Ridge regression with the alpha-transformation plot: Ridge regression plot
Description
A plot of the regularised regression coefficients is shown.
Usage
alfaridge.plot(y, x, a, lambda = seq(0, 5, by = 0.1) )
Arguments
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 \(\alpha\)-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 \(\lambda\).
Value
A plot with the values of the coefficients as a function of \(\lambda\).
Details
For every value of \(\lambda\) the coefficients are obtained. They are plotted versus the \(\lambda\) values.
References
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
# NOT RUN {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) )
# }