pcLasso v1.1


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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' <arXiv:1810.04651>.


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.

Functions in pcLasso

Name Description
cv.pcLasso Cross-validation for pcLasso
pcLasso Fit a model with principal components lasso
plot.cv.pcLasso Plot the cross-validation curve produced by "cv.pcLasso" object
predict.cv.pcLasso Make predictions from a "cv.pcLasso" object
predict.pcLasso Make predictions from a "pcLasso" object
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Type Package
License GPL-3
URL https://arxiv.org/abs/1810.04651
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2019-02-06 00:11:42 UTC; robtibshirani
Repository CRAN
Date/Publication 2019-02-06 12:30:03 UTC

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