glassoFast (version 1.0.1)

glassoFast: 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)

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

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.

Author

Julien Clavel

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).

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
# \donttest{
 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
	# }