Description Usage Arguments Details Value Author(s) References
Computes the coefficient estimates for lasso-penalized linear regression.
1 2 |
X |
nxp data matrix. |
Y |
nxr matrix of response values |
lam |
tuning parameter for lasso regularization term. Defaults to |
crit |
criterion for convergence. Criterion |
tol |
tolerance for algorithm convergence. Defaults to 1e-4.. |
maxit |
maximum iterations. Defaults to 1e4 |
For details on the implementation of 'GLASSO', see the vignette https:#mgallow.github.io/GLASSO/.
returns list of returns which includes:
Call |
function call. |
Iterations |
number of iterations. |
Loss |
value of the objective function. |
Coefficients |
estimated regression coefficients. |
Matt Galloway gall0441@umn.edu
For more information on the coordinate descent algorithm, see:
Friedman, Jerome, et al. 'Pathwise coordinate optimization.' The Annals of
Applied Statistics 1.2 (2007): 302-332.
https://arxiv.org/pdf/0708.1485.pdf
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.