ggm_inference: Gaussian graphical model: statistical inference

In GGMnonreg: Non-Regularized Gaussian Graphical Models

Description

Learn the conditional dependence structure with null hypothesis significance testing. This provides a valid measure of parameter uncertainty.

Usage

ggm_inference(
  Y,
  alpha = 0.05,
  control_precision = FALSE,
  boot = TRUE,
  B = 1000,
  cores = 2,
  method = "pearson",
  progress = TRUE
)

Arguments

Y	The data matrix of dimensions n (observations) by p (nodes).
alpha	The desired significance level (defaults to 0.05). Note that 1 - alpha corresponds to specificity.
control_precision	Logical. Should precision (i.e., 1 - false discovery rate) be controlled at the level alpha (defaults to FALSE) ?
boot	Logical. Should a non-parametric bootstrap be employed (defaults to TRUE)?
B	Integer. Number of bootstrap replicates (defaults to 1000).
cores	Integer. Number of cores to be used when executing in parallel.
method	Character string. Which type of correlation coefficients to be computed. Options include "pearson" (default), "kendall", "spearman", and "polychoric".
progress	Logical. Should a progress bar be included (defaults to TRUE)?

Value

An object of class ggm_inference

Examples

Y <- ptsd

fit <- ggm_inference(Y)

GGMnonreg documentation built on April 8, 2021, 5:06 p.m.