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

Cross validation for the alpha-k-NN regression with compositional response data {Compositional}

R Documentation

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 "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

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 mtsagris@uoc.gr.

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

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)

[Package Compositional version 6.9 Index]

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