EBICglasso.qgraph {EGAnet} | R Documentation |

`EBICglasso`

from `qgraph`

1.4.4This 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).

```
EBICglasso.qgraph(
data,
n = NULL,
gamma = 0.5,
penalize.diagonal = FALSE,
nlambda = 100,
lambda.min.ratio = 0.01,
returnAllResults = FALSE,
penalizeMatrix,
countDiagonal = FALSE,
refit = FALSE,
...
)
```

`data` |
Data matrix |

`n` |
Number of participants |

`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 |

`penalizeMatrix` |
Optional logical matrix to indicate which elements are penalized |

`countDiagonal` |
Should diagonal be counted in EBIC computation?
Defaults to |

`refit` |
Logical, should the optimal graph be refitted without LASSO regularization?
Defaults to |

`...` |
Arguments sent to |

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.

A partial correlation matrix

Sacha Epskamp <mail@sachaepskamp.com>

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

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

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

#psych package Revelle, W. (2014) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA. R package version 1.4.4.

#Matrix package Douglas Bates and Martin Maechler (2014). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.1-3.

```
### Using wmt2 dataset from EGAnet ###
data(wmt2)
# Compute correlations:
CorMat <- qgraph::cor_auto(wmt2[,7:24])
# Compute graph with tuning = 0 (BIC):
BICgraph <- EBICglasso.qgraph(CorMat, n = nrow(wmt2), gamma = 0)
# Compute graph with tuning = 0.5 (EBIC)
EBICgraph <- EBICglasso.qgraph(CorMat, n = nrow(wmt2), gamma = 0.5)
```

[Package *EGAnet* version 1.1.0 Index]