histde: Histogram density estimate

In ks: Kernel Smoothing

Histogram density estimate

Description

Histogram density estimate for 1- and 2-dimensional data.

Usage

histde(x, binw, xmin, xmax, adj=0)

## S3 method for class 'histde'
predict(object, ..., x)

Arguments

x	matrix of data values
binw	(vector) of binwidths
xmin,xmax	vector of minimum/maximum values for grid
adj	displacement of default anchor point, in percentage of 1 bin
object	object of class histde
...	other parameters

Details

If binw is missing, the default binwidth is \hat{b}_i = 2 \cdot 3^{1/(d+2)} \pi^{d/(2d+4)} S_i n^{-1/(d+2)}, the normal scale selector.

If xmin is missing then it defaults to the data minimum. If xmax is missing then it defaults to the data maximum.

Value

A histogram density estimate is an object of class histde which is a list with fields:

x	data points - same as input
eval.points	vector or list of points at which the estimate is evaluated
estimate	density estimate at eval.points
binw	(vector of) bandwidths
nbin	(vector of) number of bins
names	variable names

See Also

plot.histde

Examples

## positive data example
set.seed(8192)
x <- 2^rnorm(100)
fhat <- histde(x=x)
plot(fhat, border=6)
points(c(0.5, 1), predict(fhat, x=c(0.5, 1)))

## large data example on a non-default grid
set.seed(8192)
x <- rmvnorm.mixt(10000, mus=c(0,0), Sigmas=invvech(c(1,0.8,1)))
fhat <- histde(x=x, xmin=c(-5,-5), xmax=c(5,5))
plot(fhat)

## See other examples in ? plot.histde

ks documentation built on Aug. 11, 2023, 1:10 a.m.