R: Bandwidth Selectors for Kernel Density Estimation

bandwidth {base} | R Documentation

## Bandwidth Selectors for Kernel Density Estimation

### Description

Bandwidth selectors for gaussian windows in `density`.

### Usage

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

### Arguments

`x` | A data vector.
`nb` | number of bins to use.
`lower, upper` | Range over which to minimize. The default is almost always satisfactory.
`method` | Either `"ste"` ("solve-the-equation") or `"dpi"` ("direct plug-in").

### 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.

### Value

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

### References

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

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

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Springer.

### See Also

`density`.

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

### Examples

    data(precip)
    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)

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