Fit a linear model by ridge regression.
lm.ridge(formula, data, subset, na.action, lambda = 0, model = FALSE, x = FALSE, y = FALSE, contrasts = NULL, ...)
- a formula expression as for regression models, of the form
response ~ predictors. See the documentation of
formulafor other details.
offsetterms are allowed.
- an optional data frame in which to interpret the variables occurring
- expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
- a function to filter missing data.
- A scalar or vector of ridge constants.
- should the model frame be returned? Not implemented.
- should the design matrix be returned? Not implemented.
- should the response be returned? Not implemented.
- a list of contrasts to be used for some or all of factor terms in the
formula. See the
- additional arguments to
If an intercept is present in the model, its coefficient is not penalized. (If you want to penalize an intercept, put in your own constant term and remove the intercept.)
- A list with components
coef matrix of coefficients, one row for each value of
lambda. Note that these are not on the original scale and are for use by the
scales scalings used on the X matrix. Inter was intercept included? lambda vector of lambda values ym mean of
xm column means of
GCV vector of GCV values kHKB HKB estimate of the ridge constant. kLW L-W estimate of the ridge constant.
Brown, P. J. (1994) Measurement, Regression and Calibration Oxford.
longley # not the same as the S-PLUS dataset names(longley) <- "y" lm.ridge(y ~ ., longley) plot(lm.ridge(y ~ ., longley, lambda = seq(0,0.1,0.001))) select(lm.ridge(y ~ ., longley, lambda = seq(0,0.1,0.0001)))