| Cross validation for the alpha-k-NN regression with compositional predictor variables {Compositional} | 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 |
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 |
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
See Also
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)