bfa_gauss: Initialize and fit a Gaussian factor model

Description Usage Arguments Details Value

View source: R/bfa_gauss.R

Description

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.

Usage

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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)

Details

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

Additional parameters:

Value

A bfa object with posterior samples.


bfa documentation built on May 29, 2017, 1:38 p.m.