bfa_gauss: Initialize and fit a Gaussian factor model
In bfa: Bayesian Factor Analysis

Description Usage Arguments Details Value

This function performs a specified number of MCMC iterations and returns an object containing summary statistics from the MCMC samples as well as the actual samples of factor scores if keep.scores is TRUE. Default behavior is to save only samples of the loadings.

bfa_gauss(x, data = NULL, num.factor = 1, restrict = NA, nsim = 10000,
  nburn = 1000, thin = 1, print.status = 500, keep.scores = FALSE,
  loading.prior = c("gdp", "pointmass", "normal"), factor.scales = TRUE,
  coda = "loadings", ...)

`x`	A formula or bfa object.
`data`	The data (if x is a formula)
`num.factor`	Number of factors
`restrict`	A matrix or list giving identifiability restrictions on factor loadings. A matrix should be the same size as the loadings matrix. Acceptable values are 0 (identically 0), 1 (unrestricted), or 2 (strictly positive). List elements should be character vectors of the form c("variable",1, ">0") where 'variable' is the manifest variable, 1 is the factor, and ">0" is the restriction. Acceptable restrictions are ">0" or "0".
`nsim`	Number of iterations past burn-in
`nburn`	Number of initial (burn-in) iterations to discard
`thin`	Keep every thin'th MCMC sample (i.e. save nsim/thin samples)
`print.status`	How often to print status messages to console
`keep.scores`	Save samples of factor scores
`loading.prior`	Specify the prior on factor loadings - generalized double Pareto ("gdp", default), point mass mixtures (mixture of point mass at zero + mean zero normal) ("pointmass") or normal/Gaussian ("normal")
`factor.scales`	Include a shared precision parameter for each column of the factor loadings matrix. See details for setting hyperprior parameters. This is implemented as in PX-FA of Ghosh and Dunson (2009)
`coda`	Create `mcmc` objects to allow use of functions from the `coda` package: "all" for loadings and scores, "loadings" or "scores" for one or the other, or "none" for neither
`...`	Prior parameters and other (experimental) arguments (see details)

Note: All the priors in use assume that the manifest variables are on approximately the same scale.

Additional parameters:

loadings.var: Factor loading prior variance
tau.a, tau.b: Gamma hyperparameters (scale=1/b) for factor precisions (if factor.scales=T). Default is a=b=1 (MV t w/ df=2)
rho.a, rho.b: Beta hyperparameters for point mass prior
sigma2.a, sigma2.b: Gamma hyperparameters for error precisions
gdp.alpha, gdp.beta: GDP prior parameters
mu.mean, mu.var: (Scalar) prior mean and variance for mu[j] (where E(y) = mu). Defaults are 0 and 1e4.