EBICglasso.qgraph: 'EBICglasso' from 'qgraph' 1.4.4

In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

EBICglasso from qgraph 1.4.4

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 criterion (EBIC) described by Foygel & Drton (2010).

Usage

EBICglasso.qgraph(
  data,
  n = NULL,
  corr = c("auto", "cor_auto", "cosine", "pearson", "spearman"),
  na.data = c("pairwise", "listwise"),
  gamma = 0.5,
  penalize.diagonal = FALSE,
  nlambda = 100,
  lambda.min.ratio = 0.1,
  fast = FALSE,
  returnAllResults = FALSE,
  penalizeMatrix = NULL,
  countDiagonal = FALSE,
  refit = FALSE,
  model.selection = c("EBIC", "JSD"),
  verbose = FALSE,
  ...
)

Arguments

data	Matrix or data frame. Should consist only of variables to be used in the analysis
n	Numeric (length = 1). Sample size if data provided is a correlation matrix
corr	Character (length = 1). Method to compute correlations. Defaults to "auto". Available options: "auto" — Automatically computes appropriate correlations for the data using Pearson's for continuous, polychoric for ordinal, tetrachoric for binary, and polyserial/biserial for ordinal/binary with continuous. To change the number of categories that are considered ordinal, use ordinal.categories (see polychoric.matrix for more details) "cor_auto" — Uses cor_auto to compute correlations. Arguments can be passed along to the function "cosine" — Uses cosine to compute cosine similarity "pearson" — Pearson's correlation is computed for all variables regardless of categories "spearman" — Spearman's rank-order correlation is computed for all variables regardless of categories For other similarity measures, compute them first and input them into data with the sample size (n)
na.data	Character (length = 1). How should missing data be handled? Defaults to "pairwise". Available options: "pairwise" — Computes correlation for all available cases between two variables "listwise" — Computes correlation for all complete cases in the dataset
gamma	Numeric (length = 1) EBIC tuning parameter. Defaults to 0.50 and is generally a good choice. Setting to 0 will cause regular BIC to be used
penalize.diagonal	Boolean (length = 1). Should the diagonal be penalized? Defaults to FALSE
nlambda	Numeric (length = 1). Number of lambda values to test. Defaults to 100
lambda.min.ratio	Numeric (length = 1). Ratio of lowest lambda value compared to maximal lambda. Defaults to 0.1. NOTE qgraph sets the default to 0.01
fast	Boolean (length = 1). Whether the glassoFast version should be used to estimate the GLASSO. Defaults to FALSE. The fast results may differ by less than floating point of the original GLASSO implemented by glasso and should not impact reproducibility much
returnAllResults	Boolean (length = 1). Whether all results should be returned. Defaults to FALSE (network only). Set to TRUE to access glassopath output
penalizeMatrix	Boolean matrix. Optional logical matrix to indicate which elements are penalized
countDiagonal	Boolean (length = 1). Should diagonal be counted in EBIC computation? Defaults to FALSE. Set to TRUE to mimic qgraph < 1.3 behavior (not recommended!)
refit	Boolean (length = 1). Should the optimal graph be refitted without LASSO regularization? Defaults to FALSE
model.selection	Character (length = 1). How lambda should be selected within GLASSO. Defaults to "EBIC". "JSD" is experimental and should not be used otherwise
verbose	Boolean (length = 1). Whether messages and (insignificant) warnings should be output. Defaults to FALSE (silent calls). Set to TRUE to see all messages and warnings for every function call
...	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; for maintanence, Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen at gmail.com>

References

Instantiation of GLASSO
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432-441.

glasso + EBIC
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. In Advances in neural information processing systems (pp. 604-612).

glasso package
Friedman, J., Hastie, T., & Tibshirani, R. (2011). glasso: Graphical lasso-estimation of Gaussian graphical models. R package version 1.7.

Tutorial on EBICglasso
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634.

Examples

# Obtain data
wmt <- wmt2[,7:24]

# Fast
fast <- EBICglasso.qgraph(wmt)

# Regular
regular <- EBICglasso.qgraph(wmt, fast = FALSE)

# Difference between fast and regular
sum(abs(fast - regular))

# Compute graph with tuning = 0 (BIC)
BICgraph <- EBICglasso.qgraph(data = wmt, gamma = 0)

# Compute graph with tuning = 0.5 (EBIC)
EBICgraph <- EBICglasso.qgraph(data = wmt, gamma = 0.5)

EGAnet documentation built on April 11, 2025, 6:11 p.m.