Histogram density estimate — histde • ks

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

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

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.

See also

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