add_cross_loadings: Adds (Substantial) Cross-loadings to `simulate_factors` Data

Description

Intended to add substantial cross-loadings to simulated data from simulate_factors. See examples to get started

Usage

add_cross_loadings(
  lf_object,
  proportion_cross_loadings,
  proportion_cross_loadings_range = NULL,
  magnitude_cross_loadings,
  magnitude_cross_loadings_range = NULL,
  leave_cross_loadings = FALSE
)

Value

Returns a list containing the same parameters as the original lf_object but with updated data, population_correlation, and parameters (specifically, loadings matrix). Also returns original lf_object in original_results

Arguments

lf_object: Data object from simulate_factors
proportion_cross_loadings: Numeric (length = 1 or factors). Proportion of variables that should be cross-loaded randomly onto one other factor. Accepts number of variables to cross-load onto one other factor as well
proportion_cross_loadings_range: Numeric (length = 2). Range of proportion of variables that should be cross-loaded randomly onto one other factor. Accepts number of variables to cross-load onto one other factor as well
magnitude_cross_loadings: Numeric (length = 1, factors, or total number of variables to cross-load across all factors). The magnitude or size of the cross-loadings. Must range between -1 and 1.
magnitude_cross_loadings_range: Numeric (length = 2). The range of the magnitude or size of the cross-loadings. Defaults to NULL
leave_cross_loadings: Boolean. Should cross-loadings be kept? Defaults to FALSE. Convergence problems can arise if cross-loadings are kept, so setting them to zero is the default. Only set to TRUE with careful consideration of the structure. Make sure to perform additional checks that the data are adequate

Author

Alexander P. Christensen <alexpaulchristensen@gmail.com>, Hudson Golino <hfg9s@virginia.edu>, Luis Eduardo Garrido <luisgarrido@pucmm.edu>

References

Christensen, A. P., Garrido, L. E., & Golino, H. (2022). Unique variable analysis: A network psychometrics method to detect local dependence. PsyArXiv

Examples

Run this code

# 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
)

# Add substantial cross-loadings
two_factor_CL <- add_cross_loadings(
  lf_object = two_factor,
  proportion_cross_loadings = 0.25,
  magnitude_cross_loadings = 0.35
)

# Randomly vary proportions
two_factor_CL <- add_cross_loadings(
  lf_object = two_factor,
  proportion_cross_loadings_range = c(0, 0.25),
  magnitude_cross_loadings = 0.35
)

# Randomly vary magnitudes
two_factor_CL <- add_cross_loadings(
  lf_object = two_factor,
  proportion_cross_loadings = 0.25,
  magnitude_cross_loadings_range = c(0.35, 0.45)
)

# Set number of cross-loadings per factor (rather than proportion)
two_factor_CL <- add_cross_loadings(
  lf_object = two_factor,
  proportion_cross_loadings = 2,
  magnitude_cross_loadings = 0.35
)

Run the code above in your browser using DataLab

Join us for RADAR: AI Edition