bkde2D(x, bandwidth, gridsize=c(51, 51), range.x=<<see below>>, truncate=T)
x
| a two-column matrix containing the observations from the distribution whose density is to be estimated. Missing values are not allowed. |
bandwidth
| vector containing the bandwidth to be used in each coordinate direction. |
gridsize
| vector containing the number of equally spaced points in each direction over which the density is to be estimated. |
range.x
|
a list containing two vectors, where each vector
contains the minimum and maximum values of x
at which to compute the estimate for each direction.
The default minimum in each direction is minimum
data value minus 1.5 times the bandwidth for
that direction. The default maximum is the maximum
data value plus 1.5 times the bandwidth for
that direction
|
truncate
|
logical flag: if TRUE, data with x values outside the
range specified by range.x are ignored.
|
x1
| vector of values of the grid points in the first coordinate direction at which the estimate was computed. |
x2
| vector of values of the grid points in the second coordinate direction at which the estimate was computed. |
fhat
|
matrix of density estimates
over the mesh induced by x1 and x2 .
|
x1
,x2
pair the bivariate Gaussian kernel is
centered on that location and the heights of the
kernel, scaled by the bandwidths, at each datapoint are summed.
This sum, after a normalization, is the corresponding
fhat
value in the output.Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.
bkde
, density
, hist
, ksmooth
.data(geyser) x <- cbind(geyser$duration,geyser$waiting) est <- bkde2D(x,bandwidth=c(0.7,7)) contour(est$x1,est$x2,est$fhat) # persp(est$fhat) ## not yet in R