mkde: Multivariate kernel density estimation

In Compositional: Compositional Data Analysis

Multivariate kernel density estimation

Description

Multivariate kernel density estimation.

Usage

mkde(x, h = NULL, thumb = "silverman")

Arguments

x	A matrix with Euclidean (continuous) data.
h	The bandwidh value. It can be a single value, which is turned into a vector and then into a diagonal matrix, or a vector which is turned into a diagonal matrix. If you put this NULL then you need to specify the "thumb" argument below.
thumb	Do you want to use a rule of thumb for the bandwidth parameter? If no, set h equal to NULL and put "estim" for maximum likelihood cross-validation, "scott" or "silverman" for Scott's and Silverman's rules of thumb respectively.

Details

The multivariate kernel density estimate is calculated with a (not necssarily given) bandwidth value.

Value

A vector with the density estimates calculated for every vector.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail] and Giorgos Athineou <[e-mail]>.

References

Arsalane Chouaib Guidoum (2015). Kernel Estimator and Bandwidth Selection for Density and its Derivatives. The kedd R package.

M.P. Wand and M.C. Jones (1995). Kernel smoothing, pages 91-92.

B.W. Silverman (1986). Density estimation for statistics and data analysis, pages 76-78.

See Also

mkde.tune, comp.kerncontour

Examples

mkde( as.matrix(iris[, 1:4]), thumb = "scott" )
mkde( as.matrix(iris[, 1:4]), thumb = "silverman" )

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.