# 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

 `1` ```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.

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## Examples

 ```1 2 3 4 5 6 7 8``` ```# 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.