ggm_inference: Gaussian graphical model: statistical inference

Description Usage Arguments Value Examples

View source: R/ggm_inference.R

Description

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

Usage

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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

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Y <- ptsd

fit <- ggm_inference(Y)

donaldRwilliams/GGMnonreg documentation built on May 13, 2021, 11:57 a.m.