Renvlp (version 3.4.5)

u.logit.env: Select the dimension of logit.env

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 envelope model in logistic regression.

Usage

u.logit.env(X, Y, alpha = 0.01)

Value

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.

Arguments

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

Response. An n by 1 matrix. The univariate response must be binary.

alpha

Significance level for testing. The default is 0.01.

Examples

data(horseshoecrab)
X1 <- as.numeric(horseshoecrab[ , 1] == 2)
X2 <- as.numeric(horseshoecrab[ , 1] == 3)
X3 <- as.numeric(horseshoecrab[ , 1] == 4)
X4 <- as.numeric(horseshoecrab[ , 2] == 2)
X5 <- as.numeric(horseshoecrab[ , 2] == 3)
X6 <- horseshoecrab[ , 3]
X7 <- horseshoecrab[ , 5]
X <- cbind(X1, X2, X3, X4, X5, X6, X7)
Y <- as.numeric(ifelse(horseshoecrab[ , 4] > 0, 1, 0))

if (FALSE) u <- u.logit.env(X, Y)
if (FALSE) u