mixed_search: Mixed Graphical Model: automated search

In donaldRwilliams/GGMnonreg: Non-Regularized Gaussian Graphical Models

Description

Data mining to learn the graph.

Usage

mixed_search(Y, data_type = NULL, IC = "BIC")

Arguments

Y	A data matrix of dimensions n (observations) by p (nodes)
data_type	Vector of length p. The type of data, with options of "b" (binary), "p" (Poisson), and "g" (Gaussian).
IC	Character string. The desired information criterion. Options include "AIC" and "BIC" (default).

Details

Only backwards selection is currently implemented. Only an adjacency matrix is provided.

Value

An object of class mixed_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.

Examples

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

# search data (ising model)
fit <- mixed_search(Y, data_type = rep("b", 5))

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