Description Usage Arguments Value Author(s) References See Also Examples
View source: R/pcf.kern.R View source: R/denpro.R
Computes a multivariate kernel estimate and gives the output as a piecewise constant function object.
1 2 |
dendat |
n*d matrix of real numbers; the data matrix |
h |
d vector of positive real numbers; vector of smoothing parameters |
N |
vector of d positive dyadic integers; the dimension of the grid where the kernel estimate will be evaluated; we evaluate the estimate on a regular grid which contains the support of the kernel estimate |
kernel |
"gauss", "epane", "bart", or "uniform"; the kernel is either the standard Gaussian, Epanechnikov product kernel, Bartlett kernel, or uniform kernel |
weights |
n vector of nonnegative weights, where n is the sample size; sum of the elements of "weights" should be one; these are the weights of a time localized kernel estimator |
support |
2*d vector of reals gives the d intervals of a rectangular support in the form c(low1,upp1,...,lowd,uppd) |
lowest |
a real value; the density estimate will be truncated to take value zero, if the value of the estimate is less or equal to "lowest" |
radi |
a nonnegative real number; the support is estimated as the smallest rectangle containing the observations with an additional band whose width is equal to "radi" |
a piecewise constant function object, see the web site
Jussi Klemela
http://www.rni.helsinki.fi/~jsk/denpro/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | n<-100
dendat<-sim.data(n=n,type="mulmod")
h<-1
pcf<-pcf.kern(dendat,h=h,N=c(32,32))
dp<-draw.pcf(pcf)
contour(dp$x,dp$y,dp$z,drawlabels=FALSE)
d<-2
h<-(4/(d+2))^(1/(d+4))*n^(-1/(d+4))*apply(dendat,2,sd)
pcf<-pcf.kern(dendat,h=h,N=c(32,32))
dp<-draw.pcf(pcf)
contour(dp$x,dp$y,dp$z,drawlabels=FALSE)
# we use now nonuniform weighting of kernels
weights<-matrix(0,n,1)
threshold<-4
for (i in 1:n){
eta<-(n-i)
if (eta/h>threshold) result<-0 else result<-exp(-eta^2/(2*h^2))
weights[i]<-result
}
weights<-weights/sum(weights)
pcf<-pcf.kern(dendat,h=1,N=c(32,32),weights=weights)
dp<-draw.pcf(pcf)
contour(dp$x,dp$y,dp$z,drawlabels=FALSE)
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