LCT {EGAnet}  R Documentation 
An algorithm to identify whether data were generated from a factor or network model using factor and network loadings. The algorithm uses heuristics based on theory and simulation. These heuristics were then submitted to several deep learning neural networks with 240,000 samples per model with varying parameters.
LCT(
data,
n,
iter = 100,
dynamic = FALSE,
dynamic.args = list(n.embed = 4, tau = 1, delta = 1, use.derivatives = 1)
)
data 
Matrix or data frame.
A data frame with the variables to be used in the test or a correlation matrix.
If the data used is a correlation matrix, the argument 
n 
Integer. Sample size (if the data provided is a correlation matrix) 
iter 
Integer.
Number of replicate samples to be drawn from a multivariate
normal distribution (uses 
dynamic 
Boolean.
Is the dataset a time series where rows are time points and
columns are variables?
Defaults to 
dynamic.args 
List.
Arguments to be used in

Returns a list containing:
empirical 
Prediction of model based on empirical dataset only 
bootstrap 
Prediction of model based on means of the loadings across the bootstrap replicate samples 
proportion 
Proportions of models suggested across bootstraps 
Hudson F. Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen at gmail.com>
Christensen, A. P., & Golino, H. (2021). Factor or network model? Predictions from neural networks. Journal of Behavioral Data Science, 1(1), 85126.
# Compute LCT
## Network model
LCT(data = wmt2[,7:24])
## Factor model
LCT(data = psychTools::bfi[,1:25])
# Dynamic LCT
LCT(sim.dynEGA[sim.dynEGA$ID == 1,1:20], dynamic = TRUE)