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 if keep.scores or keep.loadings are TRUE
.
Default behavior is to save only the loadings.
1 2 3 4 5 |
x |
A formula, matrix or bfa object. |
data |
The data if x is a formula |
num.factor |
Number of factors |
restrict |
A matrix or list giving 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". |
center.data |
Center data |
scale.data |
Scale data |
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 |
keep.loadings |
Save samples of factor loadings |
loading.prior |
Specify point mass ("pointmass", default) or normal priors ("normal") |
coda |
Create |
... |
Prior parameters and other (experimental) arguments (see details) |
Additional parameters:
loadings.var: Factor loading prior variance
tau.a, tau.b: Gamma hyperparameters (scale=1/b) for factor precisions (if factor.scales=T)
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
A bfa
object with posterior samples.
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