| overdisp {sjstats} | R Documentation |
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.
overdisp(x, trafo = NULL) zero_count(x, tolerance = 0.05)
x |
Fitted GLMM ( |
trafo |
A specification of the alternative, can be numeric or a
(positive) function or |
tolerance |
The tolerance for the ratio of observed and predicted
zeros to considered as over- or underfitting zero-counts. A ratio
between 1 +/- |
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.
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.
For overdispersoion test, a p-value < .05 indicates overdispersion.
For zero_count(), a model that is underfitting zero-counts
indicates a zero-inflation in the data, i.e. it is recommended to
use negative binomial or zero-inflated models then.
Bolker B et al. (2017): GLMM FAQ.
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)