lars  R Documentation 
These are all variants of Lasso, and provide the entire sequence of coefficients and fits, starting from zero, to the least squares fit.
lars(x, y, type = c("lasso", "lar", "forward.stagewise", "stepwise"), trace = FALSE, normalize = TRUE, intercept = TRUE, Gram, eps = 1e12, max.steps, use.Gram = TRUE)
x 
matrix of predictors 
y 
response 
type 
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, with default 
max.steps 
Limit the number of steps taken; the default is 
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

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.
A "lars" object is returned, for which print, plot, predict, coef and summary methods exist.
Brad Efron and Trevor Hastie
Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics doi: 10.1214/009053604000000067; see also https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf. Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY.
print, plot, summary and predict methods for lars, and cv.lars
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)
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