smart (version 1.0.1)

TCE: Transellptical Correlation Estimation

Description

A function to conduct Transellptical Correlation Estimation

Usage

TCE(x, method, nlambda = NULL, lambda.min.ratio = NULL, lambda = NULL, verbose = TRUE)

Arguments

x

The n by d data matrix or d by d covariance matrix from the input

method

Method to be used to estimating the correlation matrix with 5 options: pearson, ns, npn, spearman and kendall. kendall as default.

nlambda

The number of regularization/thresholding paramters. The default value is 20.

lambda.min.ratio

The largest sparsity level for the estimated graphs. The default valye is 0.05.

lambda

A sequence of positive numbers for conducting thresholding.

verbose

If verbose = FALSE, tracing information printing is disabled. The default value is TRUE.

Value

An object with S3 class "TCE" is returned:

cov.input

An indicator of the sample covariance.

path

A list of k by k adjacency matrices of estimated graphs as a graph path corresponding to lambda.

sparsity

The sparsity levels of the graph path.

method

The method used in the correlation graph estimation stage.

lambda

The sequence of thresholding parameters used.

Details

The correlation graph is estimated by correlation cut-off based on the given thresholding level.

References

1.Han Liu, Fang Han, Ming Yuan, John Lafferty, Larry Wasserman. High dimensional semiparametric gaussian copula graphical models. Annals of Statistics, to apear. 2.Tuo Zhao and Han Liu. HUGE: A Package for High-dimensional Undirected Graph Estimation. Technical Report, Carnegie Mellon University, 2010

Examples

require(huge)
L = huge.generator(n = 200, d = 80, graph = "hub")
out = TCE(L$data,method="kendall")
out
plot(out)