For a Gaussian process, calculates cross-validated predictions and the variance of cross-validated predictions for all points of the design. These are cross-validated in the sense that when predicting output at design point x, all observations at x are removed from the collection of observed outputs
Usage
CV(gp, predictObserved = TRUE, verbose = FALSE)
Arguments
gp
an object of type gp
predictObserved
if TRUE (the default), the variance of a predicted observation is calculated (and a nugget term is added); if FALSE, the variance of a predicted mean response is calculated (and a nugget term is NOT added)
verbose
if TRUE, status is displayed following the cross-validation of each observation
Value
a matrix where the first column corresponds to the cross-validated predictions and the second column corresponds to the variance of the cross-validated predictions
# NOT RUN {## fit a single Gaussian process ##x = -5:5; y1 = sin(x) + rnorm(length(x),sd=.1)
fit1 = mlegp(x, y1)
cv = CV(fit1) ## note that cv is the same as fit1$cv# }