R/plot.Krig.R

In fields: Tools for Spatial Data

#
# fields  is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2022 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka,  douglasnychka@gmail.edu,
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the R software environment if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
# or see http://www.r-project.org/Licenses/GPL-2
##END HEADER
"plot.Krig" <- function(x, digits = 4, which = 1:4, 
    ...) {
    out <- x
    #
    #   don't do plots 2:4 if a fixed lambda
    #
    if (x$fixed.model) {
        which <- 1
    }
    fitted.values <- predict(out)
    std.residuals <- (out$residuals * sqrt(out$weights))/out$tauHat.GCV
    if (any(which == 1)) {
        temp <- summary(out)
        plot(fitted.values, out$y, ylab = "Y", xlab = " predicted values", 
            bty = "n", ...)
        abline(0, 1)
       # hold <- par("usr")
       # text(hold[1], hold[4], paste(" R**2 = ", format(round(100 * 
        #    temp$covariance, 2)), "%", sep = ""), cex = 0.8, 
        #    adj = 0)
    }
    if (any(which == 2)) {
        plot(fitted.values, std.residuals, ylab = "(STD) residuals", 
            xlab = " predicted values", bty = "n", ...)
        yline(0)
        hold <- par("usr")
       # text(hold[1], hold[4], paste(" RMSE =", format(signif(sqrt(sum(out$residuals^2)/(temp$num.observation - 
       #     temp$enp)), digits))), cex = 0.8, adj = 0)
    }
    if (any(which == 3)) {
        if (nrow(out$gcv.grid) > 1) {
            ind <- out$gcv.grid[, 3] < 1e+19
            out$gcv.grid <- out$gcv.grid[ind, ]
            yr <- range(unlist(out$gcv.grid[, 3:5]), na.rm = TRUE)
            plot(out$gcv.grid[, 2], out$gcv.grid[, 3], xlab = "Eff. number of parameters", 
                ylab = " GCV function", bty = "n", ylim = yr, 
             
                 ...)
            lines(out$gcv.grid[, 2], out$gcv.grid[, 4], lty = 3)
            lines(out$gcv.grid[, 2], out$gcv.grid[, 5], lty = 1)
            xline(out$eff.df, lwd=2, col="grey")
            usr.save<- par()$usr
            usr.save[3:4]<- range( -out$gcv.grid[,7] )
            par( usr= usr.save, ylog=FALSE)
            lines( out$gcv.grid[, 2], -out$gcv.grid[,7] ,
            lty=2, lwd=2, col="blue")
            axis( side=4)
            mtext( side=4, line=2, "log profile likelihood ")
            title("GCV-points, solid-model, dots- single  \n REML dashed", 
                cex = 0.5)
            box()
        }
    }
    if (any(which == 4)) {
        hist(std.residuals, ylab="")
    }
}