stats (version 3.5.0)

bandwidth: Bandwidth Selectors for Kernel Density Estimation

Description

Bandwidth selectors for Gaussian kernels in density.

Usage

bw.nrd0(x)
bw.nrd(x)
bw.ucv(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
       tol = 0.1 * lower)
bw.bcv(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
       tol = 0.1 * lower)
bw.SJ(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
      method = c("ste", "dpi"), tol = 0.1 * lower)

Arguments

x

numeric vector.

nb

number of bins to use.

lower, upper

range over which to minimize. The default is almost always satisfactory. hmax is calculated internally from a normal reference bandwidth.

method

either "ste" ("solve-the-equation") or "dpi" ("direct plug-in"). Can be abbreviated.

tol

for method "ste", the convergence tolerance for uniroot. The default leads to bandwidth estimates with only slightly more than one digit accuracy, which is sufficient for practical density estimation, but possibly not for theoretical simulation studies.

Value

A bandwidth on a scale suitable for the bw argument of density.

Details

bw.nrd0 implements a rule-of-thumb for choosing the bandwidth of a Gaussian kernel density estimator. It defaults to 0.9 times the minimum of the standard deviation and the interquartile range divided by 1.34 times the sample size to the negative one-fifth power (= Silverman's ‘rule of thumb’, Silverman (1986, page 48, eqn (3.31))) unless the quartiles coincide when a positive result will be guaranteed.

bw.nrd is the more common variation given by Scott (1992), using factor 1.06.

bw.ucv and bw.bcv implement unbiased and biased cross-validation respectively.

bw.SJ implements the methods of Sheather & Jones (1991) to select the bandwidth using pilot estimation of derivatives. The algorithm for method "ste" solves an equation (via uniroot) and because of that, enlarges the interval c(lower, upper) when the boundaries were not user-specified and do not bracket the root.

The last three methods use all pairwise binned distances: they are of complexity \(O(n^2)\) up to n = nb/2 and \(O(n)\) thereafter. Because of the binning, the results differ slightly when x is translated or sign-flipped.

References

Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and Visualization. New York: Wiley.

Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society series B, 53, 683--690. http://www.jstor.org/stable/2345597.

Silverman, B. W. (1986). Density Estimation. London: Chapman and Hall.

Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Springer.

See Also

density.

bandwidth.nrd, ucv, bcv and width.SJ in package MASS, which are all scaled to the width argument of density and so give answers four times as large.

Examples

# NOT RUN {
require(graphics)

plot(density(precip, n = 1000))
rug(precip)
lines(density(precip, bw = "nrd"), col = 2)
lines(density(precip, bw = "ucv"), col = 3)
lines(density(precip, bw = "bcv"), col = 4)
lines(density(precip, bw = "SJ-ste"), col = 5)
lines(density(precip, bw = "SJ-dpi"), col = 6)
legend(55, 0.035,
       legend = c("nrd0", "nrd", "ucv", "bcv", "SJ-ste", "SJ-dpi"),
       col = 1:6, lty = 1)
# }