The alpha-k-NN regression with compositional predictor variables | R Documentation |
The α-k-NN regression with compositional predictor variables.
alfa.knn.reg(xnew, y, x, a = 1, k = 2:10, apostasi = "euclidean", method = "average")
xnew |
A matrix with the new compositional predictor variables whose response is to be predicted. Zeros are allowed. |
y |
The response variable, a numerical vector. |
x |
A matrix with the available compositional predictor variables. Zeros are allowed. |
a |
A single value of α. As zero values in the compositional data are allowed, you must be careful to choose strictly positive vcalues of α. If negative values are passed, the positive ones are used only. If the data are already alpha-transformed, you can make this NULL. |
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". |
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". |
The α-k-NN regression with compositional predictor variables is applied.
A matrix with the estimated response data for each value of k.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Michail Tsagris, Abdulaziz Alenazi and Connie Stewart (2020). Non-parametric regression models for compositional data. https://arxiv.org/pdf/2002.05137.pdf
aknn.reg, alfa.knn, alfa.pcr, alfa.ridge
library(MASS) x <- as.matrix(fgl[, 2:9]) x <- x / rowSums(x) y <- fgl[, 1] mod <- alfa.knn.reg(x, y, x, a = 0.5, k = 2:4)
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