Check overdispersion of GL(M)M'sSource:
check_overdispersion() checks generalized linear (mixed)
models for overdispersion.
Fitted model of class
Currently not used.
A list with results from the overdispersion test, like chi-squared statistics, p-value or dispersion ratio.
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".
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
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.
library(glmmTMB) data(Salamanders) m <- glm(count ~ spp + mined, family = poisson, data = Salamanders) check_overdispersion(m) #> # Overdispersion test #> #> dispersion ratio = 2.946 #> Pearson's Chi-Squared = 1873.710 #> p-value = < 0.001 #> #> Overdispersion detected. m <- glmmTMB( count ~ mined + spp + (1 | site), family = poisson, data = Salamanders ) check_overdispersion(m) #> # Overdispersion test #> #> dispersion ratio = 2.324 #> Pearson's Chi-Squared = 1475.875 #> p-value = < 0.001 #> #> Overdispersion detected.