eip: Edge Inclusion "Probability"

In donaldRwilliams/GGMnonreg: Non-Regularized Gaussian Graphical Models

Description

Compute the proportion of bootstrap samples that each relation was selected, corresponding to an edge inclusion "probability".

Usage

eip(Y, method = "pearson", B = 1000, progress = TRUE)

Arguments

Y	The data matrix of dimensions n (observations) by p (nodes).
method	Character string. Which type of correlation coefficients to be computed. Options include "pearson" (default), "kendall", "spearman", and "polychoric".
B	Integer. Number of bootstrap replicates (defaults to 1000).
progress	Logical. Should a progress bar be included (defaults to TRUE)?

Details

The order is the upper-triangular.

Value

An object of class eip, including a matrix of edge inclusion "probabilities".

Note

In the context of regression, this general approach was described in see Figure 6.4. \insertCite@see Figure 6.4, @Hastie2015;textualGGMnonreg. In this case, the selection is based on classical hypothesis testing instead of L1-regularization.

References

\insertAllCited

Examples

# data
Y <- ptsd

# eip
fit_eip <- eip(Y, method = "spearman")

# print
fit_eip

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