Generate True Model Parameters

Description

Generate matrices of true model parameters for the supported true models. These matrices are intended to passed to the model_matrix argument of the powerly function.

Usage

generate_model(type, ...)

Arguments

Name	Description
type	Character string representing the type of true model. See the True Models section for possible values.
...	Required arguments used for the generation of the true model. See the True Models section for the arguments required for each type of true model.

Return

A matrix containing the model parameters.

True Models

Gaussian Graphical Model

Type: ggm

... arguments:

Name	Description
nodes	A single positive integer representing the number of nodes in the network (e.g., 10).
density	A single numerical value indicating the density of the network (e.g., 0.4).
positive	A single numerical value representing the proportion of positive edges in the network (e.g., 0.9 for $90\%$ positive edges).
range	A length two numerical value indicating the uniform interval from where to sample values for the partial correlations coefficients (e.g., c(0.5, 1)).
constant	A single numerical value representing the constant described by Yin and Li (2011).

Note. For more information see the arguments of the genGGM function in the bootnet package.

Compatible performance measures:

• sen (sensitivity)
• spe (specificity)
• mcc (Matthews correlation)
• rho (Pearson correlation)

See the Performance Measures section for the powerly function for more information on the compatible performance measures.

Examples

The example below shows how to generate a true network model based on a random architecture (Barabási & Albert, 1999) with $10$ nodes, $90\%$ positive edge weights, and an edge density of $0.4$.

# Generate true model.
true_model <- generate_model(
    type = "ggm",
    nodes = 10,
    density = 0.4,
    positive = 0.9
)

# Load the `qgraph` package.
library(qgraph)

# Plot the model.
qgraph(true_model)

See Also

Functions powerly and validate.

References

Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509

Yin, J., & Li, H. (2011). A sparse conditional Gaussian graphical model for analysis of genetical genomics data. The Annals of Applied Statistics, 5(4), 2630–2650. https://doi.org/10.1214/11-AOAS494