Usage
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", ...)
Arguments
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)