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

View source: R/cv.logit.env.R

cv.logit.envR Documentation

Cross validation for logit.env


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


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



Predictors. An n by p matrix, p is the number of predictors and n is number of observations. The predictors must be continuous variables.


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


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


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


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


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.


The output is a real nonnegative number.


The prediction error estimated by m-fold cross validation.


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

Renvlp documentation built on Oct. 11, 2023, 1:06 a.m.

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