Usage
bfa_mixed(x, data = NULL, num.factor = 1, restrict = NA, normal.dist = NA, nsim = 10000, nburn = 1000, thin = 1, print.status = 500, keep.scores = FALSE, loading.prior = c("gdp", "pointmass", "normal"), factor.scales = FALSE, px = TRUE, coda = "loadings", coda.scale = TRUE, imh.iter = 500, imh.burn = 500, ...)
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 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".
normal.dist
A character vector specifying which variables should be treated as observed
Gaussian. Defaults to all numeric variables if x is a formula.
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 GDP ("gdp", default) point mass ("pointmass") or normal priors ("normal")
factor.scales
Include a separate scale parameter for each factor
px
Use parameter expansion for ordinal variables (recommended)
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
coda.scale
Put the loadings on the correlation scale when creating mcmc objects
imh.iter
Iterations used to build IMH proposal
imh.burn
Burn-in before collecting samples used to build IMH proposal (total burn-in is nburn+imh.iter+imh.burn)
...
Prior parameters and other (experimental) arguments (see details)