cv.mogavs: k-Fold Crossvalidation for a mogavs model

In mogavs: Multiobjective Genetic Algorithm for Variable Selection in Regression

Description

Performs k-fold CV for a model of class mogavs via the cvTools package.

Usage

cv.mogavs(mogavs, nvar, data, y_ind, K = 10, R = 1, order = FALSE)

Arguments

mogavs	A model of class mogavs.
nvar	The number of variables for which you want to run k-fold CV.
data	Used data set.
y_ind	The column number for the y-variable in the dataset.
K	Number of folds in the cross-validation, default K=10.
R	Number of repeats for the CV, default R=1.
order	Logical, whether the result should be sorted by the column CVerror.

Details

Perform k-fold cross-validation for all the linear models with nvar number of variables, which have been tried during the course of the genetic algorithm.

Value

A data frame with the following columns:

archInd	The row index of the linear model in the archiveSet of the mogavs model.
formula	The formula of the linear model as a character string.
CVerror	The root mean square error of the model.
CVse	The standard error of the model across the R runs of the cross-validation. NA if R=1.

Author(s)

Tommi Pajala <[e-mail]>

See Also

mogavsToLinear

Examples

data(sampleData)
mod<-mogavs(y~.,data=sampleData,maxGenerations=20)
cv.mogavs(mod,nvar=3,data=sampleData,y_ind=1,K=10,R=1,order=FALSE)

mogavs documentation built on May 2, 2019, 1 a.m.