Package: RRMLRfMC 0.4.0

RRMLRfMC: 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)<doi:10.1002/sim.8923> 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.

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

RRMLRfMC_0.4.0.tar.gz
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RRMLRfMC_0.4.0.tgz(r-4.4-any)RRMLRfMC_0.4.0.tgz(r-4.3-any)
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RRMLRfMC.pdf |RRMLRfMC.html✨
RRMLRfMC/json (API)

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Exports:rrmultinomsdfun

Dependencies:nnet