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MultiVarSel (version 1.1.3)

Variable Selection in a Multivariate Linear Model

Description

It performs variable selection in a multivariate linear model by estimating the covariance matrix of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion. The method is described in the paper Perrot-Docks et al. (2017) .

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Version

Install

install.packages('MultiVarSel')

Monthly Downloads

133

Version

1.1.3

License

GPL (>= 2)

Maintainer

Marie Perrot-Dockc3<a8>s

Last Published

March 21st, 2019

Functions in MultiVarSel (1.1.3)

whitening_test

This function provides the p-value of an adaptation of the Portmanteau statistic to test if there is some dependence in the rows of the residuals matrix given as an argument of the function.
whitening

This function provides an estimation of the inverse of the square root of the covariance matrix of each row of the residuals matrix.
variable_selection

This function allows the user to select the most relevant variables thanks to the estimation of their selection frequencies obtained by the stability selection approach.
group

This is a qualitative variable indicating the type of tree each row of Y is.
copals_camera

Copals data
whitening_choice

This function helps to choose the best whitening strategy among the following types of dependence modellings: AR1, ARMA, non parametric and without any whitening.
Y

This is a metabolomic dataset from 30 copals samples of trees coming from Africa
MultiVarSel-package

Package
metab

This is a dataset containing the abundance of 199 metabolites from 9 seeds samples just after germination. The temperature of seed maturation vary between the different seeds.
prot

This is a dataset containing the abundance of 724 proteins from 9 seeds samples just after germination. The temperature of seed maturation vary between the different seeds.