[R-sig-ME] Problems with glmmadmb function for zero inflated count data

Irene Rojo ire.rojo at gmail.com
Mon Apr 9 20:07:58 CEST 2018


Dear all,

I am trying to perform analyses for fish density but I am having several
problems. Also I am not an expert in statistics (at all) so I apologise if
my questions are too basic.

We sampled in 5 zones (ZN; fixed factor with 5 levels) and 3 protection
levels in each zone (PL; three levels). We selected 3, 6 and 9 sites (ST;
random effect) in each of the protection levels, respectively, and carried
out 3 underwater visual censuses in each site.

I am modeling counts of the most abundant species together as the response
variable, and including the area sampled as the offset term of the formula.
And I have so many zeros in my data.

I first tried the "glmer" function but there is so much overdispersion.
Then I thought about the "zeroinfl" function but it doesn't deal with
random effects. It works well if I miss the randon factor, but I don't
think that is right.

So I am trying to fit the models with the "glmmadmb" function as follows:

m0<- glmmadmb(n~  ZN*PL +
          offset(log(area))
          + ( 1 | ST),
          data = den,
          zeroInflation = TRUE,
          family = "nbinom", link = "logit"
         )

I am getting a huge error, either for the poisson or nbinom families:

Parameters were estimated, but standard errors were not: the most likely
problem is that the curvature at MLE was zero or negative
Error in glmmadmb(nTRT10 ~ ZN * PL + offset(log(areaTRT10)) + (1 | ST),  :
  The function maximizer failed (couldn't find parameter file)
Troubleshooting steps include (1) run with 'save.dir' set and inspect
output files; (2) change run parameters: see '?admbControl';(3) re-run with
debug=TRUE for more information on failure mode
In addition: Warning message:
running command 'C:\Windows\system32\cmd.exe /c glmmadmb -maxfn 500 -maxph
5 -noinit -shess' had status

I don't understand the error, so I can't think how to fix it. Can anyone
help with this?

Also, is it right to use this function for the kind of data I have? If not,
could you please suggest a better option?

Thanks in advance,

Irene

	[[alternative HTML version deleted]]



More information about the R-sig-mixed-models mailing list