Bayesian Clustering Factor Models (BCFM) for clustering and latent factor analysis of multivariate cross-sectional data.
You can install the development version of BCFM from GitHub with:
# install.packages("devtools")
devtools::install_github("ategge/BCFM", build_vignettes = TRUE)This is a basic example which shows you how to use BCFM:
library(BCFM)
# Load example data
data("sim.data", package = "BCFM")
# Specify variables to use for clustering
cluster.vars <- paste0("V", 1:20)
# Create output directory for results
# Use tempdir() for examples, or specify your own directory for real analyses
output_dir <- file.path(tempdir(), "BCFM_results")
# Run model selection
BCFM.model.selection(
data = sim.data,
cluster.vars = cluster.vars, # Required parameter
grouplist = 2:4, # Try 2, 3, and 4 groups
factorlist = 2:4, # Try 2, 3, and 4 factors
n.iter = 10000, # Number of MCMC iterations
burnin = 5000, # Burnin for Information Criterion calculations
every = 10, # Progress update frequency
cluster.size = 0.01, # Minimum proportion required for each cluster (default 0.05)
output_dir = output_dir, # Specify where to save results
seed = 123 # Optional seed for reproducibility
)
# Results are saved in output_dir
# Load and visualize IC results
load(file.path(output_dir, "IC.Rdata"))
ggplot_IC(IC.matrix, factor_list = 2:4, group_list = 2:4)
# Load and visualize model results for 4 groups and 3 factors
load(file.path(output_dir, "results-covarianceF-g4-f3.Rdata"))
ggplot_latent.profiles(SDresult$Result)For a complete workflow tutorial, see the vignette:
# After installation with build_vignettes = TRUE
vignette("introduction-to-BCFM", package = "BCFM")
# Or browse all vignettes
browseVignettes("BCFM")If you use BCFM in your research, please cite:
[Add your citation here when you have a publication]GPL-3