See more details in huge
huge.tiger(
x,
lambda = NULL,
nlambda = NULL,
lambda.min.ratio = NULL,
sym = "or",
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
the path from nlambda and lambda.min.ratio in C++.
The number of regularization/thresholding parameters. The default value is 20 for method = "ct" and 10 for method = "mb", "glasso" or "tiger".
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".
Symmetrize the output graphs. If sym = "and", the edge between node i and node j is selected ONLY when both node i and node j are selected as neighbors for each other. If sym = "or", the edge is selected when either node i or node j is selected as the neighbor for each other. The default value is "or". ONLY applicable when method = "mb" or "tiger".
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. Correlation construction, covariance validation, and automatic
lambda selection then occur together in C++.
Raw observations are centered and normalized, while covariance
input is converted to a correlation matrix, inside the shared C++ core.
When lambda = NULL, the same native correlation matrix determines
the returned default lambda path. A user-supplied path must be finite,
strictly positive, and non-increasing; tied values are allowed and are
used unchanged. If smaller generated values cannot be certified to the solver's
KKT tolerance, the function warns and returns the longest certified path
prefix. A user-supplied value that cannot be certified raises an error.
huge, and huge-package.