Cross validation for the \(\alpha\)-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)The response variable, a numerical vector.
A matrix with the available compositional data. Zeros are allowed.
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.
The number of nearest neighbours to consider. It can be a single number or a vector.
The type of distance to use, either "euclidean" or "manhattan".
The number of folds. Set to 10 by default.
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
If seed is TRUE the results will always be the same.
A list including:
The Kullback-Leibler divergence for all combinations of \(\alpha\) and k.
The Jensen-Shannon divergence for all combinations of \(\alpha\) and k.
The minimum Kullback-Leibler divergence.
The minimum Jensen-Shannon divergence.
The optimim \(\alpha\) that leads to the minimum Kullback-Leibler divergence.
The optimim k that leads to the minimum Kullback-Leibler divergence.
The optimim \(\alpha\) that leads to the minimum Jensen-Shannon divergence.
The optimim k that leads to the minimum Jensen-Shannon divergence.
The runtime of the cross-validation procedure.
A k-fold cross validation for the \(\alpha\)-k-NN regression for compositional response data is performed.
Michail Tsagris, Abdulaziz Alenazi and Connie Stewart (2020). The alpha-k-NN- regression for compositional data. https://arxiv.org/pdf/2002.05137.pdf
# NOT RUN {
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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