alfaknnreg.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 predictor variables

R Documentation

Cross validation for the `\alpha`-k-NN regression with compositional predictor variables

Description

Cross validation for the \alpha-k-NN regression with compositional predictor variables.

Usage

alfaknnreg.tune(y, x, a = seq(-1, 1, by = 0.1), k = 2:10, nfolds = 10,
apostasi = "euclidean", method = "average", folds = NULL, seed = NULL, graph = FALSE)

Arguments

`y`	The response variable, a numerical vector.
`x`	A matrix with the available compositional data. Zeros are allowed.
`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.
`nfolds`	The number of folds. Set to 10 by default.
`apostasi`	The type of distance to use, either "euclidean" or "manhattan".
`method`	If you want to take the average of the reponses of the k closest observations, type "average". For the median, type "median" and for the harmonic mean, type "harmonic".
`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.
`graph`	If graph is TRUE (default value) a filled contour plot will appear.

Details

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

Value

A list including:

`mspe`	The mean square error of prediction.
`performance`	The minimum mean square error of prediction.
`opt_a`	The optimal value of `\alpha`.
`opt_k`	The optimal value of k.
`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

library(MASS)
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
y <- fgl[, 1]
mod <- alfaknnreg.tune(y, x, a = seq(0.2, 0.4, by = 0.1), k = 2:4, nfolds = 5)

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.