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

[Alessandra Bielli]

Dear Ben

Thanks for your quick response.

Yes, emergence success is usually between 60 and 80% or higher.
I am not sure how to use a binomial, if my data are counts?

Can you explain why the approximation doesn't work well if success gets
much above 50%? Does it make sense, then, to have "unhatched" as dependent
variable, so that I predict mortality (usually below 50%) using a nb with
offset(log(total clutch)) ?

> summary(m.emerged)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
 Family: Negative Binomial(2.2104)  ( log )
Formula: Unhatched ~ Relocation..Y.N. + SP + offset(log(Total_Clutch)) +
   (1 | Beach_ID) + (1 | Week)
   Data: main

     AIC      BIC   logLik deviance df.resid
  5439.4   5466.0  -2713.7   5427.4      614

Scaled residuals:
    Min      1Q  Median      3Q     Max
-1.4383 -0.7242 -0.2287  0.4866  4.0531

Random effects:
 Groups   Name        Variance Std.Dev.
 Week     (Intercept) 0.003092 0.0556
 Beach_ID (Intercept) 0.025894 0.1609
Number of obs: 620, groups:  Week, 31; Beach_ID, 8

Fixed effects:
                  Estimate Std. Error z value Pr(>|z|)
(Intercept)       -1.38864    0.08227 -16.879  < 2e-16 ***
Relocation..Y.N.Y  0.32105    0.09152   3.508 0.000452 ***
SPL                0.22218    0.08793   2.527 0.011508 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) R..Y.N
Rlct..Y.N.Y -0.143
SPL         -0.540 -0.038

Thanks,

Alessandra

On Tue, Feb 11, 2020 at 7:29 PM Ben Bolker <bbolker using gmail.com> wrote:

>
>   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
> >
> >
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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