u.predict2.env: Select the dimension of the constructed partial envelope for...

In therimalaya/envelope: Computing Envelope Estimators in Multivariate Linear Regression

Description

This function outputs dimensions selected by Akaike information criterion (AIC), Bayesian information criterion (BIC) and likelihood ratio testing with specified significance level for the constructed partial envelope model.

Usage

u.predict2.env(X, Y, Xnew, alpha = 0.01)

Arguments

X	Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.
Y	Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.
Xnew	The value of X with which to estimate or predict Y. A p dimensional vector.
alpha	Significance level for testing. The default is 0.01.

Value

u.aic	Dimension of the constructed partial envelope subspace selected by AIC.
u.bic	Dimension of the constructed partial envelope subspace selected by BIC.
u.lrt	Dimension of the constructed partial envelope subspace selected by the likelihood ratio testing procedure.
loglik.seq	Log likelihood for dimension from 0 to r.
aic.seq	AIC value for dimension from 0 to r.
bic.seq	BIC value for dimension from 0 to r.

Examples

data(fiberpaper)
X <- fiberpaper[, 5:7]
Y <- fiberpaper[, 1:4]

u <- u.predict2.env(X, Y, X[10, ])
u

therimalaya/envelope documentation built on May 29, 2019, 1:38 p.m.