factoryze: 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

Data. Will be coerced to data frame

n.factors

Integer: If NULL, will be estimated using parallel analysis

method

String: 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

String: Rotation methods. No rotation: "none"; Orthogonal: "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT", "bifactor"; Oblique: "promax", "oblimin", "simplimax", "bentlerQ, "geominQ", "biquartimin", "cluster". Default = "oblimin"

scores

String: Factor score estimation method. Options: "regression", "Thurstone": simple regression, "tenBerge": correlation-preserving, "Anderson", "Barlett". Default = "regression"

cor

String: 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

String: Factor analysis method for the bifactor analysis. Same options as method Default = "minres"

omega.rotation

String: 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

String: 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

Details

Consult psych::fa for more information on the parameters