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DrBats (version 0.1.3)

Data Representation: Bayesian Approach That's Sparse

Description

Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.

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Version

Install

install.packages('DrBats')

Monthly Downloads

231

Version

0.1.3

License

GPL-3

Maintainer

Gabrielle Weinrott

Last Published

May 18th, 2016

Functions in DrBats (0.1.3)

coinertia.drbats

Perform Coinertia Analysis on the PCA of the Weighted PCA and Deville's PCA

Project a set of curves onto a histogram basis

Post-process an MCMC list with reflection issues

Main simulation function

Fit a Bayesian Latent Factor to a data set using STAN

Format scores output for visualization

Main function to fit the Bayesian Latent Factor model

Plot the estimates for the latent factors

A stanfit object fitted to the toydata

Calculate the log likelihood of the model

Perform a PCA using Deville's method

Convert a STAN objet to MCMC list

Build and decompose a low-rank matrix W

weighted.Deville

Perform a weighted PCA using Deville's method on a data matrix X that we project onto a histogram basis and weighted

PCA data projected onto a histogram basis

interval.pca.Deville

Perform Interval-PCA using Deville's method

A toy longitudinal data set

Calculate the unnormalized posterior density of the model