[R-sig-ME] Help with glmmADMB and ZIP mixed effects models
Wang, Shirley
shirleywang at g.harvard.edu
Sat May 20 05:27:11 CEST 2017
Hello,
I'm hoping to run a zero-inflated mixed effects poisson regression model
for count data. My dataset includes 111 subjects, randomized to 3 groups
and each assessed 7 times (777 observations). Through searching online, it
seems like the glmmADMB package is my best bet. From my understanding, NA
missing values should be removed prior to analysis -- this leaves me with
264 observations. I tried running glmmadmb() with zero inflation, poisson,
and mixed effects specified; however, I'm running into some errors. This is
my dataset:
> str(x12)
'data.frame': 264 obs. of 12 variables:
$ Subject : Factor w/ 111 levels "1","2","3","4",..: 1 1 1 1 2 2 2 3 4 4
...
$ Time : int 1 5 6 7 1 5 7 1 1 6 ...
$ Group : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ SRS : int 56 55 56 55 50 46 42 49 47 42 ...
$ BDI : int 40 23 41 35 44 14 11 22 16 9 ...
$ ERQ : int 14 11 14 15 18 12 15 14 26 27 ...
$ Cut : int 3 4 10 15 1 1 0 1 0 0 ...
$ NSSI : int 8 14 32 58 2 2 8 2 0 1 ...
$ Ideation : int 2 0 5 10 5 3 0 1 0 0 ...
$ Plan : int 1 0 2 8 1 4 5 0 0 0 ...
$ Stop : int 2 2 1 2 3 3 3 2 3 2 ...
$ Likelihood: int 4 4 4 4 3 4 4 4 3 3 ...
- attr(*, "na.action")=Class 'omit' Named int [1:513] 2 3 4 9 10 11 13 16
17 18 ...
.. ..- attr(*, "names")= chr [1:513] "2" "3" "4" "9" ...
>
And here is my session info:
> sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X Yosemite 10.10.5
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_1.1-13 Matrix_1.2-8 pscl_1.4.9 lattice_0.20-34
[5] glmmADMB_0.8.3.3 MASS_7.3-45
loaded via a namespace (and not attached):
[1] Rcpp_0.12.10 magrittr_1.5 splines_3.3.3 devtools_1.12.0
[5] munsell_0.4.3 colorspace_1.3-2 minqa_1.2.4 stringr_1.2.0
[9] plyr_1.8.4 tools_3.3.3 grid_3.3.3 nlme_3.1-131
[13] gtable_0.2.0 coda_0.19-1 withr_1.0.2 digest_0.6.12
[17] lazyeval_0.2.0 tibble_1.3.0 R2admb_0.7.15 nloptr_1.0.4
[21] ggplot2_2.2.1 memoise_1.0.0 stringi_1.1.5 scales_0.4.1
>
I tried to use the following formula, with "Cut" as my outcome, and got
this lengthy error message:
> results <- glmmadmb(Cut ~ Time + Group + (1|Subject), data = x12,
zeroInflation = TRUE, family = "poisson")
matrix not pos definite in sparse choleski
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(Cut ~ Time + Group + (1 | Subject), data = x12,
zeroInflation = TRUE, :
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 './glmmadmb -maxfn 500 -maxph 5 -noinit -shess' had status
42
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
matrix not pos definite in sparse choleski
Error: Invalid index 111 used for array range [0, 110] in "double&
dvector::operator[] (int i)".
invalid index for array
>
How can I go about fixing this error? Alternatively, is there another way
to run ZIP mixed models for count data? I had been successfully using the
lme4 package w/ lmer function to run linear mixed models for continuous
data, but I'm not sure whether this package can also handle zero-inflated
count data.
Thank you so much in advance for any help or advice you might be able to
provide!
Best,
Shirley
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