Reduced-Rank Multinomial Logistic Regression for Markov Chains
Description
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.