CrossValidation: Cross-Validation function
In victorvicpal/MGSR: Multivariate Gaussian Subspatial Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

The cross-validation method is based on a common resampling. Each sample value Z(uα) is removed in turn from the data set and a value Z(u[α]) is estimated at that location using the N − 1 other samples.

1	CrossValidation(RES)

RES

Resulting lmc fitting.

Large datasets can last a very long time.

Object containing RMSE, pseudo-R^2, residuals and variance of residuals

Victor Vicente Palacios

Wackernagel H. Multivariate Geostatistics: An Introduction with Applications. Springer-Verlag 2003.

lmc

data("iris")
Versicolor <- iris[which(iris$Species=='versicolor'),-5]
##Data Standarization
means_vers <- apply(Versicolor,2,mean)
sd_vers <- apply(Versicolor,2,sd)
Versicolor_st <- Versicolor

for (i in 1:length(Versicolor[1,]))
{Versicolor_st[,i] <- (Versicolor[,i]-means_vers[i])/sd_vers[i]}

##PrComp
PC_train <- princomp(Versicolor_st)
## CrossVariogram Calculation
CV_vers <- crossvariogram(as.data.frame(PC_train$scores[,1:2]),as.data.frame(Versicolor_st),11)
## lmc 
RES_vers <- lmc(CV_vers,'Pow',1.6)
#CrossValidation
Cr_val <- CrossValidation(RES_vers)