EGAnet (version 0.9.6)

net.loads: Network Loadings

Description

Computes the between- and within-community strength of each item for each community. This function uses the comcat and stable functions to calculate the between- and within-community strength of each item, respectively.

Usage

net.loads(A, wc, pos.manifold = FALSE, min.load = 0, plot = FALSE)

Arguments

A

Matrix, data frame, or EGA object. An adjacency matrix of network data

wc

Numeric or character vector. A vector of community assignments. If input into A is an EGA object, then wc is automatically detected

pos.manifold

Boolean. Should a positive manifold be applied (i.e., should all dimensions be positively correlated)? Defaults to FALSE. Set to TRUE for a positive manifold

min.load

Numeric. Sets the minimum loading allowd in the standardized network loading matrix. Values equal or greater than the minimum loading are kept in the output. Values less than the minimum loading are removed. This matrix can be viewed using print() or summary() Defaults to 0

plot

Boolean. Should proportional loadings be plotted? Defaults to FALSE. Set to TRUE for plot with pie charts visualizing the proportion of loading associated with each dimension

Value

Returns a list containing:

unstd

A matrix of the unstandardized within- and between-community strength values for each node

std

A matrix of the standardized within- and between-community strength values for each node

minLoad

The minimum loading to appear in summary of network loadings. Use print() or summary() to view

plot

A qgraph plot of the network loadings. Use plot to view

Details

Simulation studies have demonstrated that a node's strength centrality is roughly equivalent to factor loadings (Christensen, Golino, & Silvia, 2019; Hallquist, Wright, & Molenaar, in press). Hallquist and colleagues (in press) found that node strength represented a combination of dominant and cross-factor loadings. This function computes each node's strength within each specified dimension, providing a rough equivalent to factor loadings (including cross-loadings).

For more details, type vignette("Network_Scores")

References

Christensen, A. P., & Golino, H. (2020). On the equivalency of factor and network loadings. PsyArXiv. doi:10.31234/osf.io/xakez

Christensen, A. P., Golino, H., & Silvia, P. (2019). A psychometric network perspective on the measurement and assessment of personality traits. PsyArXiv. doi:10.31234/osf.io/ktejp

Hallquist, M., Wright, A. C. G., & Molenaar, P. C. M. (in press). Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory. Multivariate Behavioral Research. doi:10.31234/osf.io/pg4mf

Examples

# NOT RUN {
# Load data
wmt <- wmt2[,7:24]

# }
# NOT RUN {
# Estimate EGA
ega.wmt <- EGA(wmt)

# }
# NOT RUN {
# Network loadings
net.loads(ega.wmt)

# }