[ADMB Users] help with glmmADMB 0.6.4 - function maximizer failed
Rafael Mares
crm53 at cam.ac.uk
Fri Sep 23 04:13:34 PDT 2011
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
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