[R] errors with hurdle negative binomial mixed effect models

[Ben Bolker]

Marta Lomas <lomasvega <at> hotmail.com> writes:

> 
> Hello! 

> I am new in the mailing list for R help and I hope to be able to
>  formulate a good question easy to understand.

  We hope so too :-)

 [snip]

  I will take a first crack at this here, but follow-ups should
probably be redirected to the r-sig-mixed-models at r-project.org
mailing list, which is more appropriate for questions dealing
with (G)LMMs. 

> I am modeling my data set with hurdle negative binomial mixed
> effects, to find the correlation of some bird counts with
> environmental (categorical and continuous) variables.
 
> When I run different models I have always an error. For instance:
> 
> -For the truncated modeling of the non-zero counts:
> 
> > HURgvgsw <- glmmadmb(count~ GVG*sward + (1|week), 
>   data=subset(SW_GVG,count>0), + family=
> "truncnbinom") 
> 
> Error en glmmadmb(count ~ GVG * sward + (1 | week), data = subset(SW_GVG,  : 
> rank of X = 6 < ncol(X) = 10
> 
> -Or the binomial part where the zeros are modeled with the non-zeros: 
> 
> > HURgvgsw <- glmmadmb(count~ sward*GVG + (1|week) + (1|cluster), 
>  data=SW_GVG, family= "binomial") 
> 
> Error en glmmadmb(count ~ sward * GVG + (1 | week) + 
> (1 | cluster), data = SW_GVG,  : 
> rank of X = 13 < ncol(X) = 15
> 
> Would you have the solution to this?

   This error message is telling you that some of your fixed-effect
variables (which are, internally, combined into the fixed-effect
design matrix X) are perfectly multicollinear.  This is most likely
happening because sward and GVG are categorical variables (or at
least are being treated as categorical variables) and some 
combinations are missing from the data set (for future reference:
the output of summary(SW_GVG) is useful for diagnosis).

For more information, search http://glmm.wikidot.com/faq for the
word 'rank'

  Good luck 
    Ben Bolker