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RRMLRfMC
View on CRAN: Click
here
Download and install RRMLRfMC package within the R console
Install from CRAN:
install.packages("RRMLRfMC")
Install from Github:
library("remotes")
install_github("cran/RRMLRfMC")
Install by package version:
library("remotes")
install_version("RRMLRfMC", "0.4.0")
Attach the package and use:
library("RRMLRfMC")
Maintained by
Pei Wang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-06-07
Latest Update: 2021-06-07
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.
How to cite:
Pei Wang (2021). RRMLRfMC: Reduced-Rank Multinomial Logistic Regression for Markov Chains. R package version 0.4.0, https://cran.r-project.org/web/packages/RRMLRfMC. Accessed 21 Dec. 2024.
Previous versions and publish date:
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Complete documentation for RRMLRfMC
Functions, R codes and Examples using
the RRMLRfMC R package
Some associated functions: Aupdate . Gupdate . cogdat . derivativeB . derivatives . expand . norm . rrmultinom . sdfun .
Some associated R codes: Aupdate.R . Gupdate.R . cogdat.R . derivativeB.R . derivatives.R . expand.R . norm.R . rrmultinom.R . sdfun.R . Full RRMLRfMC package functions and examples
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