Learn R Programming

mclogit (version 0.9.6)

Multinomial Logit Models, with or without Random Effects or Overdispersion

Description

Provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small.

Copy Link

Version

Install

install.packages('mclogit')

Monthly Downloads

4,227

Version

0.9.6

License

GPL-2

Issues

Pull Requests

Stars

Forks

Maintainer

Last Published

October 27th, 2022

Functions in mclogit (0.9.6)

Transport

Choice of Means of Transport
mclogit.control

Control Parameters for the Fitting Process
dispersion

Overdispersion in Multinomial Logit Models
getSummary-methods

`getSummary` Methods
mclogit.fit

Internal functions used for model fit.
predict

Predicting responses or linear parts of the baseline-category and conditional logit models
simulate.mclogit

Simulating responses from baseline-category and conditional logit models
mblogit

Baseline-Category Logit Models for Categorical and Multinomial Responses
electors

Class, Party Position, and Electoral Choice
mclogit

Conditional Logit Models and Mixed Conditional Logit Models