Data mining to learn the graph.
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A data matrix of dimensions n (observations) by p (nodes) or a correlation matrix of dimensions p by p.
Character string. The desired information criterion. Options include
Character string. Which search method should be used? The options included
Character string. The desired subset selection method
Integer. Sample size. Required if a correlation matrix is provided.
type = "neighborhood_selection" was described in
type = "approx_L0" was described in \insertCitewilliams2020beyond;textualGGMnonreg.
The penalty for
type = "approx_L0" is called seamless L0 \insertCitedicker2013variableGGMnonreg
An object of class
wadj (weighted adjacency matrix)
adj (adjacency matrix).
type = "neighborhood_selection" employs multiple regression to estimate
the graph (requires the data), whereas
type = "approx_L0" directly estimates
the precision matrix (data or a correlation matrix are acceptable). If
data is provided and
type = "approx_L0", by default Pearson correlations are
used. For another correlation coefficient, provide the desired correlation matrix.
type = "approx_L0" is a continuous approximation to (non-regularized)
best subset model selection. This is accomplished by using regularization, but
the penalty (approximately) mimics non-regularized estimation.
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