Compositional: aknnreg.tune – R documentation

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aknnreg.tune

Cross validation for the α-k-NN regression for compositional response data

Cross validation for the α-k-NN regression for compositional response data.

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

`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 α=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`	If seed is TRUE the results will always be the same.
`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".

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

A list including:

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

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Michail Tsagris, Abdulaziz Alenazi and Connie Stewart (2020). The alpha-k-NN regression for compositional data. https://arxiv.org/pdf/2002.05137.pdf

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 Data Analysis

v4.6

GPL (>= 2)

Authors

Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]

Initial release

2021-04-27