model check for negative binomial model

summary(m.emerged)

  Short answer: if emergence success gets much above 50%, then the
approximation you're making (Poisson + offset for binomial, or NB +
offset for negative binomial) doesn't work well.  You might try a
beta-binomial (with glmmTMB) or a binomial + an observation-level random
effect.

  (On the other hand, your intercept is -0.3, which corresponds to a
baseline emergence of 0.42 - not *very* high (but some beaches and years
will be well above that ...)

  Beyond that, are there any obvious patterns of mis-fit in the
predicted values ... ?

On 2020-02-11 8:09 p.m., Alessandra Bielli wrote:

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