# alfaridge.plot: Ridge regression plot In Compositional: Compositional Data Analysis

## Description

A plot of the regularised regression coefficients is shown.

## Usage

 `1` ```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 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.

## Details

For every value of λ the coefficients are obtained. They are plotted versus the λ values.

## Value

A plot with the values of the coefficients as a function of λ.

## Author(s)

Michail Tsagris

R implementation and documentation: Giorgos Athineou <[email protected]> and Michail Tsagris <[email protected]>

## 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. http://arxiv.org/pdf/1106.1451.pdf

` ridge.plot, alfa.ridge `
 ```1 2 3 4 5``` ```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) ) ```