glmmADMB question

Thank you for the reply and advice; I tried blme and it worked using the
following (there are 7 levels to my fixed effect):


m <- bglmer(Ges~ Condition+(1|Subject),data=Dat,
           family=poisson,
           fixef.prior = normal(cov = diag(9,7)))

However, when I tried to use different outcome variables, some models again
failed to converge with the following error :

In get("checkConv", lme4Namespace)(attr(opt, "derivs"), opt$par,  :
  Model failed to converge with max|grad| = 0.73863 (tol = 0.001, component
1)

Do you think that this is something that can be fixed by adjusting the
controls within bglmer?

Thank you very much for the advice,
Jenny

On 8 January 2017 at 10:19, Ben Bolker <bbolker at gmail.com> wrote:

  A few suggestions:

- your "crazy" parameters below, and your statement that

Some issues with my data are that for one of the conditions, the count
of Ges was 0 for all subjects. Similarly, for some subjects, the count
for Ges  was 0 across all conditions.

 suggest that you have an issue of complete separation (e.g. see

<http://stats.stackexchange.com/questions/128742/mixed-logistic-model-with-complete-separation>;
however, the solutions listed there don't currently work in glmmADMB or
glmmTMB ... are you sure you need zero-inflation?  Lots of zeros doesn't
necessarily mean zero-inflation (it could just mean a Poisson/NB with a
very low mean)

  The options I know of for handling complete separation in GLMMs in R
include the blme package (can do anything glmer does, but *not* NB
models - although you could approximate that via a logNormal-Poisson
model); MCMCglmm; and brms.  The latter two can handle zero-inflated
models, but take you into the deep (Bayesian) end of the pool ...

On 17-01-08 09:32 AM, Jennifer Botting wrote:

Hi,

I'm having trouble running a ZIPGLMM in glmmADMB.  I am comparing the
number of behaviours exhibited by 12 individuals over 7 conditions. Each
subject was tested 4 times.

str(Dat)

'data.frame': 329 obs. of  26 variables:
 $ Subject      : Factor w/ 12 levels "Baraka","Batang",..: 11 11 11 11
11
11 11 11 11 11 ...
 $ Sex          : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
 $ History      : Factor w/ 2 levels "HR","MR": 1 1 1 1 1 1 1 1 1 1 ...
 $ Session      : int  1 2 3 4 1 2 3 4 1 2 ...
 $ Order        : int  3 1 1 4 5 5 5 2 1 2 ...
 $ Condition    : Factor w/ 7 levels "A ","B","BE",..: 1 1 1 1 2 2 2 2 3
3
...
 $ Voc: int  0 0 0 0 7 1 5 5 6 3 ...
 $ Non  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Fac       : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Ges     : int  0 0 0 0 0 0 0 1 0 0 ...
...............................

The data include a very large number of zeros, so I tried the following
formula in glmmADMB, starting with Ges as my outcome variable:

*> m <- glmmadmb(formula = Ges ~ Condition + (1 | Subject), data = Dat,
family = "poisson", zeroInflation = TRUE) *

and got the following error:

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(formula = Ges~ Condition + (1 | Subject), data = Dat,
:
  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 1

When I ran it with debug=TRUE, I got the following output:

Parameters were estimated, but standard errors were not: the most likely
problem is that the curvature at MLE was zero or negative
run failed:  Initial statistics: 7 variables; iteration 0; function
evaluation 0; phase 1 Function value  3.7639324e+002; maximum gradient
component mag  1.2938e+001 Var   Value    Gradient   |Var   Value
 Gradient   |Var   Value    Gradient   1  0.00000  1.2938e+001 |  2
 0.00000  9.7086e-001 |  3  0.00000  1.6197e+000   4  0.00000
-2.6734e+000
|  5  0.00000 -5.9309e-002 |  6  0.00000 -2.6798e+000   7  0.00000
-4.9402e-001 |   - final statistics: 7 variables; iteration 7; function
evaluation 10 Function value  3.2460e+002; maximum gradient component
mag
-8.0580e-005 Exit code = 1;  converg criter  1.0000e-004 Var   Value
 Gradient   |Var   Value    Gradient   |Var   Value    Gradient   1
-7.41263  1.8275e-006 |  2 -0.58080  3.4938e-005 |  3 -0.99038
1.6835e-005
  4  1.59079  8.0028e-005 |  5  0.06984  3.2566e-005 |  6  1.63706
-8.0580e-005   7  0.32457  7.7648e-005 |  Initial statistics: 8
variables;
iteration 0; function evaluation 0; phase 2 Function value
3.2460159e+002;
maxi... <truncated>
Error in glmmadmb(formula = Ges ~ Condition + (1 | Subject), data = Dat,
:
  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 1
restored working directory to I:/xxxxx
removed temp directory
C:\Users\BO~1\AppData\Local\Temp\1\RtmpiwJxjv\glmmADMB12a03acd7a0c


I tried adding another fixed effect and the model ran, but gave crazy
values for the condition levels:

Call:
glmmadmb(formula = Ges ~ Condition + History + (1 | Subject),
    data = Dat, family = "poisson", zeroInflation = TRUE)

AIC: 257.9

Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept)    -91.28   91007.00    0.00    0.999
ConditionB      89.63   91007.00    0.00    0.999
ConditionBE     88.69   91007.00    0.00    0.999
ConditionEYC    91.28   91007.00    0.00    0.999
ConditionF      90.54   91007.00    0.00    0.999
ConditionFE     91.17   91007.00    0.00    0.999
ConditionHA     89.59   91007.00    0.00    0.999
HistoryMR       -2.48       1.02   -2.43    0.015 *
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Number of observations: total=329, Subject=12
Random effect variance(s):
Group=Subject
            Variance StdDev
(Intercept)    1.837  1.355

Zero-inflation: 0.15369  (std. err.:  0.13609 )

Log-likelihood: -118.968
Warning message:
In .local(x, sigma, ...) :
  'sigma' and 'rdig' arguments are present for compatibility only:
ignored



I tried changing some controls that people had suggested online, such as

 *admb.opts=admbControl(shess=FALSE,noinit=FALSE)*

but this didn't work with my model.

Some issues with my data are that for one of the conditions, the count
of
Ges was 0 for all subjects. Similarly, for some subjects, the count for
Ges
was 0 across all conditions.

I'd be extremely grateful if you had any advice.

Jenny

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