| Type: | Package |
| Title: | Fast Graphical LASSO |
| Version: | 1.0.1 |
| Date: | 2023-08-21 |
| Suggests: | glasso, rbenchmark |
| Author: | Matyas A Sustik [aut, cph], Ben Calderhead [aut, cph], Julien Clavel [com, ctb] |
| Maintainer: | Julien Clavel <julien.clavel@hotmail.fr> |
| Description: | A fast and improved implementation of the graphical LASSO. |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| Packaged: | 2023-08-21 11:15:08 UTC; julic5 |
| Repository: | CRAN |
| Date/Publication: | 2023-08-21 11:50:02 UTC |
glassoFast: a Fast Graphical LASSO
Description
This package propose a fast implementation of the graphical LASSO of Friedman et al. 2008 based on the algorithm (FORTRAN subroutine) of Sustik and Calderhead (2012). This algorithm also avoid non-termination issues observed for the "glasso" function of the R package glasso.
Details
| Package: | glassoFast |
| Type: | Package |
| Version: | 1.0.1 |
| Date: | 2017-06-07 |
| License: | GPL (>=3.0) |
The original FORTRAN Subroutine was taken from (now broken link):
http://www.cs.utexas.edu/users/sustik/glassofast/
Author(s)
Julien Clavel
Maintainer: Julien Clavel <julien.clavel@hotmail.fr>
References
Friedman J., Hastie T., Tibshirani R. 2008. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 9:432-441.
Sustik M.A., Calderhead B. 2012. GLASSOFAST: An efficient GLASSO implementation. UTCS Technical Report TR-12-29:1-3.
See Also
Fast graphical LASSO
Description
This function is a faster alternative to the "glasso" function in the glasso package (Friedman et al. 2008). This package uses the algorithm (FORTRAN subroutine) of Sustik and Calderhead (2012).
Usage
glassoFast(S, rho, thr = 1e-04, maxIt = 10000, start = c("cold", "warm"),
w.init = NULL, wi.init = NULL, trace = FALSE)
Arguments
S |
Covariance matrix (a p by p symmetric matrix) |
rho |
The regularization parameter for lasso. (a non-negative value or a p by p matrix of regularization parameters) |
thr |
Threshold for convergence. Default is 1.e-4. |
maxIt |
Maximum number of iterations of outer loop. Default is 10,000. |
start |
Type of start. Cold start is default. Using warm start, can provide starting values for w and wi. |
w.init |
Optional starting values for estimated covariance matrix (p by p). Only needed when start="warm" is specified |
wi.init |
Optional starting values for estimated inverse covariance matrix (p by p). Only needed when start="warm" is specified |
trace |
Flag for printing out information as iterations proceed. Default FALSE. |
Details
Estimate a sparse inverse covariance matrix using a lasso (L1) penalty, following the Friedman et al. (2008) approach. The function is a wrapper of the faster and corrected (for non-termination convergence issues) FORTRAN subroutine of Sustik and Calderhead (2012).
Value
w |
Estimated covariance matrix |
wi |
Estimated inverse covariance matrix |
errflag |
Memory allocation error flag: 0 means no error; !=0 means memory allocation error |
niter |
Number of iterations of outer loop |
Author(s)
Julien Clavel
References
Friedman J., Hastie T., Tibshirani R. 2008. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 9:432-441.
Sustik M.A., Calderhead B. 2012. GLASSOFAST: An efficient GLASSO implementation. UTCS Technical Report TR-12-29:1-3.
See Also
glasso
Examples
set.seed(100)
# Make a random covariance matrix
p=5
x<-matrix(rnorm(p*p),ncol=p)
s<- var(x)
# Compute the LASSO estimates
glassoFast(s, rho=.1)
# compare with glasso
require(glasso)
glasso(s, rho=.1)
# benchmark glassoFast and glasso
require(rbenchmark)
p=100
x<-matrix(rnorm(p*p),ncol=p)
s<- var(x)
benchmark(glassoFast(s, rho=.15), glasso(s, rho=.15), replications = 100)
# up to an order of magnitude faster