add_cross_loadings {latentFactoR}

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
)

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

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

Author(s)

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

References

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

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
)

# 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
)