Ridge regression
Ridge regression.
ridge.reg(y, x, lambda, B = 1, xnew = NULL)
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 λ. |
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. |
This is used in the function alfa.ridge. There is also a built-in function available from the MASS library, called lm.ridge.
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). |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>
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.
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)
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