sp_fa: Posterior sampling for the sparse Bayesian infinite factor...

In rdevito/MSFA: Maximum Likelihood Estimation and Bayesian analysis of MSFA models

Description

Ported to R from Matlab code supporting the paper by Bhattacharya and Dunson (2011). Courtesy of A. Bhattacharya.

Usage

sp_fa(dat, k, trace = TRUE, nprint = 1000, control = list(...), ...)

Arguments

dat	Data matrix, of size n x p.
k	Number of latent factor.
trace	If TRUE then trace information is being printed every nprint iterations of the Gibbs sampling. Default is TRUE.
nprint	Frequency of tracing information. Default is every 1000 iterations.
control	A list of hyperparameters for the prior distributions and for controlling the Gibbs sampling. See sp_fa_control.
...	Arguments to be used to form the default control argument if it is not supplied directly.

Value

A list, with components Sigma and Lambda, containing the posterior samples for the covariance matrix of the model, with dimension p x p x (nrun - burn)/thin, and of the loading matrix, with dimension p x k x (nrun - burn)/thin, respectively.

References

Bhattacharya, A. and Dunson, D.B. (2011). Sparse Bayesian infinite factor models. Biometrika, 98, p. 291-306.

rdevito/MSFA documentation built on March 18, 2020, 2:57 p.m.