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.

1 2 3 4 |

`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 |

`...` |
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.

A `bfa`

object with posterior samples.

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