modelFit {DrBats} | R Documentation |
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/