# Plot Method for a gekm Object Visualization of the leave-one-out cross-validation results of a `gekm` model. UTF-8 ```r ## 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 ## Returns 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. ## 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. 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. 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. tools:::Rd_expr_doi("10.2514/1.8650") . ## Author(s) Carmen van Meegen ## 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 ```r ## 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") ```

plot.gekm function