# EBICglasso.qgraph: 'EBICglasso' from 'qgraph' 1.4.4 In hfgolino/EGA: Exploratory Graph Analysis - A Framework for Estimating the Number of Dimensions in Multivariate Data Using Network Psychometrics

## Description

This function uses the `glasso` package (Friedman, Hastie and Tibshirani, 2011) to compute a sparse gaussian graphical model with the graphical lasso (Friedman, Hastie & Tibshirani, 2008). The tuning parameter is chosen using the Extended Bayesian Information criterium (EBIC) described by Foygel & Drton (2010).

## Usage

 ```1 2 3 4``` ```EBICglasso.qgraph(S, n, gamma = 0.5, penalize.diagonal = FALSE, nlambda = 100, lambda.min.ratio = 0.01, returnAllResults = FALSE, checkPD = TRUE, penalizeMatrix, countDiagonal = FALSE, refit = FALSE, ...) ```

## Arguments

 `S` A covariance or correlation matrix `n` Sample size used in computing `S` `gamma` EBIC tuning parameter. 0.5 is generally a good choice. Setting to zero will cause regular BIC to be used. `penalize.diagonal` Should the diagonal be penalized? `nlambda` Number of lambda values to test. `lambda.min.ratio` Ratio of lowest lambda value compared to maximal lambda `returnAllResults` If `TRUE` this function does not return a network but the results of the entire glasso path. `checkPD` If `TRUE`, the function will check if `S` is positive definite and return an error if not. It is not advised to use a non-positive definite matrix as input as (a) that can not be a covariance matrix and (b) glasso can hang if the input is not positive definite. `penalizeMatrix` Optional logical matrix to indicate which elements are penalized `countDiagonal` Should diagonal be counted in EBIC computation? Defaults to `FALSE`. Set to `TRUE` to mimic qgraph < 1.3 behavior (not recommended!). `refit` Logical, should the optimal graph be refitted without LASSO regularization? Defaults to `FALSE`. `...` Arguments sent to `glasso`

## Details

The glasso is run for 100 values of the tuning parameter logarithmically spaced between the maximal value of the tuning parameter at which all edges are zero, lambda_max, and lambda_max/100. For each of these graphs the EBIC is computed and the graph with the best EBIC is selected. The partial correlation matrix is computed using `wi2net` and returned.

## Value

A partial correlation matrix

## Author(s)

Sacha Epskamp <[email protected]>

## References

Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432-441. doi: 10.1093/biostatistics/kxm045

#glasso package Jerome Friedman, Trevor Hastie and Rob Tibshirani (2011). glasso: Graphical lasso-estimation of Gaussian graphical models. R package version 1.7. https://CRAN.R-project.org/package=glasso

Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. In Advances in neural information processing systems (pp. 604-612). https://papers.nips.cc/paper/4087-extended-bayesian-information-criteria-for-gaussian-graphical-models

#psych package Revelle, W. (2014) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA. R package version 1.4.4. https://CRAN.R-project.org/package=psych

#Matrix package Douglas Bates and Martin Maechler (2014). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.1-3. https://CRAN.R-project.org/package=Matrix

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```### Using wmt2 dataset from EGAnet ### data(wmt2) ## Not run: # Compute correlations: CorMat <- cor_auto(wmt2[,7:24]) # Compute graph with tuning = 0 (BIC): BICgraph <- EBICglasso.qgraph(CorMat, nrow(wmt2), 0) # Compute graph with tuning = 0.5 (EBIC) EBICgraph <- EBICglasso.qgraph(CorMat, nrow(wmt2), 0.5) ## End(Not run) ```

hfgolino/EGA documentation built on Aug. 16, 2019, 2:50 a.m.