| Title: | Fast Graphical LASSO |
|---|---|
| Description: | A fast and improved implementation of the graphical LASSO. |
| Authors: | Matyas A Sustik [aut, cph], Ben Calderhead [aut, cph], Julien Clavel [com, ctb] |
| Maintainer: | Julien Clavel <[email protected]> |
| License: | GPL (>=3) |
| Version: | 1.0 |
| Built: | 2024-11-09 05:07:33 UTC |
| Source: | https://github.com/jclavel/glassofast |
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.
| Package: | glassoFast |
| Type: | Package |
| Version: | 1.0.0 |
| Date: | 2017-06-07 |
| License: | GPL (>=3.0) |
The original FORTRAN Subroutine is available from:
http://www.cs.utexas.edu/users/sustik/glassofast/
Julien Clavel
Maintainer: Julien Clavel <[email protected]>
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.
This function is a faster alternative to the "glasso" function in the glasso package (Friedman et al. 2008). This package is based on the algorithm (FORTRAN subroutine) of Sustik and Calderhead (2012).
glassoFast(S, rho, thr = 1e-04, maxIt = 10000, start = c("cold", "warm"), w.init = NULL, wi.init = NULL, trace = FALSE)glassoFast(S, rho, thr = 1e-04, maxIt = 10000, start = c("cold", "warm"), w.init = NULL, wi.init = NULL, trace = FALSE)
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. |
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).
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 |
Julien Clavel
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.
glasso
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 fasterset.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