R/summary.GP.R

In GPfit: Gaussian Processes Modeling

#' Summary of GP model fit
#' 
#' Prints the summary of a class \code{GP} object estimated by \code{GP_fit}
#' 
#' prints the summary of the GP object (\code{object}), by calling
#' \code{\link{print.GP}}
#' 
#' @param object a class \code{GP} object estimated by \code{GP_fit}
#' @param \dots for compatibility with generic method \code{\link{summary}}
#' @author Blake MacDonald, Hugh Chipman, Pritam Ranjan
#' @seealso \code{\link{print.GP}} for more description of the output; \cr
#' \code{\link{GP_fit}} for more information on estimating the model; \cr
#' \code{\link{summary}} for more description on the \code{summary} function.
#' @examples
#' 
#' ## 1D example
#' 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)
#' x <- lhs::maximinLHS(n, d)
#' y <- computer_simulator(x)
#' GPmodel <- GP_fit(x, y)
#' summary(GPmodel)
#' 
#' ## 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 <- 10
#' d <- 2
#' set.seed(1)
#' x <- lhs::maximinLHS(n, d) 
#' y <- computer_simulator(x)
#' GPmodel <- GP_fit(x, y)
#' summary(GPmodel)
#' 
#' @export
#' @method summary GP

summary.GP <- function(object, ...){
    print(object, ...)
}