bootnet {bootnet} | R Documentation |

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.

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, ...)

`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 |

`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 |

`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 |

`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 |

`subNodes` |
Range of nodes to sample in node-drop bootstrap |

`caseMin` |
Minimum proportion of cases to drop when |

`caseMax` |
Maximum proportion of cases to drop when |

`caseN` |
Number of sampling levels to test when |

`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 |

`communities` |
Used for bridge centrality measures (see |

`useCommunities` |
Used for bridge centrality measures (see |

`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. |

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 |

`boots` |
A list of |

Sacha Epskamp <mail@sachaepskamp.com>

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212.

`estimateNetwork`

, `differenceTest`

, `corStability`

, `plot.bootnet`

, `summary.bootnet`

# 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)

[Package *bootnet* version 1.5 Index]