summary.glassoM

An object with S3 class <code>"glassoM"</code>

object

Summary information about the model usage and the solution path length

The package "huge" provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing (Gaussianization), neighborhood screening,
        graph estimation, and model selection techniques into a
        pipeline. In preprocessing stage, the NonparaNormal(NPN)
        transformation is applied to help relax the normality
        assumption. In the graph estimation stage, the structure of
        either the whole graph or a pre-specified sub-graph is
        estimated by the Meinshausen & Buhlmann Graph Estimation via
        Lasso (GEL) by default and it can be further accelerated by the
        Graph SURE Screening (GSS) subroutine which preselects the
        graph neighborhood of each variable. In the case d >> n, the
        computation is memory optimized and is targeted on larger-scale
        problems (with d>2000). We also provide two alternative
        approaches for the graph estimation stage:(1) Graph
        Approximation via Correlation Thresholding (GACT) which is
        highly efficient and (2) A slightly modified Graphical Lasso
        (GLASSO) procedure in which the memory usage is optimized using
        sparse matrix output. Three regularization/thresholding
        parameter selection methods are included in this package: (1)
        StARS: Stability Approach for Regularization Selection (2) PIC:
        Permutation Information Criterion (3) Extended Bayesian
        Information Criterion (EBIC).

summary.glassoM: Summary function for S3 class "glassoM"

Description

Usage

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See Also