pcf.kern {denpro} R Documentation

## Calculates a multivariate kernel estimate

### Description

Calculates a multivariate kernel estimate and gives the output as a piecewise constant function object.

### Usage

```pcf.kern(dendat, h, N, kernel = "gauss", weights = NULL, support = NULL,
lowest = 0)
```

### Arguments

 `dendat` n*d matrix of real numbers; the data matrix `h` positive real; smoothing parameter `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"

### Value

a piecewise constant function object, see the web site

Jussi Klemela

### References

http://www.rni.helsinki.fi/~jsk/denpro/

`draw.pcf` `lstseq.kern`

### Examples

```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)

# 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)

```

[Package denpro version 0.9.0 Index]