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#################################################################################
##
## R package spd by Alexios Ghalanos Copyright (C) 2008-2013
## This file is part of the R package spd.
##
## The R package spd 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 3 of the License, or
## (at your option) any later version.
##
## The R package spd 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.
##
#################################################################################
# approximate PDF from CDF given a set of bin points found by calling
# .histcount
eps=.Machine$double.eps
.interpPDF = function(xm,ppx,ppz)
{
# xm = middle values of dspd(x,...)
# ppx = kernel interpolated density of original data
# ppz = empirical cdf probability [0,1] (cumsum of ppy/sum of ppy)
m=length(xm)
bins<-vector(mode="numeric",length=m)
nbin = length(ppx)
nx = vector(mode="numeric",length=nbin)
z=as.data.frame(1:nbin)
kk=apply(z,1,FUN=function(x) which( xm >= ppx[x]))
for(i in 1:nbin)
{
bins[kk[[i]]]<-i
}
nx=apply(z,1,FUN=function(x) length(kk[[x]]))
kk = which( xm > ppx[nbin] )
bins[kk] = 0
nx[nbin+1] = length(kk)
counts = -diff(nx)
bin=bins
bin[xm==ppx[1]] = 1
bin[xm==ppx[length(ppx)]] = length(counts)-1
fx = vector(mode="numeric",length=length(xm))
tx = bin>0
bin = bin[tx]
# find the PDF probability
tmp = (ppz[bin+1] - ppz[bin])/ (ppx[bin+1] - ppx[bin])
tna = which(!is.na(tmp))
tx = tx[tna]
fx[tna] = tmp[!is.na(tmp)]
return(fx)
}
.findthresh<-function(data, exceed)
{
data <- rev(sort(data))
uniq <- unique(data)
idx <- match(data[exceed], uniq)
idx <- pmin(idx + 1, length(uniq))
return(uniq[idx])
}
.description<-function()
{
ans = paste(as.character(date()), "by user:", Sys.getenv("USERNAME"))
ans
}
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