Matrix rotationsOrthogonal and oblique matrix rotations.

p = number of variables, m = number of factors.

Method	Parameters	Purpose
Varimax		Orthogonal only. A computational faster equivalent to CF-Varimax.
Promax	power 0 ... 4	Oblique only.
Crawford-Ferguson	kappa 0 ... 1	Smaller kappa minimizes variables complexity and larger kappa minimizes factor complexity.
CF-Varimax	Crawford-Ferguson kappa = 1/p.	Spread variances across factors. Each factor tends to have either large or small loadings on a particular variable making it easy to identify each variable with a single factor.
CF-Quartimax	Crawford-Ferguson kappa = 0.	Minimizes variable complexity. Works well with distinct clusters without cross-loadings.
CF-Equamax	Crawford-Ferguson kappa = m/(2p).
CF-Parsimax	Crawford-Ferguson kappa = (m-1) / (p+m-2).
CF-Factor Parsimony	Crawford-Ferguson kappa = 1.	Minimizes factor complexity. Primarily of theoretical interest.
Oblimin	gamma 0 ... 1
O-Quartimin	Oblimin gamma = 0.	Equivalent to quartimax.
O-Biquartimin	Oblimin gamma = 0.5.
O-Covarimin	Oblimin gamma = 1.	Equivalent to varimax.
Geomin	delta > 0	Minimizes variable complexity.

Related concepts

Factor rotation

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From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/multivariate/matrix-rotations

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