QAIC.RdCalculate a modification of Akaike's Information Criterion for overdispersed count data (or its version corrected for small sample, “quasi-AIC\(_{c}\)”), for one or several fitted model objects.
QAIC(object, ..., chat, k = 2, REML = NULL)
QAICc(object, ..., chat, k = 2, REML = NULL)a fitted model object.
optionally, more fitted model objects.
\(\hat{c}\), the variance inflation factor.
the ‘penalty’ per parameter.
optional logical value, passed to the logLik method
indicating whether the restricted log-likelihood or log-likelihood should be
used. The default is to use the method used for model estimation.
If only one object is provided, returns a numeric value with the
corresponding QAIC or QAIC\(_{c}\); otherwise returns a
data.frame with rows corresponding to the objects.
\(\hat{c}\) is the dispersion parameter estimated from the global model, and can be calculated by dividing model's deviance by the number of residual degrees of freedom.
In calculation of QAIC, the number of model parameters is increased by 1 to account for estimating the overdispersion parameter. Without overdispersion, \(\hat{c} = 1\) and QAIC is equal to AIC.
Note that glm does not compute maximum-likelihood estimates in models
within the quasi- family. In case it is justified, it can be worked
around by ‘borrowing’ the aic element from the corresponding
‘non-quasi’ family (see ‘Example’).
Consider using negative binomial family with overdispersed count data.
AICc, quasi family used for models with
over-dispersion.
Tests for overdispersion in GLM[M]: check_overdispersion.