Cross validation for the alpha-k-NN regression with compositional response data | R Documentation |
Cross validation for the α-k-NN regression with 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 = NULL, 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 |
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". |
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 optimal α 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 α 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. |
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Michail Tsagris, Abdulaziz Alenazi and Connie Stewart (2021). Non-parametric regression models for compositional data. https://arxiv.org/pdf/2002.05137.pdf
aknn.reg, akernreg.tune, akern.reg, alfa.rda, alfa.fda, rda.tune
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)
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