ggmFit: Obtain fit measures of a Gaussian graphical model

In qgraph: Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Obtain fit measures of a Gaussian graphical model

Description

Obtain fit measures of a given Gaussian graphical model (GGM). Input can be either a partial correlation matrix, inverse covariance matrix or qgraph object.

Usage

ggmFit(pcor, covMat, sampleSize, refit = TRUE, ebicTuning = 0.5, 
        nPar, invSigma, tol = sqrt(.Machine$double.eps), verbose = TRUE,
        countDiagonalPars = TRUE)

Arguments

pcor	Implied partial correlation matrix or qgraph object.
covMat	Observed variance-covariance matrix
sampleSize	The sample size used in computing the variance-covariance matrix
refit	Logical, should the network be refitted using glasso?
ebicTuning	EBIC tuning parameter.
invSigma	Implied inverse variance-covariance matrix. If this object is assigned pcor is not used.
nPar	Number of parameters, if not specified this is retrieved from the number of zeroes in the inverse variance–covariance matrix. Can be used to compute fit measures of any statistical model (e.g., SEM).
tol	Tolerance for setting an edge to zero.
verbose	Logical, should progress reports be printed to the console?
countDiagonalPars	Logical, should the diagonal of the precision matrix be counted as parameters?

Author(s)

Sacha Epskamp <[e-mail]>

Examples

library("psych")

# Load BFI data:
data(bfi)
bfi <- bfi[,1:25]

# Covariance matrix:
CovMat <- cov(bfi[,1:25], use="pairwise.complete.obs")

# Compute network:
EBICgraph <- qgraph(CovMat, graph = "glasso", sampleSize = nrow(bfi),
                    tuning = 0.5, layout = "spring", title = "BIC", details = TRUE)

# Obtain fit measures:
fitNetwork <- ggmFit(EBICgraph, CovMat, nrow(bfi))
fitNetwork

qgraph documentation built on Nov. 3, 2023, 5:07 p.m.