[R-sig-ME] Fwd: model check for negative binomial model
Alessandra Bielli
b|e|||@@|e@@@ndr@ @end|ng |rom gm@||@com
Wed Feb 12 18:42:32 CET 2020
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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