[R-sig-ME] Fwd: model check for negative binomial model

[Alessandra Bielli]

Dear list

I am fitting a poisson model to estimate the effect of a treatment on
emergence success of hatchlings. To estimate emergence success, I use
number of emerged and an offset(log(total clutch).

However, overdispersion was detected:

> overdisp_fun(m.emerged) #overdispersion detected

      chisq       ratio         rdf           p
3490.300836    5.684529  614.000000    0.000000

Therefore, I switched to a negative binomial. I know overdispersion is not
relevant for nb models, but the model plots don't look too good. I also
tried to fit a poisson model with OLRE, but still the  plots don't look
good.
How do I know if my model is good enough, and what can I do to improve it?

> summary(m.emerged)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
 Family: Negative Binomial(7.604)  ( log )
Formula: Hatched ~ Relocation..Y.N. + SP + offset(log(Total_Clutch)) + (1
|Beach_ID) + (1 | Year)
   Data: main

     AIC      BIC   logLik deviance df.resid
  6015.6   6042.2  -3001.8   6003.6      614

Scaled residuals:
    Min      1Q  Median      3Q     Max
-2.6427 -0.3790  0.1790  0.5242  1.6583

Random effects:
 Groups   Name        Variance Std.Dev.
 Beach_ID (Intercept) 0.004438 0.06662
 Year     (Intercept) 0.001640 0.04050
Number of obs: 620, groups:  Beach_ID, 8; Year, 5

Fixed effects:
                  Estimate Std. Error z value Pr(>|z|)
(Intercept)       -0.29915    0.04055  -7.377 1.62e-13 ***
Relocation..Y.N.Y -0.16402    0.05052  -3.247  0.00117 **
SPL               -0.08311    0.04365  -1.904  0.05689 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) R..Y.N
Rlct..Y.N.Y -0.114
SPL         -0.497 -0.054


Thanks for your help,

Alessandra

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