plot function

Plotting GP model fits

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

## 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)

• GP: The plot method creates a graphics plot for 1-D fits and lattice plot for 2-D fits.

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)

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.

Author(s)

Blake MacDonald, Hugh Chipman, Pritam Ranjan