bootnet: Bootstrapped network estimation

In bootnet: Bootstrap Methods for Various Network Estimation Routines

Bootstrapped network estimation

Description

This function can be used to bootstrap network estimation methods so that the spread of parameter and centrality estimates can be assessed. Most important methods are type = 'nonparametric' for the non-parametric bootstrap and type = 'case' for the case-dropping bootstrap. See also Epskamp, Borsboom and Fried (2016) for more details.

Usage

bootnet(data, nBoots = 1000, default = c("none", "EBICglasso",
                 "ggmModSelect", "pcor", "IsingFit", "IsingSampler",
                 "huge", "adalasso", "mgm", "relimp", "cor", "TMFG",
                 "ggmModSelect", "LoGo", "SVAR_lavaan", "GGMncv"), type
                 = c("nonparametric", "parametric", "node", "person",
                 "jackknife", "case"), nCores = 1, statistics =
                 c("edge", "strength", "outStrength", "inStrength"),
                 model = c("detect", "GGM", "Ising", "graphicalVAR"),
                 fun, verbose = TRUE, labels, alpha = 1, caseMin =
                 0.05, caseMax = 0.75, caseN = 10, subNodes, subCases,
                 computeCentrality = TRUE, propBoot = 1, replacement =
                 TRUE, graph, sampleSize, intercepts, weighted, signed,
                 directed, includeDiagonal = FALSE, communities,
                 useCommunities, bridgeArgs = list(), library =
                 .libPaths(), memorysaver = TRUE, ...)

Arguments

data	A data frame or matrix containing the raw data. Must be numeric, integer or ordered factors.
nBoots	Number of bootstraps
default	A string indicating the method to use. See documentation at estimateNetwork.
type	The kind of bootstrap method to use.
nCores	Number of cores to use in computing results. Set to 1 to not use parallel computing.
statistics	Vector indicating which statistics to store. Options are: "edge" Edge-weight "strength" Degree or node-strength "outStrength" Out-degree or Out-strength "inStrength" In-degree or In-strength "expectedInfluence" Expected Influence "outExpectedInfluence" Outgoing expected influence "inExpectedInfluence" Incoming expected influence "bridgeInDegree" Bridge in-degree (see bridge) "bridgeOutnDegree" Bridge out-degree (see bridge) "bridgeStrength" Bridge-strength (see bridge) "bridgeCloseness" Bridge-closeness (see bridge) "bridgeBetweenness" Bridge-betweenness (see bridge) "rspbc" Randomized shortest paths betweenness centrality (see rspbc) "hybrid" Hybrid centrality (see hybrid) "eigenvector" Eigenvector centrality (see eigenvector) Can contain "edge", "strength", "closeness", "betweenness", "length", "distance", "expectedInfluence", "inExpectedInfluence", "outExpectedInfluence". By default, length and distance are not stored.
model	The modeling framework to use. Automatically detects if data is binary or not.
fun	A custom estimation function, when no default set is used. This must be a function that takes the data as input (first argument) and returns either a weights matrix or a list containing the elements "graph" for the weights matrix, "intercepts" for the intercepts (optional) and "results" for the full estimation results (optional).
verbose	Logical. Should progress of the function be printed to the console?
labels	A character vector containing the node labels. If omitted the column names of the data are used.
alpha	The centrality tuning parameter as used in centrality.
subNodes	Range of nodes to sample in node-drop bootstrap
caseMin	Minimum proportion of cases to drop when type = "case".
caseMax	Maximum proportion of cases to drop when type = "case".
caseN	Number of sampling levels to test when type = "case".
subCases	Range of persons to sample in person-drop bootstrap
computeCentrality	Logical, should centrality be computed?
propBoot	Proportion of persons to sample in bootstraps. Set to lower than 1 for m out of n bootstrap
replacement	Logical, should replacement be used in bootstrap sampling?
graph	A given network structure to use in parametric bootstrap.
sampleSize	The samplesize to use in parametric bootstrap.
intercepts	Intercepts to use in parametric bootstrap.
weighted	Logical, should the analyzed network be weighted?
signed	Logical, should the analyzed network be signed?
directed	Logical, is the analyzed network directed? Usually does not have to be set and is detected automatically.
includeDiagonal	Logical, should diagonal elements (self-loops) be included in the bootstrap? Only used when directed = TRUE.
communities	Used for bridge centrality measures (see bridge).
useCommunities	Used for bridge centrality measures (see bridge).
library	Library location to be used in parallel computing.
memorysaver	Logical. If TRUE (recommended) then raw bootstrapped data and results are not stored in the output object. This saves a lot of memory. Set this only to TRUE if you need the raw results or bootstrap data.
bridgeArgs	List of arguments used in the 'bridge' function for computing bridge centrality
...	Additional arguments used in the estimator function.

Value

A bootnet object with the following elements:

sampleTable	A data frame containing all estimated values on the real sample.
bootTable	A data frame containing all estimated values on all bootstrapped samples.
sample	A bootnetResult object with plot and print method containing the estimated network of the real sample.
boots	A list of bootnetResult objects containing the raw bootstrap results.

Author(s)

Sacha Epskamp <[e-mail]>

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.

See Also

estimateNetwork, differenceTest, corStability, plot.bootnet, summary.bootnet

Examples

# BFI Extraversion data from psychTools package:
library("psychTools")
data(bfi)
bfiSub <- bfi[,1:25]

# Estimate network:
Network <- estimateNetwork(bfiSub, default = "EBICglasso")

# Centrality indices:
library("qgraph")
centralityPlot(Network)


# Estimated network:
plot(Network, layout = 'spring') 

### Non-parametric bootstrap ###
# Bootstrap 1000 values, using 8 cores:
Results1 <- bootnet(Network, nBoots = 1000, nCores = 8)

# Plot bootstrapped edge CIs:
plot(Results1, labels = FALSE, order = "sample")

# Plot significant differences (alpha = 0.05) of edges:
plot(Results1, "edge", plot = "difference",onlyNonZero = TRUE,
     order = "sample") 

# Plot significant differences (alpha = 0.05) of node strength:
plot(Results1, "strength", plot = "difference")

# Test for difference in strength between node "A1" and "C2":
differenceTest(Results1,  "A1", "C2", "strength")

### Case-drop bootstrap ###
# Bootstrap 1000 values, using 8 cores:
Results2 <- bootnet(Network, nBoots = 1000, nCores = 8, 
              type = "case")

# Plot centrality stability:
plot(Results2)

# Compute CS-coefficients:
corStability(Results2)

bootnet documentation built on Oct. 4, 2023, 5:09 p.m.