BootTable: Summary table for a bootstrapped Joint Graphical Lasso model

In EstimateGroupNetwork: Perform the Joint Graphical Lasso and Selects Tuning Parameters

Description

Create a table of bootstrapped means and confidence intervals for all edges of a bootstrapped Joint Graphical Lasso model obtained through GroupNetworkBoot.

Usage

BootTable(BootOut)

Arguments

BootOut

The output from GroupNetworkBoot

Details

Summary table of the output of GroupNetworkBoot

Value

Var1	Nodes included in each edge
Var2	Nodes included in each edge
edges	Edge identifier
sample	sample value of each edge
boot.mean	mean of boostrapped values of each edge
ci.lb	lower bound of the .95 confidence interval
ci.ub	upper bound of the .95 confidence interval
boot.zero	proportion of bootstraps, in which an edge was estimated as equal to zero (i.e., 0= edge not estimated as zero throughout bootstraps; 1= edge estimated as zero in all bootstraps)
boot.pos	Proportion of bootstraps in which an edge was estimated as >0 (i.e., positive)
boot.neg	Proportion of bootstraps in which an edge was estimated as <0 (i.e., negative)
g	group in which the edge was estimated

Author(s)

Nils Kappelmann <[e-mail]>, Giulio Costantini

References

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1 Danaher, P., Wang, P., & Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 373–397. https://doi.org/10.1111/rssb.12033

See Also

JGL, qgraph, parcor

EstimateGroupNetwork documentation built on Feb. 10, 2021, 9:06 a.m.