penaltyPolyMARS {DiceEval}

Choice of the penalty parameter for a PolyMARS model

Description

This function fits a PolyMARS model for different values of the penalty parameter and compute criteria.

Usage

penaltyPolyMARS(X,Y,test=NULL,graphic=FALSE,K=10,
		Penalty=seq(0,5,by=0.2))

Arguments

X

a data.frame containing the design of experiments

Y

a vector containing the response variable

test

a data.frame containing the design and the response of a test set when available, the prediction criteria will be computed for the test data (default corresponds to no test set)

graphic

if TRUE the values of the criteria are represented

K

the number of folds for cross-validation (by default, K=10)

Penalty

a vector containing the values of the penalty parameter

Value

A data frame containing

a

the values of the penalty parameter

R2

the R2 criterion evaluted on the learning set

m

the size of the selected model

If a test set is available the last row is

R2test

the R2 criterion evaluated on the test set

If no test set is available, criteria computed by K-corss-validation are provided:

Q2

the Q2 evaluated by cross-validation (by default, K=10)

RMSE CV

RMSE computed by cross-validation

Note that the penalty parameter could be chosen by minimizing the value of the RMSE by cross-validation.

Author(s)

D. Dupuy

See Also

modelFit, R2 and crossValidation

Examples

data(dataIRSN5D)
X	<- dataIRSN5D[,1:5]
Y	<- dataIRSN5D[,6]
data(testIRSN5D)
library(polspline)
Crit	<- penaltyPolyMARS(X,Y,test=testIRSN5D[,-7],graphic=TRUE)