LCT: Loadings Comparison Test

Description Usage Arguments Value Author(s) References Examples

Description

An algorithm to identify whether data were generated from a random, 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.

Usage

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LCT(data, n, iter = 100)

Arguments

data

Matrix or data frame. A dataframe with the variables to be used in the test or a correlation matrix. If the data used is a correlation matrix, the argument n will need to be specified

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 mvtnorm::mvrnorm). Defaults to 100

Value

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

Author(s)

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

References

# Original implementation of LCT
Christensen, A. P., & Golino, H. (in press). On the equivalency of factor and network loadings. Behavior Research Methods. doi: 10.31234/osf.io/xakez

# Current implementation of LCT
Christensen, A. P., & Golino, H. (under review). Random, factor, or network model? Predictions from neural networks. PsyArXiv. doi: 10.31234/osf.io/awkcb

Examples

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# Compute LCT
## Network model
LCT(data = wmt2[,7:24])

## Factor model
LCT(data = NetworkToolbox::neoOpen)

EGAnet documentation built on Feb. 17, 2021, 1:06 a.m.