Description Usage Arguments Value Examples
This function outputs dimensions selected by Akaike information criterion (AIC), Bayesian information criterion (BIC) and likelihood ratio testing with specified significance level for the envelope model.
1 | u.xenv(X, Y, alpha = 0.01)
|
X |
Predictors. An n by p matrix, p is the number of predictors and n is number of observations. The predictors must be continuous variables. |
Y |
Responses. An n by r matrix, r is the number of responses. The response can be univariate or multivariate and must be continuous variable. |
alpha |
Significance level for testing. The default is 0.01. |
u.aic |
Dimension of the envelope subspace selected by AIC. |
u.bic |
Dimension of the envelope subspace selected by BIC. |
u.lrt |
Dimension of the envelope subspace selected by the likelihood ratio testing procedure. |
loglik.seq |
Log likelihood for dimension from 0 to p. |
aic.seq |
AIC value for dimension from 0 to p. |
bic.seq |
BIC value for dimension from 0 to p. |
1 2 3 4 5 | data(wheatprotein)
X <- wheatprotein[, 1:6]
Y <- wheatprotein[, 7]
u <- u.xenv(X, Y)
u
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