glassoFast: Fast graphical LASSO

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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).

Usage

1
2
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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
	

glassoFast documentation built on May 2, 2019, 3:41 p.m.