aknn.reg: The alpha-k-NN regression for compositional response data

In Compositional: Compositional Data Analysis

The \alpha-k-NN regression for compositional response data

Description

The \alpha-k-NN regression for compositional response data.

Usage

aknn.reg(xnew, y, x, a = seq(0.1, 1, by = 0.1), k = 2:10,
apostasi = "euclidean", rann = FALSE)

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.
k	The number of nearest neighbours to consider. It can be a single number or a vector.
apostasi	The type of distance to use, either "euclidean" or "manhattan".
rann	If you have large scale datasets and want a faster k-NN search, you can use kd-trees implemented in the R package "Rnanoflann". In this case you must set this argument equal to TRUE. Note however, that in this case, the only available distance is by default "euclidean".

Details

The \alpha-k-NN regression for compositional response variables is applied.

Value

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

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Tsagris M., Alenazi A. and Stewart C. (2023). Flexible non-parametric regression models for compositional response data with zeros. Statistics and Computing, 33(106).

https://link.springer.com/article/10.1007/s11222-023-10277-5

See Also

aknnreg.tune, akern.reg, alfa.reg, comp.ppr, comp.reg, kl.compreg

Examples

y <- as.matrix( iris[, 1:3] )
y <- y / rowSums(y)
x <- iris[, 4]
mod <- aknn.reg(x, y, x, a = c(0.4, 0.5), k = 2:3, apostasi = "euclidean")

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