Compositional (version 5.0)

The alpha-kernel regression for compositional response data: The \(\alpha\)-kernel regression for compositional response data

Description

The \(\alpha\)-kernel regression for compositional response data.

Usage

akern.reg( xnew, y, x, a = seq(0.1, 1, by = 0.1), h = seq(0.1, 1, length = 10) )

Arguments

xnew

A matrix with the new predictor variables whose compositions are to be predicted.

y

A matrix with the compositional response data. Zeros are allowed.

x

A matrix with the available predictor variables.

a

The value(s) of \(\alpha\). Either a single value or a vector of values. As zero values in the compositional data are allowed, you must be careful to choose strictly positive vcalues of \(\alpha\). However, if negative values are passed, the positive ones are used only.

h

The bandwidth value(s) to consider.

Value

A list with the estimated compositional response data for each value of \(\alpha\) and h.

Details

The \(\alpha\)-kernel regression for compositional response variables is applied.

References

Michail Tsagris, Abdulaziz Alenazi and Connie Stewart (2021). Non-parametric regression models for compositional data. https://arxiv.org/pdf/2002.05137.pdf

See Also

akernreg.tune, aknn.reg, aknnreg.tune, alfa.reg, comp.ppr, comp.reg, kl.compreg

Examples

# NOT RUN {
y <- as.matrix( iris[, 1:3] )
y <- y / rowSums(y)
x <- iris[, 4]
mod <- akern.reg( x, y, x, a = c(0.4, 0.5), h = c(0.1, 0.2) )
# }