permutation.scale

It finds the vector of permutation to permute data by its largest absolute value in each eigenvector. It sets the order by specified number of factors, and the rest is ordered as they were. The data is permuted, and if needed, scaled.

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

Hwasoo Shin

DIFM

Dynamic ICAR Spatiotemporal Factor Models

Marco Ferreira

permutation.scale function

<dl><dt>data</dt>
<dd>The dataset</dd>
<dt>n.factors</dt>
<dd>Number of factors</dd>
<dt>return.scale</dt>
<dd>Scale data after permutation</dd></dl>

Arguments

Permute the dataset by the largest absolute value in each eigenvector, and scale — permutation.scale

<dl>

<dt>data</dt>
<dd>The dataset</dd>


<dt>n.factors</dt>
<dd>Number of factors</dd>


<dt>return.scale</dt>
<dd>Scale data after permutation</dd>

</dl>

permutation.scale: Permute the dataset by the largest absolute value in each eigenvector, and scale

Description

Usage

Value

Arguments