See more details in huge
huge.glasso(
x,
lambda = NULL,
lambda.min.ratio = NULL,
nlambda = NULL,
scr = NULL,
cov.output = FALSE,
verbose = TRUE,
input.type = "auto"
)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).
A numeric scalar or non-empty one-dimensional numeric input
defining a finite, strictly positive, non-increasing regularization path;
tied values are allowed. Leave lambda = NULL
to generate a path from nlambda and 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 when method = "ct".
The number of regularization/thresholding parameters. The default value is 20 for method = "ct" and 10 for method = "mb", "glasso" or "tiger".
If scr = TRUE, the lossy screening rule is applied to preselect the neighborhood before the graph estimation. The default value is FALSE.
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.
If verbose = FALSE, tracing information printing is disabled. The default value is TRUE.
How to interpret x: "auto" preserves
symmetry-based detection, "data" forces an observation matrix,
and "covariance" requires a square covariance or correlation
matrix. For covariance input with lambda = NULL, "auto"
retains the historical diagonal-sensitive lambda scale; use
"covariance" for a scale based only on off-diagonal entries.
Indefinite pairwise covariance estimates are accepted only when
regularization produces a certified positive-definite result.
huge, and huge-package.