variable_selection: This function allows the user to select the most relevant...

In MultiVarSel: Variable Selection in a Multivariate Linear Model

Description

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.

Usage

variable_selection(Y, X, square_root_inv_hat_Sigma, nb_repli = 1000,
  parallel = FALSE, nb.cores = 1)

Arguments

Y	a response matrix
X	a matrix of covariables
square_root_inv_hat_Sigma	Estimation of the inverse of the square root of the covariance matrix of each row of the residuals matrix obtained by the whitening function.
nb_repli	numerical, number of replications in the stability selection
parallel	logical, if TRUE then a parallelized version of the code is used
nb.cores	numerical, number of cores used

Value

A data frame containing the selection frequencies of the different variables obtained by the stability selection, the corresponding level in the design matrix and the associated column of the observations matrix.

Examples

data("copals_camera")
Y <- scale(Y[, 1:50])
X <- model.matrix(~ group + 0)
residuals <- lm(as.matrix(Y) ~ X - 1)$residuals
S12_inv <- whitening(residuals, "AR1", pAR = 1, qMA = 0)
Frequencies <- variable_selection(
  Y = Y, X = X,
  square_root_inv_hat_Sigma = S12_inv,
  nb_repli = 10, nb.cores = 1, parallel = FALSE
)

MultiVarSel documentation built on May 2, 2019, 7:58 a.m.