Reduced-Rank Multinomial Logistic Regression for Markov Chains

Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021) in R. It combines the ideas of multinomial logistic regression in Markov chains and reduced-rank. It is very useful in a study where multi-states model is assumed and each transition among the states is controlled by a series of covariates. The key advantage is to reduce the number of parameters to be estimated. The final coefficients for all the covariates and the p-values for the interested covariates will be reported. The p-values for the whole coefficient matrix can be calculated by two bootstrap methods.


Reference manual

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0.4.0 by Pei Wang, 8 months ago

Browse source code at

Authors: Pei Wang [aut, cre] , Richard Kryscio [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports nnet

Suggests rmarkdown, knitr

See at CRAN