# 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). ```r ## 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 ```r ## 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

plot function