Mixed Conditional Logit Models

Specification and estimation of conditional logit models of binary responses and multinomial counts is provided, with or without alternative- specific random effects. The current implementation of the estimator for random effects variances uses a Laplace approximation (or PQL) approach and thus should be used only if groups sizes are large.


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 nic_elliot@yahoo.co.uk

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")

0.5.1 by Martin Elff, 2 months ago


http://www.elff.eu/software/mclogit/,http://github.com/melff/mclogit/


Report a bug at http://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


Imported by mztwinreg.

Enhanced by prediction, stargazer.


See at CRAN