Renvlp (version 2.7)

u.pred2.env: Select the dimension of the constructed partial envelope for prediction based on envelope model

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.pred2.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

# NOT RUN {
data(fiberpaper)
X <- fiberpaper[, 5:7]
Y <- fiberpaper[, 1:4]

u <- u.pred2.env(X, Y, X[10, ])
u
# }