plot {GPfit}R Documentation

Plotting GP model fits

Description

Plots the predicted response and mean squared error (MSE) surfaces for simulators with 1 and 2 dimensional inputs (i.e. d = 1,2).

Usage

## S3 method for class 'GP'
plot(x, M = 1, range = c(0, 1), resolution = 50,
  colors = c("black", "blue", "red"), line_type = c(1, 2), pch = 20,
  cex = 1, legends = FALSE, surf_check = FALSE, response = TRUE,
  ...)

Arguments

x

a class GP object estimated by GP_fit

M

the number of iterations for use in prediction. See predict.GP

range

the input range for plotting (default set to [0, 1])

resolution

the number of points along a coordinate in the specified range

colors

a vector of length 3 assigning colors[1] to training design points, colors[2] to model predictions, and colors[3] to the error bounds

line_type

a vector of length 2 assigning line_type[1] to model predictions, and line_type[2] to the error bounds

pch

a parameter defining the plotting character for the training design points, see ‘pch’ for possible options in par

cex

a parameter defining the size of the pch used for plotting the training design points, see ‘cex’ for possible options in par

legends

a parameter that controls the inclusion of a legend; by default it is ‘FALSE’

surf_check

logical, switch between 3d surface and 2d level/contour plotting, the default of FALSE implies level/contour plotting

response

logical, switch between predicted response and error (MSE) plots, the default of TRUE displays the response surface

...

additional arguments from wireframe or levelplot

Methods (by class)

Author(s)

Blake MacDonald, Hugh Chipman, Pritam Ranjan

References

Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), 366 - 378.

See Also

GP_fit for estimating the parameters of the GP model;
predict.GP for predicting the response and error surfaces;
par for additional plotting characters and line types for 1 dimensional plots;
wireframe and levelplot for additional plotting settings in 2 dimensions.

Examples


## 1D Example 1
n <- 5
d <- 1 
computer_simulator <- function(x){
    x <- 2 * x + 0.5
    y <- sin(10 * pi * x) / (2 * x) + (x - 1)^4
    return(y)
}
set.seed(3)
library(lhs)
x <- maximinLHS(n,d)
y <- computer_simulator(x)
GPmodel <- GP_fit(x,y)
plot(GPmodel)

## 1D Example 2
n <- 7
d <- 1
computer_simulator <- function(x) {
    y <- log(x + 0.1) + sin(5 * pi * x)
    return(y)
}
set.seed(1)
library(lhs)
x <- maximinLHS(n,d)
y <- computer_simulator(x)
GPmodel <- GP_fit(x, y)
## Plotting with changes from the default line type and characters
plot(GPmodel, resolution = 100, line_type = c(6,2), pch = 5)


## 2D Example: GoldPrice Function
computer_simulator <- function(x) {
    x1 <- 4 * x[, 1] - 2
    x2 <- 4 * x[, 2] - 2
    t1 <- 1 + (x1 + x2 + 1)^2 * (19 - 14 * x1 + 3 * x1^2 - 14 * x2 + 
        6 * x1 * x2 + 3 * x2^2)
    t2 <- 30 + (2 * x1 - 3 * x2)^2 * (18 - 32 * x1 + 12 * x1^2 + 48 * x2 - 
        36 * x1 * x2 + 27 * x2^2)
    y <- t1 * t2
    return(y)
}
n <- 30 
d <- 2
set.seed(1)
x <- lhs::maximinLHS(n, d) 
y <- computer_simulator(x)
GPmodel <- GP_fit(x, y)
## Basic level plot
plot(GPmodel)
## Adding Contours and increasing the number of levels
plot(GPmodel, contour = TRUE, cuts = 50, pretty = TRUE)
## Plotting the Response Surface
plot(GPmodel, surf_check = TRUE)
## Plotting the Error Surface with color
plot(GPmodel, surf_check = TRUE, response = FALSE, shade = TRUE)


[Package GPfit version 1.0-8 Index]