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#************************************************************************
# Jone's nonnegative boundary correction for Muller94's Boundary Kernel
# Density estimator
#
# @article{Jones1996,
# title = {A simple nonnegative boundary correction method for kernel density estimation},
# author = {Jones, M.C. and Foster, P.J.},
# journal = {Statistica Sinica},
# year = {1996},
# volume = {6},
# pages = {1005-1013}
# }
#************************************************************************
setClass(
Class = "JonesCorrectionMuller94BoundaryKernel",
representation = representation(
normalizedKernel = "NormalizedBoundaryKernel"),
contains = "Muller94BoundaryKernel"
)
setMethod(
f = "density",
signature = "JonesCorrectionMuller94BoundaryKernel",
definition = function(x,values,scaled = FALSE) {
.Object <- x
x <- values
isMatrix.x <- is.matrix(x)
#dims = [nrows,ncols]
dims <- dim(x)
if(!scaled){
# scale the data to the 0-1 interval
x <- getScaledPoints(.Object,x)
}
# if any value in x is lower than 0 or grater than 1 its density is 0
numDataPoints <- length(x)
index.nozero <- which(x>=0 & x <=1)
x <- x[index.nozero]
if(length(x) == 0){ # all elements in x were out of bound
return(rep(0,numDataPoints - length(index.nozero)))
}
# x is considered as a vector even if it is a matrix(elements taken by columns)
x.indices <- rep(0,times = length(x))
x.densities <- numeric(0)
if(length(.Object@densityCache) == length(.Object@dataPointsCache)){
# if there are density values calculated in the cache, first we look
# at the cache to check whether some of the values in x have been already calculated
x.indices <- match(x, .Object@dataPointsCache, nomatch=0)
if(any(x.indices > 0)){
# the density of some of the points are already calculated in the cache
x.densities[x.indices != 0] <- .Object@densityCache[x.indices[x.indices!=0]]
}else{}
}else{}
# the data poins whose densities are not calculated in the cache
x.new <- x[x.indices == 0]
x.new.length <- length(x.new)
if(x.new.length > 0){
# There are densities to be calculated
# the density value of the Muller91BoundaryKernel #borrar la densityCache antes de llamar a callNextMethod para por si acaso
f.muller <- callNextMethod(.Object,x.new,scaled=TRUE)
f.localRenormalized <- density(.Object@normalizedKernel,x.new,scaled=TRUE)
densities <- f.localRenormalized*exp((f.muller/f.localRenormalized)-1)
# The density when f.localRenormalized = 0 is also 0
densities[which(f.localRenormalized==0)] = 0
x.densities[x.indices == 0] <- densities
}else{}
# include the density (density=0) of the out-of-bound x points in the final result
aux.density <- numeric(numDataPoints)
aux.density[index.nozero] <- x.densities
x.densities <- aux.density
#if x is a matrix, we storte the densities as a matrix object
if(isMatrix.x){
dim(x.densities) <- dims
}else{}
#if data are in another scale (not in the [0,1] interval) we should
# normalize the density by dividing it by the length
# of the interval so that the density integrates to 1
domain.length <- .Object@upper.limit - .Object@lower.limit
if(!scaled){
x.densities <- x.densities/domain.length
}
return(x.densities)
})
#####################################
## Constructor functions for users ##
#####################################
jonesCorrectionMuller94BoundaryKernel <- function(dataPoints, mu=1, b=length(dataPoints)^(-2/5), dataPointsCache=NULL, lower.limit=0,upper.limit=1){
#cat("~~~~~~ Jones Correction for Muller94 Boundary Kernel: constructor ~~~~~~\n")
dataPoints.scaled <- dataPoints
dataPointsCache.scaled <- dataPointsCache
if(is.null(dataPointsCache)){
dataPointsCache.scaled <- seq(0,1,0.01)
}
if(lower.limit!=0 || upper.limit!=1){
dataPoints.scaled <- (dataPoints-lower.limit)/(upper.limit-lower.limit)
if(!is.null(dataPointsCache)){
dataPointsCache.scaled <- (dataPointsCache-lower.limit)/(upper.limit-lower.limit)
}
}
kernel.localRenormalized <- normalizedBoundaryKernel(dataPoints=dataPoints, mu=mu, b=b, dataPointsCache=dataPointsCache, lower.limit=lower.limit,upper.limit=upper.limit)
kernel <- new(Class="JonesCorrectionMuller94BoundaryKernel",dataPoints = dataPoints.scaled, b = b,
dataPointsCache = dataPointsCache.scaled, mu = mu, normalizedKernel=kernel.localRenormalized,
lower.limit=lower.limit,upper.limit=upper.limit)
setDensityCache(kernel, densityFunction=NULL)
return(kernel)
}
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