* statistics can be a fairly dangerous
combination.  If you ask politely, people on the R help lists will often
help with statistical questions, but they are technically off-topic.
There are other places (such as http://stats.stackexchange.com/ ) for
asking statistics questions ... and it would really be very best to see
if you can get some local help (classes or helpful colleagues/fellow
students/professors/consultants).
By "perfectly multicollinear" I don't mean that they are strongly
collinear (which ecologists often worry about, correctly, but sometimes
more than they need to) but rather "perfectly".

  For example, suppose you ran a 2x2 factorial design (e.g. effects of
temperature and light) but ended up with a missing "corner" (e.g. no
samples in the high-light/high-temperature combination).  You would then
be unable to estimate an interaction term, because you would be trying
to estimate 4 parameters (intercept/grand mean, light effect,
temperature effect, interaction) from only three independent sets of
data.  This is the same idea: some of your predictors probably line up
*perfectly* with combinations of other predictors.
You're correct that you will need to get back to glmer() eventually,
but I wanted you try out glm() because the presence of NAs in your
coefficient vector will confirm that collinearity is the problem, not
some other issue with glmer ...
When you run the analysis, R will drop rows from your data set that
have NAs in any of the predictors.  It looks like you have a total of
152 observations, but I wonder how many there are with complete records.
 nrow(na.omit(fledge)) will tell you this.
Can we please see the results of summary(fledge)?

  It would be good if you were willing to post your whole data set
somewhere for download (or at a pinch e-mail it to me).

  Ben Bolker