u.penv {Renvlp}

Select the dimension of penv

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 partial envelope model.

Usage

u.penv(X1, X2, Y, alpha = 0.01)

Arguments

X1

Predictors of main interest. An n by p1 matrix, n is the number of observations, and p1 is the number of main predictors. The predictors can be univariate or multivariate, discrete or continuous.

X2

Covariates, or predictors not of main interest. An n by p2 matrix, p2 is the number of covariates.

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

u.aic

Dimension of the partial envelope subspace selected by AIC.

u.bic

Dimension of the partial envelope subspace selected by BIC.

u.lrt

Dimension of the 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)
X1 <- fiberpaper[, 7]
X2 <- fiberpaper[, 5:6]
Y <- fiberpaper[, 1:4]

u <- u.penv(X1, X2, Y)
u