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Ridge regression.
ridge.reg(y, x, lambda, B = 1, xnew = NULL)
A real valued vector. If it contains percentages, the logit transformation is applied.
A matrix with the predictor variable(s), they have to be continuous.
The value of the regularisation parameter \(\lambda\).
If B = 1 (default value) no bootstrpa is performed. Otherwise bootstrap standard errors are returned.
If you have new data whose response value you want to predict put it here, otherwise leave it as is.
A list including:
The beta coefficients.
The standard eror of the coefficiens. If B > 1 the bootstrap standard errors will be returned.
The fitted or the predicted values (if xnew is not NULL).
This is used in the function alfa.ridge. There is also a built-in function available from the MASS library, called lm.ridge.
alfa.ridge
lm.ridge
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.tune, alfa.ridge, ridge.plot
# 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) # }
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