Compositional (version 5.5)

Multivariate kernel density estimation for compositional data: Multivariate kernel density estimation for compositional data

Description

Multivariate kernel density estimation for compositional data.

Usage

comp.kern(x, type= "alr", h = NULL, thumb = "silverman")

Arguments

x

A matrix with Euclidean (continuous) data.

type

The type of trasformation used, either the additive log-ratio ("alr"), the isometric log-ratio ("ilr") or the pivot coordinate ("pivot") transformation.

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 it is 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, leave the "h" NULL and put "estim" for maximum likelihood cross-validation, "scott" or "silverman" for Scott's and Silverman's rules of thumb respectively.

Value

A vector with the density estimates calculated for every vector.

Details

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

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

comp.kerncontour, mkde

Examples

# NOT RUN {
x <- as.matrix(iris[, 1:3])
x <- x / rowSums(x)
f <- comp.kern(x)
# }