print.select

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

Print the information about the model usage, graph dimension, model selection criterion, sparsity level of the optimal graph

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 graph structure
        is estimated by the Meinshausen & Buhlmann Graph Estimation via
        Lasso (MBGEL) 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. We also provide two alternative approaches for the
        graph estimation stage:(1) Graph Estimation via Correlation
        Thresholding (GECT) 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) RIC: Rotation Information
        Criterion (3) Extended Bayesian Information Criterion (EBIC
        only for GLASSO).

print.select: Print function for S3 class "select"

Description

Usage

Arguments

See Also