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)