huge.ct: Graph estimation via correlation thresholding (ct)

In huge: High-Dimensional Undirected Graph Estimation

Graph estimation via correlation thresholding (ct)

Description

See more details in huge

Usage

huge.ct(
  x,
  nlambda = NULL,
  lambda.min.ratio = NULL,
  lambda = NULL,
  verbose = TRUE,
  input.type = "auto"
)

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).
nlambda	The number of regularization/thresholding parameters. The default value is 20 for method = "ct" and 10 for method = "mb", "glasso" or "tiger".
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".
lambda	A numeric scalar or non-empty one-dimensional numeric input of finite, non-negative thresholds. Values are applied in the supplied order, and zero is allowed. Leave lambda = NULL to generate a path from nlambda and lambda.min.ratio.
verbose	If verbose = FALSE, tracing information printing is disabled. The default value is TRUE.
input.type	How to interpret x: "auto" preserves symmetry-based detection, "data" forces an observation matrix, and "covariance" requires a square covariance or correlation matrix.

Details

The default path targets increasing numbers of undirected edges and defines each graph by the strict rule abs(correlation) > lambda. Equal-weight edges are never split, so ties can make the realized sparsity smaller than the nominal target. Reusing the returned lambda values therefore reconstructs the same path. When lambda = NULL, nlambda must be a positive integer and lambda.min.ratio must lie in (0, 1]. Supplying lambda overrides those two arguments.

See Also

huge, and huge-package.

huge documentation built on Aug. 4, 2026, 5:09 p.m.