Bayesian Clustering Factor Models

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Documentation for package ‘BCFM’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

BCFM.fit	Fit BCFM Model
BCFM.model.selection	BCFM Model Selection Over Multiple Groups and Factors
BCFMcpp	Gibbs sampler of BCFM
getmode	Get the mode of a vector
ggplot_B.CI	Build factor loadings plot
ggplot_B.trace	Trace plot for posterior of factor loadings
ggplot_IC	Plot IC Matrix from Model Selection
ggplot_latent.profiles	Plot Latent Factor Profiles by Cluster
ggplot_mu.density	Density of group means mu using ggplot2
ggplot_omega.density	The density plot of the diagonal of group covariance, Omega, with ggplot2
ggplot_probs.density	Density plot for posterior of probabilities
ggplot_probs.trace	Trace plot of probabilities parameter
ggplot_sigma2.CI	A credible interval plot of posterior of sigma squared
ggplot_tau.CI	A credible interval plot of posterior of factor loadings covariance, tau
ggplot_variability	Variability explained by factors
ggplot_Zit.heatmap	A heatmap of group assignments, Z using ggplot2
IC	Information Criterion. Very close to the original BIC method, but this uses the integrated likelihood instead.
init.data	Initialize Data Array for BCFM Model
initialize.cluster.hyperparms	Initialize cluster hyperparameters
initialize.hyp.parm	Initialize hyperparmeters for BCFM model
initialize.model.attributes	Build model attributes from the dataset
permutation.order	Order of permutation by the largest absolute value in each eigenvector
permutation.scale	Permute the dataset by the largest absolute value in each eigenvector, and scale
sim.data	Simulated data for BCFM model