mclogit: dispersion – R documentation

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dispersion

Overdispersion in Multinomial Logit Models

Description

The function dispersion() extracts the dispersion parameter from a multinomial logit model or computes a dispersion parameter estimate based on a given method. This dispersion parameter can be attached to a model using update(). It can also given as an argument to summary().

Usage

dispersion(object,method, ...)
## S3 method for class 'mclogit'
dispersion(object,method=NULL, ...)

Arguments

`object`	an object that inherits class `"mclogit"`. When passed to `dispersion()`, it should be the result of a call of `mclogit()` of `mblogit()`, without random effects.
`method`	a character string, either `"Afroz"`, `"Fletcher"`, `"Pearson"`, or `"Deviance"`, that specifies the estimator of the dispersion; or `NULL`, in which case the default estimator, `"Afroz"` is used. The estimators are discussed in Afroz et al. (2019).
`...`	other arguments, ignored or passed to other methods.

References

Afroz, Farzana, Matt Parry, and David Fletcher. (2020). "Estimating Overdispersion in Sparse Multinomial Data." Biometrics 76(3): 834-842. doi: 10.1111/biom.13194.

Examples

library(MASS) # For 'housing' data

# Note that with a factor response and frequency weighted data,
# Overdispersion will be overestimated:
house.mblogit <- mblogit(Sat ~ Infl + Type + Cont, weights = Freq,
                         data = housing)

dispersion(house.mblogit,method="Afroz")
dispersion(house.mblogit,method="Deviance")

summary(house.mblogit)

phi.Afroz <- dispersion(house.mblogit,method="Afroz")
summary(house.mblogit, dispersion=phi.Afroz)

summary(update(house.mblogit, dispersion="Afroz"))

# In order to be able to estimate overdispersion accurately,
# data like the above (which usually comes from applying
# 'as.data.frame' to a contingency table) the model has to be
# fitted with the optional argument 'from.table=TRUE':
house.mblogit.corrected <- mblogit(Sat ~ Infl + Type + Cont, weights = Freq,
                                   data = housing, from.table=TRUE,
                                   dispersion="Afroz")
# Now the estimated dispersion parameter is no longer larger than 20,
# but just bit over 1.0.
summary(house.mblogit.corrected)

mclogit

Multinomial Logit Models, with or without Random Effects or Overdispersion

v0.8.7.3

GPL-2

Authors

Martin Elff

Initial release

2021-03-19