Description Usage Arguments Details Value Note References Examples
Data mining to learn the graph.
1 2 3 4 5 6 7  ggm_search(
x,
IC = "BIC",
type = "neighborhood_selection",
method = "forward",
n = NULL
)

x 
A data matrix of dimensions n (observations) by p (nodes) or a correlation matrix of dimensions p by p. 
IC 
Character string. The desired information criterion. Options include

type 
Character string. Which search method should be used? The options included

method 
Character string. The desired subset selection method
Options includes 
n 
Integer. Sample size. Required if a correlation matrix is provided. 
type = "neighborhood_selection"
was described in
\insertCitewilliams2019nonregularized;textualGGMnonreg
and type = "approx_L0"
was described in \insertCitewilliams2020beyond;textualGGMnonreg.
The penalty for type = "approx_L0"
is called seamless L0 \insertCitedicker2013variableGGMnonreg
An object of class ggm_search
including:
wadj: Weighted adjacency matrix, corresponding to the partial correlation network.
adj: Adjacency matrix (detected effects).
pcors: Partial correlations.
n: Sample size.
p: Number of nodes.
Y: Data.
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 (nonregularized)
best subset model selection. This is accomplished by using regularization, but
the penalty (approximately) mimics nonregularized estimation.
1 2 3 4 5  # data
Y < ptsd
# search data
fit < ggm_search(Y)

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