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## This file is part of the UncertaintyInterpolation 2.0 package.
##
## Copyright 2015 Tomas Burian
#' @title
#' Spline interpolation
#'
#' @description
#' This function provides Spline interpolation over the input data enriched by
#' the uncertainty. The input data must be an S4 object class of
#' \code{UncertainPoints} and grid type of \code{data.frame}.
#' Output object is type of S4 class \code{UncertainInterpolation}.
#'
#' @param object Input data. An object of \code{UncertainPoints} class.
#' @param grid Input grid type of \code{dataframe}.
#' @param m A polynomial function of degree (m-1) will be included in
#' the model as the drift (or spatial trend) component. Default is
#' the value such that 2m-d is greater than zero where d is the dimension of x.
#' @param p Polynomial power for Wendland radial basis functions. Default is
#' 2m-d where d is the dimension of x.
#' @param scale.type The independent variables and knots are scaled to the specified
#' scale.type. By default the scale type is "range", whereby the locations are
#' transformed to the interval (0,1) by forming (x-min(x))/range(x) for each x.
#' Scale type of "user" allows specification of an x.center and x.scale by the user.
#' The default for "user" is mean 0 and standard deviation 1. Scale type of "unscaled"
#' does not scale the data.
#' @param lon.lat If TRUE locations are interpreted as lognitude and latitude and great circle distance is used to find distances among locations.
#' @param miles If TRUE great circle distances are in miles if FALSE distances are in kilometers.
#' @param method Determines what "smoothing" parameter should be used. The default is to estimate standard GCV Other choices are: GCV.model, GCV.one, RMSE, pure error and REML. The differences are explained in the Krig help file.
#' @param GCV If TRUE the decompositions are done to efficiently evaluate the estimate, GCV function and likelihood at multiple values of lambda.
#'
#' @usage
#' \S4method{splineUncertain}{UncertainPoints,data.frame}(object, grid, m = NULL, p = NULL,
#' scale.type = "range", lon.lat = FALSE, miles = TRUE, method = "GCV", GCV = TRUE)
#'
#' @return Returns an object of class \code{UncertainInterpolation}.
#'
#' @seealso \code{\link[UncerIn2]{UncertainPoints-class}}, \code{\link[UncerIn2]{UncertainInterpolation-class}}, \code{\link[UncerIn2]{Grid.def}},\code{\link[UncerIn2]{Grid.box}}, \code{\link[UncerIn2]{Grid.interpolation}}, \code{\link[fields]{Tps}}, \code{\link[UncerIn2]{Plot}}, \code{\link[UncerIn2]{uncertaintyInterpolation2-package}}
#'
#' @name splineUncertain
#' @docType methods
#' @rdname splineUncertain
#' @aliases splineUncertain,UncertainPoints,data.frame-method
#' @import fields
#' @importFrom stats predict
#' @exportMethod splineUncertain
setGeneric("splineUncertain",
function(object, grid, ...)
standardGeneric("splineUncertain")
)
setMethod("splineUncertain",
signature(object = "UncertainPoints", grid = "data.frame"),
definition = function(object, grid, m = NULL, p = NULL, scale.type = "range", lon.lat = FALSE,
miles = TRUE, method = "GCV", GCV = TRUE)
{
if(!(inherits(grid, "data.frame"))){
stop( paste("Grid: ", deparse(substitute(testData))," is not of type data.frame." , sep=""))
}
a = as.dataframe(object)
loc = cbind(a$x, a$y)
a1 <- Tps(loc, object@uncertaintyLower, m = m, p = p, scale.type = scale.type,
lon.lat = lon.lat, miles = miles, method = method, GCV = GCV)
a11 <- predict(a1, grid)
a2 <- Tps(loc, object@modalValue, m = m, p = p, scale.type = scale.type,
lon.lat = lon.lat, miles = miles, method = method, GCV = GCV)
a22 <- predict(a2, grid)
a3 <- Tps(loc, object@uncertaintyUpper, m = m, p = p, scale.type = scale.type,
lon.lat = lon.lat, miles = miles, method = method, GCV = GCV)
a33 <- predict(a3, grid)
new("UncertainInterpolation", x=grid$x, y=grid$y, uncertaintyLower=a11[,1], modalValue=a22[,1], uncertaintyUpper=a33[,1])
}
)
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