ridge.plot: Ridge regression plot

In Compositional: Compositional Data Analysis

Ridge regression plot

Description

A plot of the regularised regression coefficients is shown.

Usage

ridge.plot(y, x, 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. Rows are samples and columns are features.
lambda	A grid of values of the regularisation parameter \lambda.

Details

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

Value

A plot with the values of the coefficients as a function of \lambda.

Author(s)

Michail Tsagris.

R implementation and documentation: Giorgos Athineou <[e-mail]> and Michail Tsagris [e-mail].

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.

See Also

ridge.reg, ridge.tune, alfa.ridge, alfaridge.plot

Examples

y <- as.vector(iris[, 1])
x <- as.matrix(iris[, 2:4])
ridge.plot(y, x, lambda = seq(0, 2, by = 0.1) )

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.