Ridge Regression

DESCRIPTION:

Fit a linear model by ridge regression.

USAGE:

lm.ridge(formula, data, subset, na.action, lambda = 0, model = F,
         x = F, y = F, contrasts=NULL, ...)

REQUIRED ARGUMENTS:

formula
a formula expression as for regression models, of the form response ~ predictors. See the documentation of formula for other details.

OPTIONAL ARGUMENTS:

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
...
additional arguments to lm.fit.

VALUE:

A list with components
coef
matrix of coefficients, one row for each value of lambda .
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
L-W estimate of the ridge constant.

REFERENCES:

Brown, P. J. (1994) Measurement, Regression and Calibration. Oxford.

SEE ALSO:

EXAMPLES:

longley <- data.frame(y = longley.y, longley.x)
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)))
# modified HKB estimator is 0.0042754
# modified L-W estimator is 0.032295
# smallest value of GCV  at 0.0028