MultiVarSel (version 1.1.3)

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.

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

# NOT RUN {
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
)
# }