| Hlscv {ks} | R Documentation |
LSCV bandwidth for 1- to 6-dimensional data
Hlscv(x, Hstart, binned=FALSE, bgridsize, amise=FALSE, deriv.order=0,
verbose=FALSE, optim.fun="nlm", trunc)
Hlscv.diag(x, Hstart, binned=FALSE, bgridsize, amise=FALSE, deriv.order=0,
verbose=FALSE, optim.fun="nlm", trunc)
hlscv(x, binned=TRUE, bgridsize, amise=FALSE, deriv.order=0)
x |
vector or matrix of data values |
Hstart |
initial bandwidth matrix, used in numerical optimisation |
binned |
flag for binned kernel estimation. Default is FALSE. |
bgridsize |
vector of binning grid sizes |
amise |
flag to return the minimal LSCV value. Default is FALSE. |
deriv.order |
derivative order |
verbose |
flag to print out progress information. Default is FALSE. |
optim.fun |
|
trunc |
parameter to control truncation for numerical optimisation. Default is 4 for density.deriv>0, otherwise no truncation. For details see below. |
hlscv is the univariate SCV
selector of Bowman (1984) and Rudemo (1982). Hlscv is a
multivariate generalisation of this. Use Hlscv for full bandwidth matrices and Hlscv.diag
for diagonal bandwidth matrices.
Truncation of the parameter space is usually required for the LSCV selector,
for r > 0, to find a reasonable solution to the numerical optimisation.
If a candidate matrix H is
such that det(H) is not in [1/trunc, trunc]*det(H0) or
abs(LSCV(H)) > trunc*abs(LSCV0) then the LSCV(H) is reset to LSCV0 where
H0=Hns(x) and LSCV0=LSCV(H0).
For details about the advanced options for binned,Hstart,
see Hpi.
LSCV bandwidth. If amise=TRUE then the minimal LSCV value is returned too.
Bowman, A. (1984) An alternative method of cross-validation for the smoothing of kernel density estimates. Biometrika. 71, 353-360.
Chacon, J.E. & Duong, T. (2014) Efficient recursive algorithms for functionals based on higher order derivatives of the multivariate Gaussian density. Statistics & Computing. DOI 10.1007/s11222-014-9465-1.
Rudemo, M. (1982) Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics. 9, 65-78.
library(MASS) data(forbes) Hlscv(forbes) hlscv(forbes$bp)