NetworkToolbox (version 1.1.2)

nams: Network Adjusted Mean/Sum

Description

The hybrid centrality is used to adjust the mean or sum score of participant's factor scores based on each node's centrality. Each participant's response values are multipled by the corresponding hybrid centrality value (uses "random" for BC argument). In this way, more central nodes contribute a greater score and less central nodes contribute a lesser score

Usage

nams(data, A, normal = FALSE, standardize = TRUE, adjusted = c("mean",
  "sum"), factors = c("walktrap", "louvain"), method = c("TMFG", "LoGo",
  "EBICglasso", "IsingFit", "threshold"), na.data = c("pairwise", "listwise",
  "fiml", "none"), ...)

Arguments

data

Must be a dataset

A

Adjacency matrix that has already been filtered

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)

standardize

Should mean/sum scores be standardized? Defaults to TRUE. Set to FALSE for unstandardized mean/sum scores

adjusted

Should adjusted values be the mean or sum score? Defaults to "mean". Set to "sum" for sum scores

factors

Can be a vector of factor assignments or community detection algorithms ("walktrap" or "louvain") can be used to determine the number of factors. Defaults to 1 factor. Set to "walktrap" for the walktrap algortihm. Set to "louvain" for louvain community detection

method

A network filtering method. Defaults to "TMFG". The "threshold" option is set to the default arguments (thresh = "alpha", a = .05). To use additional arguments for "threshold", apply threshold function with desired arguments and input into argument A

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 community detection algorithms

Value

Returns a list of the network adjusted score (NetAdjScore) and the items associated with the specified or identified factors (FactorItems)

Examples

# NOT RUN {
sumadj <- nams(neoOpen, adjusted = "sum")

knownfactors <- nams(neoOpen,
factors = c(rep(1,8),rep(2,8),rep(3,8),rep(4,8),rep(5,8),rep(6,8)))

walkadj <- nams(neoOpen, method="threshold", factors = "walktrap")

# }