factorize: Factor Analysis
In egenn/rtemis: Machine Learning and Visualization

factorize

R Documentation

Factor Analysis

Description

Perform parallel analysis, factor analysis, bifactor analysis and hierarchical clustering.

Usage

factorize(
  x,
  n_factors = NULL,
  method = "minres",
  rotation = "oblimin",
  scores = "regression",
  cor = "cor",
  fa_n_iter = 100,
  omega_method = "minres",
  omega_rotation = c("oblimin", "simplimax", "promax", "cluster", "target"),
  omega_n_iter = 1,
  x_name = NULL,
  print_plot = TRUE,
  do_pa = TRUE,
  do_fa = TRUE,
  do_bifactor = TRUE,
  do_hclust = FALSE,
  verbosity = 1L,
  ...
)

Arguments

`x`	Data. Will be coerced to data frame
`n_factors`	Integer: If NULL, will be estimated using parallel analysis
`method`	Character: Factor analysis method: "minres": minimum residual (OLS), "wls": weighted least squares (WLS); "gls": generalized weighted least squares (GLS); "pa": principal factor solution; "ml": maximum likelihood; "minchi": minimize the sample size weighted chi square when treating pairwise correlations with different number of subjects per pair; "minrank": minimum rank factor analysis.
`rotation`	Character: Rotation methods. No rotation: "none"; Orthogonal: "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT", "bifactor"; Oblique: "promax", "oblimin", "simplimax", "bentlerQ, "geominQ", "biquartimin", "cluster".
`scores`	Character: Factor score estimation method. Options: "regression", "Thurstone": simple regression, "tenBerge": correlation-preserving, "Anderson", "Barlett".
`cor`	Character: Correlation method: "cor": Pearson correlation, "cov": Covariance, "tet": tetrachoric, "poly": polychoric, "mixed": mixed cor for a mixture of tetrachorics, polychorics, Pearsons, biserials, and polyserials, "Yuleb": Yulebonett, "Yuleq" and "YuleY": Yule coefficients
`fa_n_iter`	Integer: Number of iterations for factor analysis.
`omega_method`	Character: Factor analysis method for the bifactor analysis. Same options as `method`
`omega_rotation`	Character: Rotation method for bifactor analysis: "oblimin", "simplimax", "promax", "cluster", "target".
`omega_n_iter`	Integer: Number of iterations for bifactor analysis.
`x_name`	Character: Name your dataset. Used for plotting
`print_plot`	Logical: If TRUE, print plots along the way.
`do_pa`	Logical: If TRUE, perform parallel analysis.
`do_fa`	Logical: If TRUE, perform factor analysis.
`do_bifactor`	Logical: If TRUE, perform bifactor analysis.
`do_hclust`	Logical: If TRUE, perform hierarchical cluster analysis.
`verbosity`	Integer: Verbosity level.
`...`	Additional arguments to pass to `psych::fa`