Mkex: Kernel Density Estimation

In ShanLu92/FeaAug: SVM with Feature Augmentation

Description

The function Mkex and Mkey return results of kernel density estimates about x and y.

Usage

Mkex(m, yname, kernel = "gaussian")

Mkey(m, yname, kernel = "gaussian")

Arguments

m	dataset that needs to be estimated.
yname	the name of result-column.
kernel	a character string giving the smoothing kernel to be used. This must partially match one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian", and may be abbreviated to a unique prefix (single letter).

Details

For dataset m, the default algorithm use density( ) to disperses the mass of the empirical distribution function over a regular grid of at least 512 points and then uses the fast Fourier transform to convolve this approximation with a discretized version of the kernel and then uses linear approximation to evaluate the density at the specified points.

Value

Mkex gives the n coordinates of the points where the density is estimated. Mkey gives the estimated density values. These will be non-negative, but can be zero.

Source

For density, based on

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole (for S version).

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. New York: Springer.

Examples

intest = subnum(rawdata, k=10)[[1]]
test = rawdata[intest,]
training = rawdata[-intest,]
groups = subgroup(training, 'class', c('attack', 'normal'))
d0 = groups[[1]]
d1 = groups[[2]]
denx0 = Mkex(d0, yname)
deny0 = Mkey(d0, yname)

ShanLu92/FeaAug documentation built on Jan. 31, 2021, 7:21 p.m.