CVcluster: Cross-validation estimate of predictive accuracy for...

In gamclass: Functions and Data for a Course on Modern Regression and Classification

Cross-validation estimate of predictive accuracy for clustered data

Description

This function adapts cross-validation to work with clustered categorical outcome data. For example, there may be multiple observations on individuals (clusters). It requires a fitting function that accepts a model formula.

Usage

CVcluster(formula, id, data, na.action=na.omit, nfold = 15, FUN = MASS::lda,
              predictFUN=function(x, newdata, ...)predict(x, newdata, ...)$class,
              printit = TRUE, cvparts = NULL, seed = 29)

Arguments

formula	Model formula
id	numeric, identifies clusters
data	data frame that supplies the data
na.action	na.fail (default) or na.omit
nfold	Number of cross-validation folds
FUN	function that fits the model
predictFUN	function that gives predicted values
printit	Should summary information be printed?
cvparts	Use, if required, to specify the precise folds used for the cross-validation. The comparison between different models will be more accurate if the same folds are used.
seed	Set seed, if required, so that results are exactly reproducible

Value

class	Predicted values from cross-validation
CVaccuracy	Cross-validation estimate of accuracy
confusion	Confusion matrix

Author(s)

John Maindonald

References

https://maths-people.anu.edu.au/~johnm/nzsr/taws.html

Examples

if(requireNamespace('mlbench')&requireNamespace('MASS')){
data('Vowel',package='mlbench')
acc <- CVcluster(formula=Class ~., id = V1, data = Vowel, nfold = 15, FUN = MASS::lda,
              predictFUN=function(x, newdata, ...)predict(x, newdata, ...)$class,
                     printit = TRUE, cvparts = NULL, seed = 29)
}

gamclass documentation built on Aug. 21, 2023, 5:07 p.m.