Principal Components Lasso

A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' .

Bug fixes:

  • predict.pcLasso now works when family = “binomial” (previously, the intercept term was being added in an incorrect manner).
  • Previously, standardize = TRUE scaled the beta coefficients and intercept a0 incorrectly. This has been fixed.
  • pcLasso now generates lambda values for the objective function RSS/(2n) + penalty, instead of that for RSS/2 + penalty.


Reference manual

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1.1 by Rob Tibshirani, 3 months ago

Browse source code at

Authors: Jerome Friedman , Kenneth Tay , Robert Tibshirani

Documentation:   PDF Manual  

GPL-3 license

Imports svd

Suggests knitr, rmarkdown

See at CRAN