NetworkToolbox (version 1.1.1)

bootgen: Bootstrapped Network Generalization

Description

Bootstraps the sample to identify the most stable correlations. Also produces a network that is penalizes low reliability edges. This function is useful for overcoming the structural constraint of the IFN approach

Usage

bootgen(data, method = c("MaST", "PMFG", "TMFG", "LoGo", "threshold"),
  alpha = 0.05, n = nrow(data), iter = 100, normal = FALSE,
  na.data = c("pairwise", "listwise", "fiml", "none"), seeds = NULL, cores,
  progBar = TRUE, ...)

Arguments

data

A set of data

method

A network filtering method. Defaults to "TMFG"

alpha

Alpha threshold bootstrapped network generalization network

n

Number of people to use in the bootstrap. Defaults to full sample size

iter

Number of bootstrap iterations. Defaults to 100 iterations

normal

Should data be transformed to a normal distribution? Defaults to FALSE. Data is not transformed to be normal. Set to TRUE if data should be transformed to be normal (computes correlations using the cor_auto function from the qgraph package)

na.data

How should missing data be handled? For "listwise" deletion the na.omit function is applied. Set to "fiml" for Full Information Maxmimum Likelihood (psych package). Full Information Maxmimum Likelihood is recommended but time consuming

seeds

Seeds to use for random number generation. Defaults to NULL. Input seeds from previous run (see examples)

cores

Number of computer processing cores to use for bootstrapping samples. Defaults to n - 1 total number of cores. Set to any number between 1 and maxmimum amount of cores on your computer

progBar

Should progress bar be displayed? Defaults to TRUE. Set to FALSE for no progress bar

...

Additional arguments for filtering methods

Value

Returns a list that includes the original filtered network (orignet), correlation matrix of the mean bootstrapped network (bootmat), unfiltered reliabilities of all of the connections (netrel), seeds used in the random number generator (Seeds)

References

Perez, M. E., & Pericchi, L. R. (2014). Changing statistical significance with the amount of information: The adaptive a significance level. Statistics & Probability Letters, 85, 20-24.

Tumminello, M., Coronnello, C., Lillo, F., Micciche, S., & Mantegna, R. N. (2007). Spanning trees and bootstrap reliability estimation in correlation-based networks. International Journal of Bifurcation and Chaos, 17(7), 2319-2329.

Examples

# NOT RUN {
bootTMFG<-bootgen(neoOpen)

#Replicates previous analysis
repbootTMFG<-bootgen(neoOpen, seeds = bootTMFG$Seeds)

bootLoGo<-bootgen(neoOpen,method="LoGo")

bootMaST<-bootgen(neoOpen,method="MaST")

bootThreshold<-bootgen(neoOpen,method="threshold")
# }