BootTable {EstimateGroupNetwork}

Summary table for a bootstrapped Joint Graphical Lasso model

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