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#' Plot 1D Gaussian Process Model
#'
#' Plots univariate Gaussian process (GP) models with 95\% credible bands. For extremely simply visualizations.
#'
#' @param model A GP model of class \code{km} (see \code{\link[DiceKriging]{km-class}}).
#' @param inputs An vector of values at which to evaluate the GP model.
#' @param plot_data Plot data used to fit the GP as well?
#'
#' @return A list of GP predictions and uncertainties used to generate the figure.
#'
#' @export
#' @examples
#' x <- matrix(seq(-1,1,0.3),ncol=1)
#' y <- x^2
#' gp <- gp_fit(x,
#' y,
#' options=list(formula=~1,
#' kernel = "matern5_2",
#' optimizer = "gen",
#' nuggetUse = FALSE))
#' gp_plot(gp$km.model,seq(-1.5,1.5,0.1))
gp_plot <- function(model,inputs, plot_data = FALSE) {
inputs <- sort(inputs)
y <- stats::predict(model,data.frame(design = inputs),type="SK")
top <- max(y$upper95)
bottom <- min(y$lower95)
graphics::plot(inputs,y$mean,type='l',ylim=c(bottom - abs(top-bottom)*0.25,top + abs(top-bottom)*0.25),xlab="design",ylab="response")
graphics::lines(inputs,y$upper95,lty=2,col='red')
graphics::lines(inputs,y$lower95,lty=2,col='blue')
graphics::points(model@X,model@y,pch=20)
return(y)
}
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