One of "lasso", "lar", "forward.stagewise" or "stepwise". The names can
be abbreviated to any unique substring. Default is "lasso".
trace
If TRUE, lars prints out its progress
normalize
If TRUE, each variable is standardized to have unit L2 norm, otherwise
it is left alone. Default is TRUE.
intercept
if TRUE, an intercept is included in the model (and not penalized),
otherwise no intercept is included. Default is TRUE.
Gram
The X'X matrix; useful for repeated runs (bootstrap) where a large X'X
stays the same.
eps
An effective zero
max.steps
Limit the number of steps taken; the default is 8 * min(m,
n-intercept), with m the number of variables, and n the number of samples.
For type="lar" or type="stepwise", the maximum number of steps is
min(m,n-intercept). For type="lasso" and especially
type="forward.stagewise", there can be many more terms, because
although no more than min(m,n-intercept) variables can be active during
any step, variables are frequently droppped and added as the algorithm
proceeds. Although the default usually guarantees that the algorithm
has proceeded to the saturated fit, users should check.
use.Gram
When the number m of variables is very large, i.e. larger than N, then
you may not want LARS to precompute the Gram matrix. Default is use.Gram=TRUE
Details
LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani
(2002). With the "lasso" option, it computes the complete lasso
solution simultaneously for ALL values of the shrinkage parameter in
the same computational cost as a least squares fit. A "stepwise" option
has recently been added to LARS.
Value
A "lars" object is returned, for which print, plot, predict, coef and summary
methods exist.
Author(s)
Brad Efron and Trevor Hastie
References
Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression"
(with discussion) Annals of Statistics; see also http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf.
Hastie, Tibshirani and Friedman (2002) Elements of Statistical
Learning, Springer, NY.
See Also
print, plot, summary and predict methods for lars, and cv.lars
Examples
data(diabetes)
par(mfrow=c(2,2))
attach(diabetes)
object <- lars(x,y)
plot(object)
object2 <- lars(x,y,type="lar")
plot(object2)
object3 <- lars(x,y,type="for") # Can use abbreviations
plot(object3)
detach(diabetes)