Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/ElasticNet_HJBiplot.R
This function is a generalization of the Ridge regularization method and the LASSO penalty. Realizes the representation of the SPARSE HJ Biplot through a combination of LASSO and Ridge, on the data matrix. This means that with this function you can eliminate weak variables completely as with the LASSO regularization or contract them to zero as in Ridge.
1 | ElasticNet_HJBiplot(X, Lambda = 1e-04, Alpha = 1e-04, Transform.Data = 'scale')
|
X |
array_like; |
Lambda |
float; |
Alpha |
float; |
Transform.Data |
character; |
Algorithm used to perform automatic selection of variables and continuous contraction simultaneously. With this method, the model obtained is simpler and more interpretable. It is a particularly useful method when the number of variables is much greater than the number of observations.
ElasticNet_HJBiplot
returns a list containing the following components:
loadings |
array_like; |
n_ceros |
array_like; |
coord_ind |
array_like; |
coord_var |
array_like; |
eigenvalues |
array_like; |
explvar |
array_like; |
Mitzi Cubilla-Montilla, Carlos Torres-Cubilla, Ana Belen Nieto Librero and Purificacion Galindo Villardon
Galindo, M. P. (1986). Una alternativa de representacion simultanea: HJ-Biplot. Questiio, 10(1), 13-23.
Erichson, N. B., Zheng, P., Manohar, K., Brunton, S. L., Kutz, J. N., & Aravkin, A. Y. (2018). Sparse principal component analysis via variable projection. arXiv preprint arXiv:1804.00341.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), 67(2), 301-320.
1 | ElasticNet_HJBiplot(mtcars, Lambda = 0.2, Alpha = 0.1)
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