Ridge regression coefficients plot {MXM}

Ridge regression

Description

A plot of the regularised parameters is shown.

Usage

ridge.plot(target, dataset, lambda = seq(0, 5, by = 0.1) ) 

Arguments

target

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 log( target/(1 - target) ). In any case, they must be continuous only.

dataset

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]>, Vincenzo Lagani <[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, ridgereg.cv

Examples

#simulate a dataset with continuous data
dataset <- matrix( runif(300 * 20, 1, 20), nrow = 300 ) 
#the target feature is the last column of the dataset as a vector
target <- dataset[, 20]
dataset <- dataset[, -20]
ridge.plot(target, dataset)