semnetboot: Partial Bootstrapped Semantic Network Analysis

Description

Bootstraps (without replacement) the nodes in the network and computes global network characteristics

Usage

semnetboot(data, method = c("TMFG", "LoGo", "MaST", "threshold"),
  normal = FALSE, nodes, iter = 1000, na.data = c("pairwise", "listwise",
  "fiml", "none"), ...)

Arguments

data

A set of data

method

A network filtering method. Defaults to "TMFG"

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)

nodes

Number of nodes (i.e., variables) to use in the bootstrap. Defaults to 50 Otherwise accepts the number of the nodes to be included

iter

Number of bootstrap iterations. Defaults to 1000 iterations

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

...

Additional arguments for filtering methods

Value

Returns a list that includes the original semantic network measures (origmeas; ASPL, CC, Q, S), and the bootstrapped semantic network measures (bootmeas)

References

Kenett, Y. N., Wechsler-Kashi, D., Kenett, D. Y., Schwartz, R. G., Ben Jacob, E., & Faust, M. (2013). Semantic organization in children with cochlear implants: Computational analysis of verbal fluency. Frontiers in Psychology, 4(543), 1-11.

Examples

Run this code

# NOT RUN {
lowO <- subset(animals, Group==1)[-1]

semTMFG<-semnetboot(lowO)

semLoGo<-semnetboot(lowO,method="LoGo")

semMaST<-semnetboot(lowO,method="MaST")

semThreshold<-semnetboot(lowO,method="threshold")
# }