# testCrossValidation: Test the robustess of the cross-validation procedure In DiceEval: Construction and Evaluation of Metamodels

## Description

This function calculates the estimated K-fold cross-validation for different values of `K`.

## Usage

 `1` ```testCrossValidation(model,Kfold=c(2,5,10,20,30,40,dim(model\$data\$X)[1]),N=10) ```

## Arguments

 `model` a fitted model from `modelFit` `Kfold` a vector containing the values to test (default corresponds to 2,5,10,20,30,40 and the number of observations for leave-one-out procedure) `N` an integer given the number of times the K-fold cross-validation is performed for each value of K

## Value

a matrix of all the values obtained by K-fold cross-validation

## Note

For each value of K, the cross-validation procedure is repeated `N` times in order to get an idea of the dispersion of the `Q2` criterion and of the `RMSE` by K-fold cross-validation.

## Author(s)

D. Dupuy

`crossValidation`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```## Not run: rm(list=ls()) # A 2D example Branin <- function(x1,x2) { x1 <- x1*15-5 x2 <- x2*15 (x2 - 5/(4*pi^2)*(x1^2) + 5/pi*x1 - 6)^2 + 10*(1 - 1/(8*pi))*cos(x1) + 10 } # a 2D uniform design and the value of the response at these points n <- 50 X <- matrix(runif(n*2),ncol=2,nrow=n) Y <- Branin(X[,1],X[,2]) mod <- modelFit(X,Y,type="Linear",formula=formulaLm(X,Y)) out <- testCrossValidation(mod,N=20) ## End(Not run) ```