Variable kernel density estimate.
Variable kernel density estimate for 2-dimensional data.
kde.balloon(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned, bgridsize, w, compute.cont=TRUE, approx.cont=TRUE, verbose=FALSE) kde.sp(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned, bgridsize, w, compute.cont=TRUE, approx.cont=TRUE, verbose=FALSE)
x |
matrix of data values |
H |
bandwidth matrix. If this missing, |
h |
not yet implemented |
gridsize |
vector of number of grid points |
gridtype |
not yet implemented |
xmin,xmax |
vector of minimum/maximum values for grid |
supp |
effective support for standard normal |
eval.points |
vector or matrix of points at which estimate is evaluated |
binned |
flag for binned estimation. |
bgridsize |
vector of binning grid sizes |
w |
vector of weights. Default is a vector of all ones. |
compute.cont |
flag for computing 1% to 99% probability contour levels. Default is TRUE. |
approx.cont |
flag for computing approximate probability contour levels. Default is TRUE. |
verbose |
flag to print out progress information. Default is FALSE. |
The balloon density estimate kde.balloon employs bandwidths
which vary at each
estimation point (Loftsgaarden & Quesenberry, 1965). There are as many bandwidths as there are estimation
grid points. The default bandwidth is Hns(,deriv.order=2) and
the subsequent bandwidths are derived via a minimal MSE formula.
The sample point density estimate kde.sp employs bandwidths
which vary for each data point (Abramson, 1982).
There are as many bandwidths as there are data
points. The default bandwidth is Hns(,deriv.order=4) and the
subsequent bandwidths are derived via the Abramson formula.
A variable kernel density estimate for bounded data is an object of class kde.
Abramson, I. S. (1982) On bandwidth variation in kernel estimates - a square root law. Annals of Statistics, 10, 1217-1223.
Loftsgaarden, D. O. & Quesenberry, C. P. (1965) A nonparametric estimate of a multivariate density function. Annals of Mathematical Statistics, 36, 1049-1051.
data(worldbank)
wb <- as.matrix(na.omit(worldbank[,4:5]))
xmin <- c(-70,-25); xmax <- c(25,70)
fhat <- kde(x=wb, xmin=xmin, xmax=xmax)
fhat.sp <- kde.sp(x=wb, xmin=xmin, xmax=xmax)
plot(fhat, display="persp", box=TRUE, phi=20, zlim=c(0,max(fhat.sp$estimate)))
plot(fhat.sp, display="persp", box=TRUE, phi=20, zlim=c(0,max(fhat.sp$estimate)))
if (interactive()){
fhat.ball <- kde.balloon(x=wb, xmin=xmin, xmax=xmax)
plot(fhat.ball, display="persp", box=TRUE, phi=20, zlim=c(0,max(fhat.sp$estimate)))}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.