View source: R/alfaridge.plot.R

Ridge regression with the alpha-transformation plot | 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 |

`lambda` |
A grid of values of the regularisation parameter |

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

` ridge.plot, alfa.ridge `

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) )

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