[ADMB Users] help with glmmADMB 0.6.4 - function maximizer failed

[Rafael Mares]

Dear all

I am having problems trying to run a model using the alpha version of
glmmADMB (0.6.4) using R (2.13.1) in Windows (thank you for catering
to Windows users!), and I would greatly appreciate some help... maybe
I'm missing something obvious. I apologise for the long email.

My count response variable (number of feeds) fits a negative binomial
distribution and the explanatory variables are all continuous (except
for 1 which is binary) and have been standardised (mean centred and
divided by 2 standard deviations). The 2 random effects are individual
identity (id) and observation session (rown)... observation sessions
are unique (it's a factor using date and group identity) and always
have more than 1 individual per session and individuals typically
appear in more than one observation session.

This is the model I'm trying to run:

glmmadmb(pfeeds ~
rover+pups+pupage+grpsize+rain+totalobs+grpsize:pups,
random=~(1|rown)+(1|id), data=dat, family="nbinom")



--- This is the error I get when I use the full data set (number of
observations: total=6133, rown=393, id=440):

The function maximizer failed
In addition: Warning messages:
1: running command 'C:\WINDOWS\system32\cmd.exe /c "C:/Documents and
Settings/larguser1/My
Documents/R/win-library/2.13/glmmADMB/bin/windows32/glmmadmb.exe"
-maxfn 500' had status 1
2: In shell(cmd, invisible = TRUE) :
  '"C:/Documents and Settings/larguser1/My
Documents/R/win-library/2.13/glmmADMB/bin/windows32/glmmadmb.exe"
-maxfn 500' execution failed with error code 1



--- The model with the full data set runs with ~(1|id) on it's own,
but not with ~(1|rown) on it's own - I get the same error as above.
Results from full data set with only ~(1|id)

             Estimate Std. Error z value Pr(>|z|)
(Intercept)   -0.4605     0.0444  -10.36  < 2e-16 ***
rover         -0.2483     0.0505   -4.92  8.6e-07 ***
pups           0.1092     0.0500    2.18    0.029 *
pupage        -0.5155     0.0483  -10.67  < 2e-16 ***
grpsize       -0.4983     0.0627   -7.95  1.9e-15 ***
rain           0.2759     0.0481    5.73  9.9e-09 ***
totalobs       0.1850     0.0437    4.23  2.3e-05 ***
pups:grpsize   0.1088     0.1303    0.84    0.404
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of observations: total=6133, =440
Random effect variance(s):
$id
            (Intercept)
(Intercept)     0.49418

Negative binomial dispersion parameter: 1.2615 (std. err.: 0.079547)

Log-likelihood: -7059.28



--- However, using a random subset of the data (number of
observations: total=1539, rown=89, id=368), the original model with
both random effects runs ok (but with very different results).

Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept)   -0.7736     0.1161   -6.67  2.6e-11 ***
rover         -0.3265     0.2440   -1.34    0.181
pups           0.0958     0.2272    0.42    0.673
pupage        -0.3740     0.2049   -1.83    0.068 .
grpsize       -0.7771     0.1932   -4.02  5.8e-05 ***
rain           0.4118     0.2114    1.95    0.051 .
totalobs       0.0908     0.1930    0.47    0.638
pups:grpsize   0.4392     0.4554    0.96    0.335
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of observations: total=1539, =89, =368
Random effect variance(s):
$rown
            (Intercept)
(Intercept)     0.56835

$id
            (Intercept)
(Intercept)     0.25791

Negative binomial dispersion parameter: 2.639 (std. err.: 0.6241)

Log-likelihood: -1627.18



Is there a fundamental problem with my random effects structure or is
it a matter of changing something with "extra.args" in the model
specification? I've played around a bit with extra.args, changing
-mno, -ams and trying to change -maxfn, but I keep getting the same
error for my original model with the full data set.

I thank you in advance for any help.
All the best,

Rafael


--
Rafael Mares
Large Animal Research Group (LARG)
Department of Zoology
University of Cambridge
Downing Street
Cambridge
CB2 3EJ