[R-sig-ME] Fwd: model check for negative binomial model
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Wed Feb 12 02:29:11 CET 2020
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
>
>
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