variable_selection {MultiVarSel} | R Documentation |
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.
variable_selection(Y, X, square_root_inv_hat_Sigma, nb_repli = 1000,
parallel = FALSE, nb.cores = 1)
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 |
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.
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
)