NetworkToolbox (version 1.1.2)

commboot: Bootstrapped Communities Likelihood

Description

Bootstraps the sample with replace to compute walktrap reliability

Usage

commboot(data, normal = FALSE, n = nrow(data), iter = 1000,
  filter = c("TMFG", "threshold", "EBICglasso", "IsingFit"),
  method = c("louvain", "walktrap"), na.data = c("pairwise", "listwise",
  "fiml", "none"), steps = 4, cores, ...)

Arguments

data

A set of data

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

n

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

iter

Number of bootstrap iterations. Defaults to 100 iterations

filter

Set filter method. Defaults to "TMFG". See EBICglasso and IsingFit for additional arguments

method

Defaults to "walktrap". Set to "louvain" for the louvain community detection algorithm

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 (corFiml). Full Information Maxmimum Likelihood is recommended but time consuming

steps

Number of steps to use in the walktrap algorithm. Defaults to 4. Use a larger number of steps for smaller networks

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

...

Additional arguments for network filtering methods

Value

Returns the number of factors and their relative frequency found across bootstrapped samples

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.

Examples

# NOT RUN {
commTMFG<-commboot(neoOpen)

commThreshold<-commboot(neoOpen,filter="threshold")
# }