modelFit: Fit a Bayesian Latent Factor to a data set using STAN

In DrBats: Data Representation: Bayesian Approach That's Sparse

Fit a Bayesian Latent Factor to a data set using STAN

Description

Fit a Bayesian Latent Factor to a data set using STAN

Usage

modelFit(
  model = "PLT",
  var.prior = "IG",
  prog = "stan",
  parallel = TRUE,
  Xhisto = NULL,
  nchains = 4,
  nthin = 10,
  niter = 10000,
  R = NULL
)

Arguments

model	a string indicating the type of model ("PLT", or sparse", default = "PLT")
var.prior	the family of priors to use for the variance parameters ("IG" for inverse gamma, or "cauchy")
prog	a string indicating the MCMC program to use (default = "stan")
parallel	true or false, whether or not to parelleize (done using the package "parallel")
Xhisto	matrix of simulated data (projected onto the histogram basis)
nchains	number of chains (default = 2)
nthin	the number of thinned interations (default = 1)
niter	number of iterations (default = 1e4)
R	rotation matrix of the same dimension as the number of desired latent factors

Value

stanfit, a STAN object

Author(s)

Gabrielle Weinrott

References

The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mc-stan.org/

DrBats documentation built on March 18, 2022, 5:15 p.m.