cv {monoProc}R Documentation

Cross validation function

Description

computes the cross validation value (leave one out method) for the monotonized and the unconstraint fit

Usage

cv(fit, ...)

Arguments

fit an object of class "monoproclocfit.1d" or "monoproclocfit.2d"
... currently not in use

Details

so far, this function can only be used for objects of class "monoproc" if the originally fit came from the locfit-function. This function is currently not really computational efficient.

Value

returns a matrix where column 1 and 2 represents the values for the new fit and the fitold, respectively, under leave one out cross valdiation. The rows correspond to the observation number.

See Also

monoproc

Examples

if(require(UsingR)&&require(locfit)){
        data(fat)
        fat<-fat[-39,] ##two extreme observations
        fat<-fat[-41,] ##are deleted
        attach(fat)
        x<-as.matrix(cbind(weight, height))
        fit<-locfit.raw(x,body.fat.siri, alpha=0.3, deg=1, kern="epan")
        fitmono<-monoproc(fit,bandwidth=1,dir="xy", gridsize=30)
        nf<- layout(matrix(c(1,1,1,2,2,3,3,3,4,4), 2, 5, byrow = TRUE))
        layout.show(nf)
        plot(fit, type="persp", theta = 135, phi = 30,col="lightblue",
        cex=0.7,main="unconstraint Bodyfat estimate")
        plot(fit)
        plot(fitmono,theta = 135, phi = 30, col="lightblue",cex=0.7, 
        main="monotone Bodyfat estimate")
        plot(fitmono, type="contour")
        t<-cv(fitmono)
        #Cross Validation for the unconstraint estimator
        sum((t[,2]-body.fat.siri)^2)/250    
        #Cross Validation for the monotone estimator
        sum((t[,1]-body.fat.siri)^2)/250   
        plot(seq(1:250),rep(1,250), type="l",col=2, xlab="observation index", 
        ylab="Ratio of CV-unconstraint over CV-monotone")
                points((t[,2]-body.fat.siri)^2/(t[,1]-body.fat.siri)^2)
        }

[Package monoProc version 1.0-4 Index]