sjstats (version 0.15.0)

overdisp: Check overdispersion of GL(M)M's

Description

overdisp() checks generalized linear (mixed) models for overdispersion, while zero_count() checks whether models from poisson-families are over- or underfitting zero-counts in the outcome.

Usage

overdisp(x, trafo = NULL)
zero_count(x, tolerance = 0.05)

Arguments

x

Fitted GLMM (merMod-class) or glm model.

trafo

A specification of the alternative, can be numeric or a (positive) function or NULL (the default). See 'Details' in dispersiontest in package AER. Does not apply to merMod objects.

tolerance

The tolerance for the ratio of observed and predicted zeros to considered as over- or underfitting zero-counts. A ratio between 1 +/- tolerance are considered as OK, while a ratio beyond or below this treshold would indicate over- or underfitting.

Value

For overdisp(), information on the overdispersion test; for zero_count(), the amount of predicted and observed zeros in the outcome, as well as the ratio between these two values.

Details

For merMod- and glmmTMB-objects, overdisp() is based on the code in the GLMM FAQ, 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 with glmmTMB).

For glm's, overdisp() simply wraps the dispersiontest from the AER-package.

References

Bolker B et al. (2017): GLMM FAQ.

Examples

# NOT RUN {
library(sjmisc)
data(efc)

# response has many zero-counts, poisson models
# might be overdispersed
barplot(table(efc$tot_sc_e))

fit <- glm(tot_sc_e ~ neg_c_7 + e42dep + c160age,
           data = efc, family = poisson)
overdisp(fit)
zero_count(fit)

library(lme4)
efc$e15relat <- to_factor(efc$e15relat)
fit <- glmer(tot_sc_e ~ neg_c_7 + e42dep + c160age + (1 | e15relat),
             data = efc, family = poisson)
overdisp(fit)
zero_count(fit)


# }