rotate_pca

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

Fits a wide variety of multivariate spatio-temporal models
with simultaneous and lagged interactions among variables (including
vector autoregressive spatio-temporal ('VAST') dynamics)
for areal, continuous, or network spatial domains.
It includes time-variable, space-variable, and space-time-variable
interactions using dynamic structural equation models ('DSEM')
as expressive interface, and the 'mgcv' package to specify splines
via the formula interface. See Thorson et al. (2024)
<doi:10.48550/arXiv.2401.10193> for more details.

James Thorson

tinyVAST

Multivariate Spatio-Temporal Models using Structural Equations

rotate_pca function

<dl><dt>L_tf</dt>
<dd>Loadings matrix with dimension \(T \times F\).</dd>
<dt>x_sf</dt>
<dd>Spatial response with dimensions \(S \times F\).</dd>
<dt>order</dt>
<dd>Options for resolving label-switching via reflecting
each factor to achieve a given order across dimension \(T\).</dd></dl>

Arguments

Rotate factors to match Principal-Components Analysis — rotate_pca

<dl>

<dt>L_tf</dt>
<dd>Loadings matrix with dimension \(T \times F\).</dd>


<dt>x_sf</dt>
<dd>Spatial response with dimensions \(S \times F\).</dd>


<dt>order</dt>
<dd>Options for resolving label-switching via reflecting
each factor to achieve a given order across dimension \(T\).</dd>

</dl>

Get 50% off unlimited learning

rotate_pca: Rotate factors to match Principal-Components Analysis

Description

Usage

Value

Arguments