Computes the between- and within-community
`strength`

of each variable for each community

## Usage

```
net.loads(
A,
wc,
loading.method = c("BRM", "experimental"),
scaling = 2,
rotation = NULL,
...
)
```

## Arguments

- A
Network matrix, data frame, or

`EGA`

object- wc
Numeric or character vector (length =

`ncol(A)`

). A vector of community assignments. If input into`A`

is an`EGA`

object, then`wc`

is automatically detected- loading.method
Character (length = 1). Sets network loading calculation based on implementation described in

`"BRM"`

(Christensen & Golino, 2021) or an`"experimental"`

implementation. Defaults to`"BRM"`

- scaling
Numeric (length = 1). Scaling factor for the magnitude of the

`"experimental"`

network loadings. Defaults to`2`

.`10`

makes loadings roughly the size of factor loadings when correlations between factors are orthogonal- rotation
Character. A rotation to use to obtain a simpler structure. For a list of rotations, see

`rotations`

for options. Defaults to`NULL`

or no rotation. By setting a rotation,`scores`

estimation will be based on the rotated loadings rather than unrotated loadings- ...
Additional arguments to pass on to

`rotations`

## 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

- rotated
`NULL`

if`rotation = NULL`

; otherwise, a list containing the rotated standardized network loadings (`loadings`

) and correlations between dimensions (`Phi`

) from the rotation

## Details

Simulation studies have demonstrated that a node's strength centrality is roughly equivalent to factor loadings (Christensen & Golino, 2021; Hallquist, Wright, & Molenaar, 2019). Hallquist and colleagues (2019) 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; Christensen & Golino, 2021).

## References

**Original implementation and simulation**

Christensen, A. P., & Golino, H. (2021).
On the equivalency of factor and network loadings.
*Behavior Research Methods*, *53*, 1563-1580.

**Demonstration of node strength similarity to CFA loadings**

Hallquist, M., Wright, A. C. G., & Molenaar, P. C. M. (2019).
Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory.
*Multivariate Behavioral Research*, 1-25.

## Author

Alexander P. Christensen <alexpaulchristensen@gmail.com> and Hudson Golino <hfg9s at virginia.edu>

## Examples

```
# Load data
wmt <- wmt2[,7:24]
# Estimate EGA
ega.wmt <- EGA(
data = wmt,
plot.EGA = FALSE # No plot for CRAN checks
)
# Network loadings
net.loads(ega.wmt)
#> Loading Method: BRM
#>
#> 1 2
#> wmt2 0.384
#> wmt1 0.254
#> wmt3 0.217 0.131
#> wmt5 0.201 0.142
#> wmt4 0.188 0.14
#> wmt9 0.293
#> wmt7 0.258
#> wmt15 0.247
#> wmt14 0.243
#> wmt6 0.14 0.24
#> wmt16 0.22
#> wmt8 0.219
#> wmt10 0.166 0.206
#> wmt12 0.187
#> wmt18 0.183
#> wmt13 0.171
#> wmt17 0.168
#> wmt11 0.155
#> Standardized loadings >= |0.10| are displayed. To change this 'minimum', use `print(net.loads_object, minimum = 0.10)`
```