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

factoryze

R Documentation

Factor Analysis

Description

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

Usage

factoryze(
  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,
  verbose = TRUE,
  ...
)

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. Default = "minres"
`rotation`	Character: Rotation methods. No rotation: "none"; Orthogonal: "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT", "bifactor"; Oblique: "promax", "oblimin", "simplimax", "bentlerQ, "geominQ", "biquartimin", "cluster". Default = "oblimin"
`scores`	Character: Factor score estimation method. Options: "regression", "Thurstone": simple regression, "tenBerge": correlation-preserving, "Anderson", "Barlett". Default = "regression"
`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. Default = 100
`omega.method`	Character: Factor analysis method for the bifactor analysis. Same options as `method` Default = "minres"
`omega.rotation`	Character: Rotation method for bifactor analysis: "oblimin", "simplimax", "promax", "cluster", "target". Default = "oblimin"
`omega.n.iter`	Integer: Number of iterations for bifactor analysis. Default = 1
`x.name`	Character: Name your dataset. Used for plotting
`print.plot`	Logical: If TRUE, print plots along the way. Default = TRUE
`do.pa`	Logical: If TRUE, perform parallel analysis. Default = TRUE
`do.fa`	Logical: If TRUE, perform factor analysis. Default = TRUE
`do.bifactor`	Logical: If TRUE, perform bifactor analysis. Default = TRUE
`do.hclust`	Logical: If TRUE, perform hierarchical cluster analysis. Default = TRUE
`verbose`	Logical: If TRUE, print messages to output. Default = TRUE
`...`	Additional arguments to pass to `psych::fa`