| h.select {sm} | R Documentation |
This function selects a smoothing parameter for density estimation in one or two dimensions and for nonparametric regression with one or two covariates. Several methods of selection are available.
h.select(x, y = NA, weights = NA, group = NA, ...)
x |
a vector, or two-column matrix. |
y |
a vector of reponses, in regression case. |
weights |
a vector of integers representing frequencies of individual
observations. Use of this parameter is incompatible with
binning; hence nbins must then be set to 0
or left at its default value NA.
|
group |
a vector of groups indicators (numeric or character values) or a factor |
... |
other optional parameters are passed to the sm.options
function, through a mechanism which limits their effect only to this
call of the function. There are three which are relevant for this
function, namely method, which specifies the method of
smoothing parameter selection, df, which specifies the
approximate degrees of freedom associated with the selected
smoothing parameter, and structure.2d which determines
the form of the smoothing parameters in the two-dimensional case.
A full description of these arguments are given in the documentation
of sm.options.
|
see the two references below for discussion of the methods of smoothing parameter selection.
When the group argument is set, the chosen method of
smoothing parameter selection is applied to each group and the
value returned is the geometric mean of these. This is intended
for use in sm.density.compare and
sm.ancova, where
the same smoothing parameter is used for all groups so that
the principal bias terms cancel when the estimates are compared.
the value of the selected smoothing parameter.
none
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
Hurvich, C.M., Simonoff, J.S. and Tsai, C.-L. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J. R. Statistic. Soc., Series B, 60, 271-293.
x <- rnorm(50) h.select(x) h.select(x, method = "sj") x <- matrix(rnorm(100), ncol = 2) h.select(x) sm.density(x, method = "cv") x <- rnorm(50) y <- x^2 + rnorm(50) h.select(x, y) sm.regression(x, y, method = "aicc") x <- matrix(rnorm(100), ncol = 2) y <- x[,1]^2 + x[,2]^2 + rnorm(50) h.select(x, y, method = "cv", structure.2d = "common") sm.regression(x, y, df = 8)