[R-sig-ME] Fwd: model check for negative binomial model
Alessandra Bielli
b|e|||@@|e@@@ndr@ @end|ng |rom gm@||@com
Wed Feb 12 02:09:28 CET 2020
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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