cgcv: Estimate Misclassification Rate Using d-fold Cross-Validation...

In gencve: General Cross Validation Engine

Description

This is a general purpose function to estimate the misclassification rate for a specified classifier.

Usage

cgcv(X, y, yh = yh_NN, MaxIter = 1000, d = ceiling(length(y)/10), NCores = 1,
libs = character(0), seed = "default", ...)

Arguments

X	inputs
y	output factor
yh	function with arguments dfTrain and dfTest that produces the missclassification rate for test data
MaxIter	Number of iterations of the CV procedure
d	Number of observations for the hold-out sample
NCores	Default is 1 which does not use the parallel package. Otherwise, you can set to the number of cores available. If unsure, just experiment!
libs	Required libraries needed for the predictor.
seed	Default is to use R's default which is based on the current time. Otherwise set to an integer value. See Details.
...	Additional arguments that are passed to yh.

Value

cross-validated mis-classifcation rate

Author(s)

A. I. McLeod

Examples

Xy <- rmix(200) #training data
X <- as.matrix.data.frame(Xy[,1:2])
y <- Xy[,3]
cgcv(X, y, MaxIter=50)

gencve documentation built on May 2, 2019, 6:08 a.m.