I want to assess the effects of technical practices (factors) on nematodes
counts on a dataset made of observations on 21 plots with 5 samples within
a plot. Each plot is characterized by a set of levels of technical
practices.



To overcome plot effect, which I don’t want to consider, and because
nematodes counts are overdispersed (more than 75% of the values are 0), I
tried two methods (http://glmm.wikidot.com/faq) I’ve never used before :

i)       negative binomial GLMM (function glmmadmb ; I tried the two
parameterization of the negative binomial distribution.

ii)      individual-level random effects with GLMM (function glmer).



The model doesn’t run and R return the following error messages :



i)                 with the negative binomial

> Parameters were estimated, but not standard errors were not: the most
likely problem is that the curvature at MLE was zero or negative

> Erreur dans glmmadmb(Nrad ~ Pprec + Pws + Pherb2 + Pins + Peng2 + (1 |
plot),  :

> The function maximizer failed (couldn't find STD file) Troubleshooting
steps include (1) run with 'save.dir' set and inspect output files; (2)
change run parameters: see '?admbControl'



ii)                with individual-level random effects

> Error : PIRLS step-halving failed to reduce deviance in pwrssUpdate



I suspect that these errors come from the very high proportion of 0 I have
in my data (I tried with another species of nematodes I found in much more
samples and the model ran with the 2 methods).



Do these errors really come from the high distribution of my data ?

If yes, how can I solve it ?



Thank you for your replies.

Gaëlle Damour








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