lm.ridge {MASS}  R Documentation 
Fit a linear model by ridge regression.
lm.ridge(formula, data, subset, na.action, lambda = 0, model = FALSE, x = FALSE, y = FALSE, contrasts = NULL, ...)
formula 
a formula expression as for regression models, of the form
response ~ predictors . See the documentation of formula
for other details. offset terms are allowed.

data 
an optional data frame in which to interpret the variables occurring
in formula .

subset 
expression saying which subset of the rows of the data should be used in the fit. All observations are included by default. 
na.action 
a function to filter missing data. 
lambda 
A scalar or vector of ridge constants. 
model 
should the model frame be returned? 
x 
should the design matrix be returned? 
y 
should the response be returned? 
contrasts 
a list of contrasts to be used for some or all of factor terms in the
formula. See the contrasts.arg of model.matrix.default .

... 
additional arguments to lm.fit .

If an intercept is present in the model, its coefficient is not penalized. (If you want to penalized 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
coef method.

scales 
scalings used on the X matrix. 
Inter 
was intercept included? 
lambda 
vector of lambda values 
ym 
mean of y

xm 
column means of x matrix

GCV 
vector of GCV values 
kHKB 
HKB estimate of the ridge constant. 
kLW 
LW estimate of the ridge constant. 
Brown, P. J. (1994) Measurement, Regression and Calibration Oxford.
data(longley) # not the same as the SPLUS dataset names(longley)[1] < "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)))