kde(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points,
binned=FALSE, bgridsize, positive=FALSE, adj.positive, w,
compute.cont=FALSE, approx.cont=TRUE, unit.interval=FALSE,
verbose=FALSE)## S3 method for class 'kde':
predict(object, ..., x)
Hpi or hpi is called by default.kdekde which is a
list with fields:eval.pointscompute.cont=TRUE)h is missing, the default bandwidth is hpi.
For d>1, if H is missing, the default is Hpi. For d=1, if positive=TRUE then x<-log(x+adj.positive)
where the default adj.positive is the minimum of x.
For d=1, 2, 3, 4, and if eval.points is not specified, then the
density estimate is computed over a grid
defined by gridsize (if binned=FALSE) or
by bgridsize (if binned=TRUE).
If eval.points is specified, then the
density estimate is computed exactly at eval.points.
For d>4, the kernel density estimate is computed exactly
and eval.points must be specified.
The effective support for a normal kernel is where
all values outside [-supp,supp]^d are zero.
The default xmin is min(x)-Hmax*supp and xmax
is max(x)+Hmax*supp where Hmax is the maximum of the
diagonal elements of H. The grid produced is the outer
product of c(xmin[1], xmax[1]), ..., c(xmin[d], xmax[d]).
The default bgridsize, gridsize are d=1: 401; d=2: rep(151, 2);
d=3: rep(31, 3); d=4: rep(21,4).
plot.kde## positive data example
set.seed(8192)
x <- 2^rnorm(100)
fhat <- kde(x=x, positive=TRUE)
plot(fhat)
points(c(0.5, 1), predict(fhat, x=c(0.5, 1)))
## See other examples in ? plot.kdeRun the code above in your browser using DataLab