Ridge regression plot

Description

A plot of the regularised regression coefficients is shown.

Usage

1
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 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 <athineou@csd.uoc.gr> and Michail Tsagris <mtsagris@yahoo.gr>

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

1
2
3
y <- iris[, 1]
x <- iris[, 2:4]
ridge.plot(y, x, lambda = seq(0, 2, by = 0.1) )

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