ks {ks} | R Documentation |
Kernel density estimation and kernel discriminant analysis for multivariate data (1- to 6-dimensions) with display functions.
There are three main types of functions in this package: (a) bandwidth selectors, (b) kernel estimators and (c) display.
(a) For the bandwidth matrix selectors, there are several varieties: (i) plug-in
Hpi
, (ii) least squares (or unbiased) cross validation
(LSCV or UCV)
Hlscv
, (iii) biased cross validation (BCV)
Hbcv
and (iv) smoothed cross validation (SCV)
Hscv
. Scalar bandwidth selectors are not provided - see
sm
or KernSmooth
packages.
(b) For kernel density estimation, the main function is
kde
. For kernel discriminant analysis, it's kda.kde
.
(c) For display, versions of plot
send to a graphics window
the results of density estimation or discriminant analysis.
Tarn Duong <tduong@maths.unsw.edu.au>
Bowman, A. & Azzalini, A. (1997) Applied Smoothing Techniques for Data Analysis. Oxford University Press. Oxford.
Duong, T. (2004) Bandwidth Matrices for Multivariate Kernel Density Estimation. Ph.D. Thesis. University of Western Australia.
Duong, T. & Hazelton, M.L. (2003) Plug-in bandwidth matrices for bivariate kernel density estimation. Journal of Nonparametric Statistics 15, 17-30.
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics. 32, 485-506.
Sain, S.R., Baggerly, K.A. & Scott, D.W. (1994) Cross-validation of multivariate densities. Journal of the American Statistical Association. 82, 1131-1146.
Scott, D.W. (1992) Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons. Mew York.
Simonoff, J. S. (1996) Smoothing Methods in Statistics. Springer-Verlag. New York.
Wand, M.P. & Jones, M.C. (1994) Multivariate plugin bandwidth selection. Computational Statistics 9, 97-116.
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall/CRC. London.