ising_search: Ising: automated search

In donaldRwilliams/GGMnonreg: Non-Regularized Gaussian Graphical Models

Description

Data mining to learn the graph of binary variables with an Ising model \insertCitelenz1920beitrvsge,ising1925beitragGGMnonreg.

Usage

ising_search(Y, IC = "BIC", progress = TRUE)

Arguments

Y	A data matrix of dimensions n (observations) by p (nodes).
IC	Character string. The desired information criterion. Options include "AIC" and "BIC" (default).
progress	Logical. Should a progress bar be included (defaults to TRUE)?

Details

Currently only backwards selection is currently implemented.

Value

An object of class ising_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.

Note

For an excellent overview of the Ising model see \insertCitemarsman2018introduction;textualGGMnonreg.

References

\insertAllCited

Examples

# data
Y <- ifelse( ptsd[,1:5] == 0, 0, 1)

# search data
fit <- ising_search(Y)

donaldRwilliams/GGMnonreg documentation built on Nov. 13, 2021, 9:57 a.m.