Plot method for the class "fgpm"

Source: R/7_plottingFunctionsStandard.R

This method provides a diagnostic plot for the validation of regression models. 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
plot(x, y = NULL, ...)

Arguments

x

A fgpm object.

y

Not used.

...

Graphical parameters. These currently include

  • xlim, ylim to set the limits of the axes.

  • pch, pt.col, pt.bg, pt.cex to set the symbol used for the points and the related properties.

  • line to set the color used for the line.

  • xlab, ylab, main to set the labels of the axes and the main title. See Examples.

Details

Plot the Leave-One-Out (LOO) calibration.

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)
#> ** Presampling...
#> ** Optimising hyperparameters...
#> final  value 2.841058 
#> converged
#> The function value is the negated log-likelihood
#> ** Hyperparameters done!

# plotting the model
plot(m1)

# change some graphical parameters if wanted
plot(m1, line = "SpringGreen3" ,
     pch = 21, pt.col = "orangered", pt.bg = "gold",
     main = "LOO cross-validation")