This method provides a diagnostic plot for the validation of a funGp Gaussian process model. It displays a calibration plot based on the leave-one-out predictions of the output at the points used to train the model.

# S4 method for fgpm
plotLOO(model, ...)

Arguments

model

an object of class fgpm corresponding to the funGp model to validate.

...

additional arguments affecting the plot. The following typical graphics parameters are valid entries: xlim, ylim, xlab, ylab, main.

Value

None.

See also

* fgpm for the construction of funGp models;

* plotPreds for prediction plots;

* plotSims for simulation plots.

Examples

# generating input and output data for training
set.seed(100)
n.tr <- 25
sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
sOut <- fgp_BB3(sIn, fIn, n.tr)

# building the model
m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)

# plotting the model
plotLOO(m1)