latentFactoR (version 0.0.4)

NEST: Estimate Number of Dimensions using Next Eigenvalue Sufficiency Test

Description

Estimates the number of dimensions in data using NEST (Achim, 2017). See examples to get started

Usage

NEST(
  data,
  sample_size,
  iterations = 1000,
  maximum_iterations = 500,
  alpha = 0.05,
  convergence = 1e-05
)

Value

Returns a list containing:

dimensions

Number of dimensions identified

loadings

Loading matrix

converged

Whether estimation converged. If FALSE, then results are reported from last convergence point. Interpret results with caution.

Arguments

data

Matrix or data frame. Either a dataset with all numeric values (rows = cases, columns = variables) or a symmetric correlation matrix

sample_size

Numeric (length = 1). If input into data is a correlation matrix, then specifying the sample size is required

iterations

Numeric (length = 1). Number of iterations to estimate rank. Defaults to 1000

maximum_iterations

Numeric (length = 1). Maximum umber of iterations to obtain convergence of eigenvalues. Defaults to 500

alpha

Numeric (length = 1). Significance level for determine sufficient eigenvalues. Defaults to 0.05

convergence

Numeric (length = 1). Value necessary to be less than or equal to when establishing convergence of eigenvalues

Author

Alexander P. Christensen <[e-mail]>, Hudson Golino <[e-mail]>, Luis Eduardo Garrido <[e-mail]>

References

Achim, A. (2017). Testing the number of required dimensions in exploratory factor analysis. The Quantitative Methods for Psychology, 13(1), 64–74.

Brandenburg, N., & Papenberg, M. (2022). Reassessment of innovative methods to determine the number of factors: A simulation-Based comparison of Exploratory Graph Analysis and Next Eigenvalue Sufficiency Test. Psychological Methods.

Examples

# Generate factor data
two_factor <- simulate_factors(
  factors = 2, # factors = 2
  variables = 6, # variables per factor = 6
  loadings = 0.55, # loadings between = 0.45 to 0.65
  cross_loadings = 0.05, # cross-loadings N(0, 0.05)
  correlations = 0.30, # correlation between factors = 0.30
  sample_size = 1000 # number of cases = 1000
)

if (FALSE) {
# Perform NEST
NEST(two_factor$data)}