plot.gekm: Plot Method for a gekm Object

In gek: Gradient-Enhanced Kriging

Plot Method for a gekm Object

Description

Visualization of the leave-one-out cross-validation results of a gekm model.

Usage

## S3 method for class 'gekm'
plot(x, y, main = "Leave-One-Out", ylim = NULL, panel.first = abline(0, 1),
	add = FALSE, reestim = TRUE, scale = FALSE, df = NULL, add.interval = FALSE, 
	level = 0.95, args.arrows = NULL, ...)

Arguments

x	an object of class "gekm".
y	not used.
main	main title for the plot.
ylim	limits for the y-axis.
panel.first	an expression to be evaluated before the actual plot. Default is abline(0, 1).
add	logical. Should results be added to an already existing plot? Default is FALSE.
reestim	logical. Should the regression coefficients be re-estimated? Default is TRUE, see loo.gekm for details.
scale	logical. Should the estimated process variance be scaled? Default is FALSE, see sigma.gekm for details.
df	degrees of freedom for the t distribution. Default is NULL, see predict.gekm for details.
add.interval	logical. Should confidence intervals be added? Default is FALSE.
level	confidence level for calculating confidence intervals. Default is 0.95.
args.arrows	a list with further arguments to be passed to arrows. Only used if add.interval = TRUE.
...	further arguments to be passed to plot.default or points.

Details

For further details on the arguments scale see

Value

Returns the predicted values of the leave-one-out cross-validation invisibly. If add.interval = TRUE, the lower and upper bounds of the confidence intervals are also returned.

Author(s)

Carmen van Meegen

References

Bachoc, F. (2013). Cross Validation and Maximum Likelihood Estimations of Hyper-parameters of Gaussian Processes with Model Misspecification. Computational Statistics and Data Analysis, 66:55–69. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2013.03.016")}.

Dubrule, O. (1983). Cross Validation of Kriging in a Unique Neighborhood. Mathematical Geology, 15:687–699. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF01033232")}.

Martin, J. D. and Simpson, T. W. (2005). Use of Kriging Models to Approximate Deterministic Computer Models. AIAA Journal, 43(4):853–863. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2514/1.8650")}.

See Also

gekm for fitting a (gradient-enhanced) Kriging model.

loo for leave-one-out cross-validation.

branin for the Branin-Hoo function.

arrows for drawing arrows.

Examples

## 2-dimensional example: Branin-Hoo function

# Generate a grid for training
n <- 4
x1 <- seq(-5, 10, length = n)
x2 <- seq(0, 15, length = n)
x <- expand.grid(x1 = x1, x2 = x2)
y <- branin(x)
dy <- braninGrad(x)
dat <- data.frame(x, y)
deri <- data.frame(dy)

# Fit (gradient-enhanced) Kriging model
km.2d <- gekm(y ~ .^2, data = dat)
gekm.2d <- gekm(y ~ .^2, data = dat, deriv = deri)

# Plot leave-one-out cross-validation results
plot(km.2d)
plot(km.2d, panel.first = grid())
plot(km.2d, panel.first = {grid(); abline(0, 1, col = 8)})
plot(km.2d, add.interval = TRUE)
plot(km.2d, add.interval = TRUE, pch = 16, col = 4)
plot(km.2d, add.interval = TRUE, pch = 16, col = 4,
	panel.first = {grid(); abline(0, 1)}, 
	args.arrows = list(col = 4, length = 0))

plot(km.2d, pch = 1, col = 4, cex = 1.2, lwd = 2)
plot(gekm.2d, pch = 4, col = 2, cex = 1.2, lwd = 2, add = TRUE)
legend("topleft", legend = c("Kriging", "GEK"), col = c(4, 2), pch = c(1, 4), pt.lwd = 2)

par(mfrow = c(1, 2), oma = c(3.6, 3.5, 1.5, 0.2), mar = c(0, 0, 1.5, 0))
res <- plot(km.2d, col = 7, pch = 16, add.interval = TRUE, main = "Kriging",
	scale = TRUE, panel.first = {grid(); abline(0, 1, col = 8)})
res
plot(gekm.2d, col = 3, pch = 16, add.interval = TRUE, scale = TRUE, main = "GEK",
	ylim = range(res), yaxt = "n", panel.first = {grid(); abline(0, 1, col = 8)})
title(main = "Leave-One-Out", outer = TRUE)
mtext(side = 1, outer = TRUE, line = 2.5, "response")
mtext(side = 2, outer = TRUE, line = 2.5, "prediction")

gek documentation built on Jan. 31, 2026, 1:07 a.m.