check_overdispersion() checks generalized linear (mixed)
models for overdispersion.
Fitted model of class
Currently not used.
Overdispersion occurs when the observed variance is higher than the variance of a theoretical model. For Poisson models, variance increases with the mean and, therefore, variance usually (roughly) equals the mean value. If the variance is much higher, the data are "overdispersed".
A list with results from the overdispersion test, like chi-squared statistics, p-value or dispersion ratio.
If the dispersion ratio is close to one, a Poisson model fits well to the data. Dispersion ratios larger than one indicate overdispersion, thus a negative binomial model or similar might fit better to the data. A p-value < .05 indicates overdispersion.
For Poisson models, the overdispersion test is based on the code from Gelman and Hill (2007), page 115.
is based on the code in the
section How can I deal with overdispersion in GLMMs?. Note that this
function only returns an approximate estimate of an overdispersion
parameter, and is probably inaccurate for zero-inflated mixed models (fitted
Overdispersion can be fixed by either modeling the dispersion parameter, or by choosing a different distributional family (like Quasi-Poisson, or negative binomial, see Gelman and Hill (2007), pages 115-116).
Bolker B et al. (2017): GLMM FAQ.
Gelman, A., and Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.
Other functions to check model assumptions and and assess model quality:
library(glmmTMB) data(Salamanders) m <- glm(count ~ spp + mined, family = poisson, data = Salamanders) check_overdispersion(m) m <- glmmTMB( count ~ mined + spp + (1 | site), family = poisson, data = Salamanders ) check_overdispersion(m)
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