| 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.
|
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.
|
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. |
Brown, P. J. (1994) Measurement, Regression and Calibration Oxford.
data(longley)
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)))
# modified HKB estimator is 0.0042754
# modified L-W estimator is 0.032295
# smallest value of GCV at 0.0028