NEST {latentFactoR}

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 = 0.00001
)

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

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.

Author(s)

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
)

## Not run: 
# Perform NEST
NEST(two_factor$data)
## End(Not run)