rotate_pca: Rotate factors to match Principal-Components Analysis

In tinyVAST: Multivariate Spatio-Temporal Models using Structural Equations

Rotate factors to match Principal-Components Analysis

Description

Rotate lower-triangle loadings matrix to order factors from largest to smallest variance.

Usage

rotate_pca(
  L_tf,
  x_sf = matrix(0, nrow = 0, ncol = ncol(L_tf)),
  order = c("none", "increasing", "decreasing")
)

Arguments

L_tf	Loadings matrix with dimension T \times F.
x_sf	Spatial response with dimensions S \times F.
order	Options for resolving label-switching via reflecting each factor to achieve a given order across dimension T.

Value

List containing the rotated loadings L_tf, the inverse-rotated response matrix x_sf, and the rotation H

tinyVAST documentation built on April 4, 2025, 2:43 a.m.