whitening_choice: This function helps to choose the best whitening strategy...

In MultiVarSel: Variable Selection in a Multivariate Linear Model

Description

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

Usage

whitening_choice(residuals, typeDeps = "AR1", pAR = 1, qMA = 0,
  threshold = 0.05)

Arguments

residuals	the residuals matrix obtained by fitting a linear model to each column of the response matrix as if they were independent
typeDeps	character in c("AR1", "ARMA", "nonparam", "no_whitening") defining which dependence structure to use to whiten the residuals.
pAR	numerical, only use if typeDep = "ARMA", the parameter p for the ARMA(p, q) process
qMA	numerical, only use if typeDep = "ARMA", the parameter q for the ARMA(p, q) process
threshold	significance level of the test

Value

It provides a table giving the p-values for the different whitening tests applied to the residuals multiplied on the right by the inverse of the square root of the estimated covariance matrix. If the p-value is small (in general smaller than 0.05) it means that the hypothesis that each row of the residuals "whitened" matrix is a white noise, is rejected.

Examples

data(copals_camera)
Y <- scale(Y[, 1:100])
X <- model.matrix(~ group + 0)
residuals <- lm(as.matrix(Y) ~ X - 1)$residuals
whitening_choice(residuals, c("AR1", "nonparam", "ARMA", "no_whitening"),
  pAR = 1, qMA = 1 )

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