Ridge regression plot {Compositional} | R Documentation |
A plot of the regularised regression coefficients is shown.
ridge.plot(y, x, 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. Rows are samples and columns are features. |
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.
ridge.reg, ridge.tune, alfa.ridge, alfaridge.plot
y <- as.vector(iris[, 1]) x <- as.matrix(iris[, 2:4]) ridge.plot(y, x, lambda = seq(0, 2, by = 0.1) )