https://github.com/cran/fields
Tip revision: 9bfb718aad728afc7e5cc72608794fd2471fd0f9 authored by Douglas Nychka on 28 May 2019, 20:20:03 UTC
version 9.8-3
version 9.8-3
Tip revision: 9bfb718
summary.Krig.R
# fields is a package for analysis of spatial data written for
# the R software environment .
# Copyright (C) 2018
# University Corporation for Atmospheric Research (UCAR)
# Contact: Douglas Nychka, nychka@ucar.edu,
# National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307-3000
#
# 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
"summary.Krig" <- function(object, digits = 4, ...) {
x <- object
# lambda est may not be available if lambda has been supplied by user.
if (!is.na(x$lambda.est[1])) {
l.est <- x$lambda.est
}
else {
l.est <- NA
}
summary <- list(call = x$call, num.observation = length(x$residuals),
enp = x$eff.df, nt = x$nt, df.drift = sum(x$ind.drift),
res.quantile = quantile(x$residuals, seq(0, 1, 0.25)),
shat.MLE = x$shat.MLE, shat.GCV = x$shat.GCV, rhohat = x$rhohat,
m = x$m, lambda = x$lambda, cost = x$cost, rho = x$rho,
sigma2 = x$sigma2, num.uniq = length(x$yM), knot.model = x$knot.model,
np = x$np, method = x$method, lambda.est = l.est, shat.pure.error = x$shat.pure.error,
args = x$args)
class(summary) <- "summary.Krig"
summary$covariance <- cor(x$fitted.values * sqrt(x$weights),
(x$y) * sqrt(x$weights))^2
hold <- (sum((x$y - mean(x$y))^2) - sum(x$residuals^2))/(sum((x$y -
mean(x$y))^2))
summary$adjr2 <- 1 - ((length(x$residuals) - 1)/(length(x$residuals) -
x$eff.df)) * (1 - hold)
summary$digits <- digits
summary$cov.function <- as.character(x$cov.function.name)
summary$correlation.model <- x$correlation.model
summary$sum.gcv.lambda <- summaryGCV.Krig(x, x$lambda)
summary
}