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).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.