[R-sig-ME] Dispersion parameter in glmmadmb and model selection
paul.johnson at glasgow.ac.uk
Tue Mar 10 16:25:45 CET 2015
In answer to the first question, the dispersion parameter, alpha, is inversely proportional to the amount of additional variance due to overdispersion...
Y ~ Poisson(lambda)
Var(Y) = lambda
Y ~ NB(lambda, alpha)
Var(Y) = lambda + lambda^2 / alpha
...so your error distribution appears not to be very overdispersed. In fact, if probably isn’t overdispersed at all, as I think glmmadmb puts an upper limit on the alpha estimate of exp(6) = 403.43, presumably to prevent it wondering off towards infinity when there is no evidence of overdispersion. This would explain why you get the same estimate from different (non-overdispersed) data sets. Try simulating some Poisson data and fitting an NB model (see code below).
I guess for low lambda, e.g. around 10, lambda^2 / 403 will be reasonable approximation of zero addition variance, but not for higher lambda, e.g. > 100. Not sure how glmmadmb copes with that, or if it’s possible to raise this ceiling.
> y <- rpois(100, 5)
> dummy.group <- factor(rep(1:10, each = 10))
> fit <- glmmadmb(y ~ (1 | dummy.group), family = "nbinom2")
glmmadmb(formula = y ~ (1 | dummy.group), family = "nbinom2")
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.520 0.047 32.3 <2e-16 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Number of observations: total=100, dummy.group=10
Random effect variance(s):
(Intercept) 2.696e-08 0.0001642
Negative binomial dispersion parameter: 403.43 (std. err.: 0.72454)
On 10 Mar 2015, at 01:39, Zahwa Al Ayyash (Student) <zsa11 at mail.aub.edu> wrote:
> Dear list,
> 1) I am fitting a negative binomial and zero-inflated negative binomial models using glmmadmb. I am getting a very high dispersion parameter:
> Negative binomial dispersion parameter: 403.43 (std. err.: 0.39244)
> I am aware that my data might be over-dispersed, but what does the very high value indicate? Could there be an error in estimation?
> Also, surprisingly, I am using two different data sets to estimate the neg. bin. models, and I am getting the same value (403.43) but with different std. errors. Any clues?
> 2) My second question is rather general; What could be the best ways to compare glmmadmb models and select the best amongst Poisson, Neg. Bin, Zero-inflated Poisson, Zero-inflated Neg. Bin., Hurdle Poisson and Hurdle Neg. Bin?
> PS: My models employ a random effect to capture correlation among individuals (IDs).
> Many thanks to your help,
> Zahwa Al-Ayyash
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