Ridge regression with the alpha-transformation plot {Compositional} | R Documentation |
A plot of the regularised regression coefficients is shown.
alfaridge.plot(y, x, a, lambda = seq(0, 5, by = 0.1) )
y |
A numeric vector containing the values of the target variable. If the values are proportions or percentages, i.e. strictly within 0 and 1 they are mapped into R using the logit transformation. In any case, they must be continuous only. |
x |
A numeric matrix containing the continuous variables. |
a |
The value of the α-transformation. It has to be between -1 and 1. If there are zero values in the data, you must use a strictly positive value. |
lambda |
A grid of values of the regularisation parameter λ. |
For every value of λ the coefficients are obtained. They are plotted versus the λ values.
A plot with the values of the coefficients as a function of λ.
Michail Tsagris.
R implementation and documentation: Giorgos Athineou <gioathineou@gmail.com> and Michail Tsagris mtsagris@uoc.gr.
Hoerl A.E. and R.W. Kennard (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1): 55-67.
Brown P. J. (1994). Measurement, Regression and Calibration. Oxford Science Publications.
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
library(MASS) y <- as.vector(fgl[, 1]) x <- as.matrix(fgl[, 2:9]) x <- x / rowSums(x) alfaridge.plot(y, x, a = 0.5, lambda = seq(0, 5, by = 0.1) )