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.
| Version: |
0.4.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
nnet |
| Suggests: |
rmarkdown, knitr |
| Published: |
2021-06-07 |
| Author: |
Pei Wang [aut, cre],
Richard Kryscio [aut] |
| Maintainer: |
Pei Wang <wangp33 at miamioh.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
RRMLRfMC results |
Documentation:
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