binning: Linear binning for multivariate data

In ks: Kernel Smoothing

Linear binning for multivariate data

Description

Linear binning for 1- to 4-dimensional data.

Usage

binning(x, H, h, bgridsize, xmin, xmax, supp=3.7, w, gridtype="linear")

Arguments

x	matrix of data values
H, h	bandwidth matrix, scalar bandwidth
xmin, xmax	vector of minimum/maximum values for grid
supp	effective support for standard normal is [-supp,supp]
bgridsize	vector of binning grid sizes
w	vector of weights. Default is a vector of all ones.
gridtype	not yet implemented

Details

For ks \geq 1.10.0, binning is available for unconstrained (non-diagonal) bandwidth matrices. Code is used courtesy of A. & J. Gramacki, and M.P. Wand. Default bgridsize are d=1: 401; d=2: rep(151, 2); d=3: rep(51, 3); d=4: rep(21, 4).

Value

Returns a list with 2 fields

counts	linear binning counts
eval.points	vector (d=1) or list (d>=2) of grid points in each dimension

References

Gramacki, A. & Gramacki, J. (2016) FFT-based fast computation of multivariate kernel estimators with unconstrained bandwidth matrices. Journal of Computational & Graphical Statistics, 26, 459-462.

Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.

Examples

data(unicef)
ubinned <- binning(x=unicef)

ks documentation built on Sept. 30, 2024, 9:15 a.m.