plot-fgpm-method: Plot method for the class '"fgpm"'

In funGp: Gaussian Process Models for Scalar and Functional Inputs

Plot method for the class "fgpm"

Description

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.

Usage

## S4 method for signature '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)

# 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")

funGp documentation built on May 29, 2024, 8 a.m.