Multinomial Logit Models, with or without Random Effects or Overdispersion

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.


News

2014-10-13: Simplified some namespace dependencies. Eliminated useless pseudo-R-squared statistics form getSummary.mclogit

2014-08-23: Added 'anova' methods

2014-03-10: Refactored code -- algorithms should be more transparent and robust now (hopefully!). mclogit without and with random effects can handle missing values now. Fixed predict method -- use of napredict; handles single indep-variable situation now. Fixed embarassing typo -- prior weights do work now (again?). Included AIC and BIC methods contributed by Nic Elliot [email protected]

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("mclogit")

0.8.7.3 by Martin Elff, 6 months ago


http://mclogit.elff.eu,https://github.com/melff/mclogit/


Report a bug at https://github.com/melff/mclogit/issues


Browse source code at https://github.com/cran/mclogit


Authors: Martin Elff


Documentation:   PDF Manual  


GPL-2 license


Imports memisc, methods

Depends on stats, Matrix

Suggests MASS, nnet


Imported by mztwinreg.

Suggested by WeightIt.

Enhanced by prediction, stargazer.


See at CRAN