Renvlp (version 2.7)

cv.logit.env: Cross validation for logit.env

Description

Compute the prediction error for the envelope estimator in logistic regression using m-fold cross validation.

Usage

cv.logit.env(X, Y, u, m, nperm)

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.

u

Dimension of the envelope. An integer between 0 and p.

m

A positive integer that is used to indicate m-fold cross validation.

nperm

A positive integer indicating number of permutations of the observations, m-fold cross validation is run on each permutation.

Value

The output is a real nonnegative number.

cvPE

The prediction error estimated by m-fold cross validation.

Details

This function computes prediction errors using m-fold cross validation. For a fixed dimension u, the data is randomly partitioned into m parts, each part is in turn used for testing for the prediction performance while the rest m-1 parts are used for training. This process is repeated for nperm times, and average prediction error is reported.

Examples

# NOT RUN {
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))

m <- 5
nperm <- 50
# }
# NOT RUN {
cvPE <- cv.logit.env(X, Y, 1, m, nperm)
# }
# NOT RUN {
cvPE
# }
# NOT RUN {
# }