path_to_network

input \(p \times p\) weights matrix

variance-covariance matrix for the residuals. If NULL (the default) will impose
the constraint that the variables have variance 1 and the residuals are uncorrelated

Function returns the precision ("precision") or covariance ("covariance") matrix

output

Takes a path model and generates the corresponding (standardized) precision matrix or
covariance matrix. The inverse of <code><a rd-options="" href="/link/network_to_path?package=SEset&version=1.0.1" data-mini-rdoc="SEset::network_to_path">network_to_path</a></code>.

Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix.
This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools.
The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone.
The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.

Oisc3<ad>n Ryan

SEset

Computing Statistically-Equivalent Path Models

path_to_network function

Takes a path model and generates the corresponding (standardized) precision matrix or
covariance matrix. The inverse of <code><a rd-options='' href='network_to_path'>network_to_path</a></code>.

path_to_network: Precision matrix from ordered path model

Description

Usage

Arguments

Value

References

See Also