aknnreg.tune: Cross validation for the alpha-k-NN regression with...

In Compositional: Compositional Data Analysis

Cross validation for the \alpha-k-NN regression with compositional response data

Description

Cross validation for the \alpha-k-NN regression with compositional response data.

Usage

aknnreg.tune(y, x, a = seq(0.1, 1, by = 0.1), k = 2:10, apostasi = "euclidean",
nfolds = 10, folds = NULL, seed = NULL, rann = FALSE)

Arguments

y	A matrix with the compositional response data. Zeros are allowed.
x	A matrix with the available predictor variables.
a	A vector with a grid of values of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If \alpha=0 the isometric log-ratio transformation is applied.
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".
nfolds	The number of folds. Set to 10 by default.
folds	If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
seed	You can specify your own seed number here or leave it NULL.
rann	If you have large scale datasets and want a faster k-NN search, you can use kd-trees implemented in the R package "RANN". 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

A k-fold cross validation for the \alpha-k-NN regression for compositional response data is performed.

Value

A list including:

kl	The Kullback-Leibler divergence for all combinations of \alpha and k.
js	The Jensen-Shannon divergence for all combinations of \alpha and k.
klmin	The minimum Kullback-Leibler divergence.
jsmin	The minimum Jensen-Shannon divergence.
kl.alpha	The optimal \alpha that leads to the minimum Kullback-Leibler divergence.
kl.k	The optimal k that leads to the minimum Kullback-Leibler divergence.
js.alpha	The optimal \alpha that leads to the minimum Jensen-Shannon divergence.
js.k	The optimal k that leads to the minimum Jensen-Shannon divergence.
runtime	The runtime of the cross-validation procedure.

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(5): 1–17.

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

See Also

aknn.reg, akernreg.tune, akern.reg, alfa.rda, alfa.fda, rda.tune

Examples

y <- as.matrix( iris[, 1:3] )
y <- y / rowSums(y)
x <- iris[, 4]
mod <- aknnreg.tune(y, x, a = c(0.4, 0.6), k = 2:4, nfolds = 5)

Compositional documentation built on Oct. 23, 2023, 5:09 p.m.