huge.glasso: The graphical lasso (glasso) using sparse matrix output

In huge: High-Dimensional Undirected Graph Estimation

Description

See more details in huge

Usage

huge.glasso(
  x,
  lambda = NULL,
  lambda.min.ratio = NULL,
  nlambda = NULL,
  scr = NULL,
  cov.output = FALSE,
  verbose = TRUE
)

Arguments

x	There are 2 options: (1) x is an n by d data matrix (2) a d by d sample covariance matrix. The program automatically identifies the input matrix by checking the symmetry. (n is the sample size and d is the dimension).
lambda	A sequence of decreasing positive numbers to control the regularization when method = "mb", "glasso" or "tiger", or the thresholding in method = "ct". Typical usage is to leave the input lambda = NULL and have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Users can also specify a sequence to override this. When method = "mb", "glasso" or "tiger", use with care - it is better to supply a decreasing sequence values than a single (small) value.
lambda.min.ratio	If method = "mb", "glasso" or "tiger", it is the smallest value for lambda, as a fraction of the upperbound (MAX) of the regularization/thresholding parameter which makes all estimates equal to 0. The program can automatically generate lambda as a sequence of length = nlambda starting from MAX to lambda.min.ratio*MAX in log scale. If method = "ct", it is the largest sparsity level for estimated graphs. The program can automatically generate lambda as a sequence of length = nlambda, which makes the sparsity level of the graph path increases from 0 to lambda.min.ratio evenly.The default value is 0.1 when method = "mb", "glasso" or "tiger", and 0.05 method = "ct".
nlambda	The number of regularization/thresholding parameters. The default value is 30 for method = "ct" and 10 for method = "mb", "glasso" or "tiger".
scr	If scr = TRUE, the lossy screening rule is applied to preselect the neighborhood before the graph estimation. The default value is FALSE. NOT applicable when method = "ct", "mb", or "tiger".
cov.output	If cov.output = TRUE, the output will include a path of estimated covariance matrices. ONLY applicable when method = "glasso". Since the estimated covariance matrices are generally not sparse, please use it with care, or it may take much memory under high-dimensional setting. The default value is FALSE.
verbose	If verbose = FALSE, tracing information printing is disabled. The default value is TRUE.

See Also

huge, and huge-package.

huge documentation built on July 1, 2021, 1:06 a.m.