efa_procrustes: Rotate a loading matrix to a target using Procrustes alignment

Description

efa_procrustes() aligns one loading matrix to a target loading matrix with the same dimensions. It is used internally by efa_mi(), but can also be used directly when factor columns must be brought into a common orientation before averaging or comparing solutions.

Usage

efa_procrustes(
  A,
  Target,
  rotation = c("orthogonal", "oblique"),
  S = NULL,
  T_init = NULL,
  oblique_eps = 1e-05,
  oblique_maxit = 1000,
  oblique_max_line_search = 10,
  oblique_step0 = 1,
  oblique_normalize = FALSE,
  oblique_random_starts = 0,
  oblique_screen_keep = 2,
  oblique_triage_maxit = 25,
  oblique_triage_improve_tol = 0
)

Value

A list containing aligned loadings, transformation matrix T, factor intercorrelation matrix Phi, target criterion value, convergence diagnostics, line-search diagnostics, and multi-start summaries. Row and column names are preserved where possible. When oblique_normalize = TRUE

the returned loadings are back-transformed to the original scale, but value is the criterion on the Kaiser-normalized loadings, so it is not 0.5 * sum((loadings - Target)^2).

Arguments

A: Numeric loading matrix to be aligned.
Target: Numeric target matrix with the same dimensions as A.
rotation: Character string, either "orthogonal" or "oblique".
S: Optional k x k cross-product matrix crossprod(A). Supplying this is useful when the same A is rotated repeatedly. S is used only when oblique_normalize = FALSE; if Kaiser normalization is requested, the cross-product must be recomputed on the normalized matrix.
T_init: Optional k x k nonsingular starting transformation matrix for the oblique solver. Its columns are normalized internally. If NULL (the default), the oblique solver is warm-started from the closed-form orthogonal Procrustes solution.
oblique_eps: Positive convergence tolerance for the projected-gradient norm in the oblique solver.
oblique_maxit: Non-negative integer. Maximum number of projected-gradient updates in the full oblique solver.
oblique_max_line_search: Non-negative integer. Maximum number of step-halving attempts after the initial line-search step.
oblique_step0: Positive initial step size for the oblique solver.
oblique_normalize: Logical; if TRUE, apply Kaiser row normalization to the loadings (only) in the oblique solver and back-transform the aligned loadings afterwards, leaving Target unnormalized (as in GPArotation::targetQ(normalize = TRUE)).
oblique_random_starts: Non-negative integer. Number of additional random starts used by the oblique solver.
oblique_screen_keep: Non-negative integer. Number of random starts retained after cheap objective screening and sent to triage optimization.
oblique_triage_maxit: Non-negative integer. Number of short optimization iterations used in the triage stage.
oblique_triage_improve_tol: Non-negative scalar. Relative improvement required for a triaged start to be promoted to full optimization.

Details

For rotation = "orthogonal", the function solves the closed-form orthogonal Procrustes problem

$$\min_T \frac{1}{2}\|A T - B\|_F^2 \quad \textrm{subject to}\quad T'T = I,$$

where A is the loading matrix and B is Target.

For rotation = "oblique", the function calls the compiled .oblique_procrustes() optimizer. The oblique convention is the same as in GPArotation::targetQ():

$$L = A T^{-T}, \qquad \Phi = T'T, \qquad diag(\Phi) = 1.$$

By default the oblique solver is warm-started from the closed-form orthogonal Procrustes solution, which resolves the factor permutation and sign indeterminacy and avoids the poor local minima an identity start can fall into. Supply T_init to override this start. Random starts are only used for oblique alignment. For one-factor models, oblique and orthogonal alignment are equivalent, so the function uses the stable one-factor orthogonal solution instead of calling the oblique optimizer.

Examples

Run this code

## Align an estimated loading matrix to a known target pattern: fit an
## unrotated three-factor model, then rotate its loadings toward the true
## population pattern.
efa_mod <- efa_fit(test_models$baseline$cormat, N = 500, n_factors = 3,
                   estimator = "PAF", rotation = "none")
target <- population_models$loadings$baseline

## Orthogonal target rotation (rigid rotation/reflection):
efa_procrustes(efa_mod$unrot_loadings, target, rotation = "orthogonal")

## Oblique target rotation (lets the aligned factors correlate):
efa_procrustes(efa_mod$unrot_loadings, target, rotation = "oblique")