Compositional (version 5.5)

Ridge regression: Ridge regression

Description

Ridge regression.

Usage

ridge.reg(y, x, lambda, B = 1, xnew = NULL)

Arguments

y

A real valued vector. If it contains percentages, the logit transformation is applied.

x

A matrix with the predictor variable(s), they have to be continuous.

lambda

The value of the regularisation parameter \(\lambda\).

B

If B = 1 (default value) no bootstrpa is performed. Otherwise bootstrap standard errors are returned.

xnew

If you have new data whose response value you want to predict put it here, otherwise leave it as is.

Value

A list including:

beta

The beta coefficients.

seb

The standard eror of the coefficiens. If B > 1 the bootstrap standard errors will be returned.

est

The fitted or the predicted values (if xnew is not NULL).

Details

This is used in the function alfa.ridge. There is also a built-in function available from the MASS library, called lm.ridge.

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.tune, alfa.ridge, ridge.plot

Examples

# NOT RUN {
y <- as.vector(iris[, 1])
x <- as.matrix(iris[, 2:4])
mod1 <- ridge.reg(y, x, lambda = 0.1)
mod2 <- ridge.reg(y, x, lambda = 0)
# }