Description
Identify Loading Vectors under Sparsity in Factor Models.
Description
Simplify the loading matrix in factor models using the l1 criterion as proposed in Freyaldenhoven (2025) <doi:10.21799/frbp.wp.2020.25>. Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors().
README.md
Overview
The l1rotation package implements the l1-rotation criterion of Freyaldenhoven (2025) to simplify the loading matrix in factor models.
Installation
# Install from CRAN
install.packages("l1rotation")
# Install latest version from GitHub
install.packages("devtools")
devtools::install_github("SimonFreyaldenhoven/l1rotation")
Usage
Find a minimal example below. For more information see the package website, documentation and vignette.
library(l1rotation)
set.seed(916)
# Minimal example with 2 factors, where X is a 224 by 207 matrix
lf <- local_factors(X = example_data, r = 2)
# Rerun with parallel processing
lf_lambdas <- local_factors(X = example_data, r = 2, parallel = TRUE, n_cores = 10)
# Visualize Principal Component estimate of the loadings
lf$pc_plot
# Visualize l1-rotation loadings
lf$rotated_plot
Citation
Simon Freyaldenhoven. “Identification Through Sparsity in Factor Models: the l1-rotation criterion.” Philadelphia Fed Working Paper 20-25, February 2025.
Simon Freyaldenhoven, Ryan Kobler. “l1rotation package.” Code and data repository at https://github.com/SimonFreyaldenhoven/l1rotation, March 2025.