Linear binning for multivariate data
Linear binning for 1- to 4-dimensional data.
binning(x, H, h, bgridsize, xmin, xmax, supp=3.7, w, gridtype="linear")
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 |
For ks >= 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).
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 |
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.
data(unicef) ubinned <- binning(x=unicef)
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