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lmer with spatial and temporal random factors, not nested

2 messages · Marte Lilleeng, Ben Bolker

Original
Marte Lilleeng
# 
Hi, I am new to this list.
I have a question regarding including both spatial and temporal random
factors in lmer. These two are not nested, and an example of model I
try to fit is

model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year),
family=poisson, REML=FALSE),
where
richness = integer
Y & Treatment = factor
Canopy & Veg_cm = numerical, continous
Site/Block/Plot= factor
Year = integer

I get the following warning message:

Warning messages:
1: In mer_finalize(ans) :
  Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 432
2: In mer_finalize(ans) : singular convergence (7)

Is this due to the nature of my fixed/random factors or the way I put
up the random factors?

In lme I could include a component for autocorrelation,
ex:cor=corAR1(form=~Year|Site/Block/ID). Does the equivalent exist for
lmer?

I will be very happy if someone can help me!
1 day later
Ben Bolker
# 
Marte Lilleeng <mlilleeng <at> gmail.com> writes:
The r-sig-mixed-models at r-project.org mailing list would
be more appropriate for this question -- please direct any
further questions there ...
Fine, but REML=FALSE is unnecessary/irrelevant for generalized
linear mixed model (family!="gaussian") fits.
Hard to tell exactly.  It's probably due to overfitting and/or
lack of balance (glmer handles lack of balance, but extreme
lack of balance can lead to technical difficulties like this one).
No, sorry.

   Crossed random effects are possible in lme (see p. 165?)
of Pinheiro and Bates 2000, and glmmPQL in the MASS package
can handle a Poisson response, so that might be the best way
to go.  However, I would also strongly encourage you to
do some graphical exploration of your data and make sure there
aren't outliers, almost-empty blocks, etc.

  Ben Bolker