lm.ridge  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

data 
an optional data frame, list or environment in which to interpret the
variables occurring in 
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? Not implemented. 
x 
should the design matrix be returned? Not implemented. 
y 
should the response be returned? Not implemented. 
contrasts 
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 
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 
LW estimate of the ridge constant. 
Brown, P. J. (1994) Measurement, Regression and Calibration Oxford.
lm
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)))
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