glasso {glasso} | R Documentation |
Estimates a sparse inverse covariance matrix using a lasso (L1) penalty
glasso(s, rho, thr=1.0e-4,maxit=1e4, approx=FALSE, penalize.diagonal=TRUE, start=c("cold","warm"), w.init=NULL,wi.init=NULL, trace=FALSE)
s |
Covariance matrix:p by p matrix (symmetric) |
rho |
(Non-negative) regularization parameter for lasso. rho=0 means no regularization. Can be a scalar (usual) or a symmetric p by p matrix, or a vector of length p. In the latter case, the penalty matrix has jkth element sqrt(rho[j]*rho[k]). |
thr |
Threshold for convergence. Default value is 1e-4. Iterations stop when average absolute parameter change is less than thr * ave(abs(offdiag(s))) |
maxit |
Maximum number of iterations of outer loop. Default 10,000 |
approx |
Approximation flag: if true, computes Meinhausen-Buhlmann(2006) approximation |
penalize.diagonal |
Should diagonal of inverse covariance be penalized? Dafault TRUE. |
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 |
Estimates a sparse inverse covariance matrix using a lasso (L1) penalty, using the approach of Friedman, Hastie and Tibshirani (2007). The Meinhausen-Buhlmann (2006) approximation is also implemented.
A list with components
w |
Estimated covariance matrix |
wi |
Estimated inverse covariance matrix |
loglik |
Value of maximized log-likelihodo+penalty |
errflag |
Memory allocation error flag: 0 means no error; !=0 means memory allocation error - no output returned |
approx |
Value of input argument approx |
del |
Change in parameter value at convergence |
niter |
Number of iterations of outer loop used by algorithm |
Jerome Friedman, Trevor Hastie and Robert Tibshirani (2007). Sparse inverse covariance estimation with the lasso. Biostatistics 2007. http://www-stat.stanford.edu/~tibs/ftp/graph.pdf
Meinshausen, N. and B"{u}hlmann, P.(2006) High dimensional graphs and variable selection with the lasso. Annals of Statistics,34, p1436-1462.
set.seed(100) x<-matrix(rnorm(50*20),ncol=20) s<- var(x) a<-glasso(s, rho=.01) aa<-glasso(s,rho=.02, w.init=a$w, wi.init=a$wi)