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#
# fields is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2024 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka, douglasnychka@gmail.com,
#
# 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
# wrapper for Tps object
"predict.Tps"<- function(object, ...){
UseMethod("Krig")
}
"predict.Krig" <- function(object, x = NULL, Z = NULL,
drop.Z = FALSE, just.fixed = FALSE, lambda = NA, df = NA,
model = NA, eval.correlation.model = TRUE, y = NULL, yM = NULL,
verbose = FALSE, ...) {
#NOTE: most of this function is figuring out what to do!
#
# check that derivative is not called
if (!is.null(list(...)$derivative)) {
stop("For derivatives use predictDerivative")
}
# y is full data yM are the data collapsed to replicate means
# if new data is not passed then copy from the object
if (is.null(y) & is.null(yM)) {
temp.c <- object$c
temp.d <- object$d
}
# check for passed x but no Z -- this is an error
# if there are Z covariates in the model and drop.Z is FALSE
ZinModel<- !is.null(object$Z)
newX<- !is.null(x)
missingZ<- is.null(Z)
if( ZinModel&newX){
if( missingZ & !drop.Z) {
stop("Need to specify drop.Z as TRUE or pass Z values")
}
}
# default is to predict at data x's
if (is.null(x)) {
x <- object$x
}
else {
x <- as.matrix(x)
}
# default is to predict at data Z's
if (is.null(Z)) {
Z <- object$Z
}
else {
Z <- as.matrix(Z)
}
if (verbose) {
print(x)
print(Z)
}
# transformations of x values used in Krig
xc <- object$transform$x.center
xs <- object$transform$x.scale
x <- scale(x, xc, xs)
# NOTE knots are already scaled in Krig object and are used
# in transformed scale.
# i.e. knots <- scale( object$knots, xc, xs)
#
# figure out if the coefficients for the surface needto be recomputed.
find.coef<- (!is.null(y) | !is.null(yM) | !is.na(lambda) |
!is.na(df) | !is.na(model[1]))
if (verbose) {
cat("find.coef", find.coef, fill = TRUE)
}
# convert effective degrees of freedom to equivalent lambda
if (!is.na(df)) {
lambda <- Krig.df.to.lambda(df, object$matrices$D)
}
if (!is.na(model)) {
lambda <- model[1]
}
if (is.na(lambda))
lambda <- object$lambda
#
# if the coefficients need to be recomputed do it.
if (find.coef) {
if (verbose) {
cat("new coefs found", fill = TRUE)
}
object3 <- Krig.coef(object, lambda = lambda, y = y,
yM = yM)
temp.d <- object3$d
temp.c <- object3$c
}
if (verbose) {
cat(" betas", fill = TRUE)
print(temp.d)
cat("c coefs", fill = TRUE)
print(temp.c)
}
# this is the fixed part of predictor
#
Tmatrix <- do.call(object$null.function.name, c(object$null.args,
list(x = x, Z = Z, drop.Z = drop.Z)))
if (drop.Z) {
temp <- Tmatrix %*% temp.d[object$ind.drift]
}
else {
temp <- Tmatrix %*% temp.d
}
# add in spatial piece
if (!just.fixed) {
#
# Now find sum of covariance functions times coefficients
# Note that the multiplication of the cross covariance matrix
# by the coefficients is done implicitly in the covariance function
#
# The covariance function is
# evaluated by using its name, the do.call function, and any
# additional arguments.
#
temp <- temp + do.call(object$cov.function.name, c(object$args,
list(x1 = x, x2 = object$knots, C = temp.c)))
}
return(temp)
}
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