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Loadings Comparison Test

Source: R/LCT.R
LCT.Rd

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.

Usage

LCT(
  data,
  n = NULL,
  corr = c("auto", "cor_auto", "pearson", "spearman"),
  na.data = c("pairwise", "listwise"),
  model = c("BGGM", "glasso", "TMFG"),
  algorithm = c("leiden", "louvain", "walktrap"),
  uni.method = c("expand", "LE", "louvain"),
  iter = 100,
  seed = NULL,
  verbose = TRUE,
  ...
)

Arguments

data

Matrix or data frame. Should consist only of variables to be used in the analysis. Can be raw data or a correlation matrix

n

Numeric (length = 1). Sample size if data provided is a correlation matrix

corr

Character (length = 1). Method to compute correlations. Defaults to "auto". Available options:

  • "auto" — Automatically computes appropriate correlations for the data using Pearson's for continuous, polychoric for ordinal, tetrachoric for binary, and polyserial/biserial for ordinal/binary with continuous. To change the number of categories that are considered ordinal, use ordinal.categories (see polychoric.matrix for more details)

  • "cor_auto" — Uses cor_auto to compute correlations. Arguments can be passed along to the function

  • "pearson" — Pearson's correlation is computed for all variables regardless of categories

  • "spearman" — Spearman's rank-order correlation is computed for all variables regardless of categories

For other similarity measures, compute them first and input them into data with the sample size (n)

na.data

Character (length = 1). How should missing data be handled? Defaults to "pairwise". Available options:

  • "pairwise" — Computes correlation for all available cases between two variables

  • "listwise" — Computes correlation for all complete cases in the dataset

model

Character (length = 1). Defaults to "glasso". Available options:

  • "BGGM" — Computes the Bayesian Gaussian Graphical Model. Set argument ordinal.categories to determine levels allowed for a variable to be considered ordinal. See ?BGGM::estimate for more details

  • "glasso" — Computes the GLASSO with EBIC model selection. See EBICglasso.qgraph for more details

  • "TMFG" — Computes the TMFG method. See TMFG for more details

algorithm

Character or igraph cluster_* function (length = 1). Defaults to "walktrap". Three options are listed below but all are available (see community.detection for other options):

  • "leiden" — See cluster_leiden for more details

  • "louvain" — By default, "louvain" will implement the Louvain algorithm using the consensus clustering method (see community.consensus for more information). This function will implement consensus.method = "most_common" and consensus.iter = 1000 unless specified otherwise

  • "walktrap" — See cluster_walktrap for more details

uni.method

Character (length = 1). What unidimensionality method should be used? Defaults to "louvain". Available options:

  • "expand" — Expands the correlation matrix with four variables correlated 0.50. If number of dimension returns 2 or less in check, then the data are unidimensional; otherwise, regular EGA with no matrix expansion is used. This method was used in the Golino et al.'s (2020) Psychological Methods simulation

  • "LE" — Applies the Leading Eigenvector algorithm (cluster_leading_eigen) on the empirical correlation matrix. If the number of dimensions is 1, then the Leading Eigenvector solution is used; otherwise, regular EGA is used. This method was used in the Christensen et al.'s (2023) Behavior Research Methods simulation

  • "louvain" — Applies the Louvain algorithm (cluster_louvain) on the empirical correlation matrix. If the number of dimensions is 1, then the Louvain solution is used; otherwise, regular EGA is used. This method was validated Christensen's (2022) PsyArXiv simulation. Consensus clustering can be used by specifying either "consensus.method" or "consensus.iter"

iter

Numeric (length = 1). Number of replicate samples to be drawn from a multivariate normal distribution (uses MASS::mvrnorm). Defaults to 100 (recommended)

seed

Numeric (length = 1). Defaults to NULL or random results. Set for reproducible results. See Reproducibility and PRNG for more details on random number generation in EGAnet

verbose

Boolean (length = 1). Should progress be displayed? Defaults to TRUE. Set to FALSE to not display progress

...

Additional arguments that can be passed on to auto.correlate, network.estimation, community.detection, community.consensus, and EGA

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

References

Model training and validation
Christensen, A. P., & Golino, H. (2021). Factor or network model? Predictions from neural networks. Journal of Behavioral Data Science, 1(1), 85-126.

Author

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

Examples

# Get data
data <- psych::bfi[,1:25]

if (FALSE) # Compute LCT
## Factor model
LCT(data) # \dontrun{}