u.stenv {Renvlp}

Select the dimension of stenv

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 simultaneous envelope model.

Usage

u.stenv(X, Y, 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.

alpha

Significance level for testing. The default is 0.01.

Value

d

Rank of beta selected by the Bura-Cook estimator.

u.aic

Dimension of the simultaneous envelope subspace selected by AIC.

u.bic

Dimension of the simultaneous envelope subspace selected by BIC.

u.lrt

Dimension of the simultaneous envelope subspace selected by the likelihood ratio testing procedure.

loglik.mat

Log likelihood for dimension from (1, 1) to (p, r).

aic.mat

AIC value for dimension from (1, 1) to (p, r).

bic.mat

BIC value for dimension from (1, 1) to (p, r).

Examples

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

u <- u.stenv(X, Y)
u