Greetings,

I have a general and somewhat basic question about how glmer.nb works in
terms of what scale the parameter estimates are on, and predicting from
glmer.nb. My understanding from reading through some of the help files in
the glmm FAQ wiki page is that in order to predict from the glmer models,
predicted values need to be back transformed to place them on the same
scale as the response variable (accomplished by using type="response" in
the predict command).  Otherwise, the predicted values would be on the log
scale in my case (using glmer.nb).  Am I interpreting this correctly and/or
correct on what scale the predicted values would be?  I am getting rather
nonsensical values (at least in my estimation) from the predict command
when I use the type="response". The raw data for the response variable
ranges from 0-10, and I am predicting over a range values that we actually
observed for the covariates but the predicted values sometimes exceed >
10^27 larger than the actual data.  Any thoughts as to what I'm doing wrong
or my ignorance of what is actually going on would be appreciated.

I understand the etiquette is to give a reproducible example but I wasn't
sure if it was necessary for the question, and I honestly wasn't sure the
best way to accomplish that and was having difficulty replicating the issue
with simulated data. Below is a sample of the data, and the output of the
model summary if that is helpful. Let me know if other information is
needed.


     Site Year      AMAN      AMOP  Structure
1   0.002 2012 3.7500000 0.0000000      13.0
2   0.002 2013 1.9666667 0.0000000      13.0
3  0.002A 2012 0.3333333 2.7777778       0.0
4  0.002A 2013 0.0000000 0.0000000       0.0
5   0.003 2012 0.0000000 0.2500000     100.0
6   0.003 2013 0.0000000 0.1904762     100.0
7      10 2012 0.0000000 0.0000000      95.0
8      10 2013 0.2333333 0.0000000      95.0
9     103 2012 0.0000000 4.6666667      32.5


10    103 2013 0.0000000 1.9166667      32.5



> summary(aman.d5)Generalized linear mixed model fit by maximum likelihood ['glmerMod']
 Family: Negative Binomial(0.6346) ( log )
Formula: AMAN ~ AMOP * Structure + (1 | Year)
   Data: ..2

      AIC       BIC    logLik  deviance
 917.1346  940.7914 -452.5673  905.1346

Random effects:
 Groups   Name        Variance  Std.Dev.
 Year     (Intercept) 2.953e-10 1.718e-05
 Residual             9.836e-01 9.918e-01
Number of obs: 381, groups: Year, 2

Fixed effects:
                Estimate Std. Error t value Pr(>|z|)
(Intercept)    -0.124857   0.130868  -0.954 0.340047
AMOP           -0.558918   0.208893  -2.676 0.007459 **
Structure      -0.004156   0.003175  -1.309 0.190474
AMOP:Structure  0.013938   0.004152   3.357 0.000788 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) AMOP   Strctr
AMOP        -0.331
Structure   -0.709  0.243
AMOP:Strctr  0.291 -0.879 -0.330



-- 
Tom Anderson

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