Description Usage Arguments Details Value Author(s) References See Also Examples
Function to fit least angle regression path of solution for the elastic net.
1 |
x |
N by p matrix of predictors |
y |
N-vector of outcome values |
lambda2 |
Value of L2 penalty parameter |
normalize |
Should columns of x be standardized? |
This function estimates the least angle regression path of solution for Ll-penalized (lasso) logistic regression
and the Cox proportional hazards model, using the R functions enpath
and coxpath
.
These latter functions use the predictor-corrector strategy devised by Park and Hastie (2007).
beta |
Matrix whose rows of contain the estimated coefficients for each lambda value |
larsobj |
Result of call to lars on augmented data |
mx |
Column means of x |
sdx |
Column standard deviations of x |
normalize |
Value of normalize argument in call to lars.en |
lambda |
Values of lambda used |
lambda2 |
Value of lambda2 used |
act |
Actions (predictor added) at each step |
maxp |
Maximum number of predictors entered |
call |
Call to lars.en |
Rob Tibshirani
Zou, H. and Hastie, Trevor (2005) Regularization and Variable Selection via the Elastic Net. JRSSB 301-320,
Park, M. Y. & Hastie, T. (2007). l1-regularization path algorithm for generalized linear models, Journal of the Royal Statistical Society Series B 69(4),
predict.lars, covTest
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