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