Ridge regression with compositional data in the covariates side using the alpha-transformation | R Documentation |
\alpha
-transformation
Ridge regression with compositional data in the covariates side using the \alpha
-transformation.
alfa.ridge(y, x, a, lambda, B = 1, xnew = NULL)
y |
A numerical vector containing the response variable values. If they are percentages, they are mapped onto |
x |
A matrix with the predictor variables, the compositional data. Zero values are allowed, but you must be careful to choose strictly positive vcalues of |
a |
The value 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 |
lambda |
The value of the regularisation parameter, |
B |
If B > 1 bootstrap estimation of the standard errors is implemented. |
xnew |
A matrix containing the new compositional data whose response is to be predicted. If you have no new data, leave this NULL as is by default. |
The \alpha
-transformation is applied to the compositional data first and then ridge components regression is performed.
The output of the ridge.reg.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>.
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
ridge.reg, alfaridge.tune, alfaridge.plot
library(MASS)
y <- as.vector(fgl[, 1])
x <- as.matrix(fgl[, 2:9])
x <- x/ rowSums(x)
mod1 <- alfa.ridge(y, x, a = 0.5, lambda = 0.1, B = 1, xnew = NULL)
mod2 <- alfa.ridge(y, x, a = 0.5, lambda = 1, B = 1, xnew = NULL)
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