Description Usage Arguments Details

Fit a latent class model using MCMC.

1 2 3 4 |

`X` |
(Matrix) Item responses data set. |

`n.sample` |
Number of MCMC samples. |

`n.chains` |
Number of chains. |

`n.thin` |
Thinning value. |

`n.burnin` |
Number of burn-in. |

`n.adapt` |
Number of adaptation samples. |

`raw` |
(Logical) Return the randomLCA or runjags object? |

`runjags.method` |
Parallel or normal method. See runjags documentation. |

`silent` |
(Logical) Suppress output. |

`calcSE` |
(logical) Calculate standard error of estimates for randomLCA fit. |

`gold.std` |
(Logical) Is the last item/column in X the gold standard? |

`method` |
(DEPRECATED–Use MCMC only) EM algorithm or MCMC. |

Uninformative priors are used, e.g. Unif(0, 1) for probabilities. Initial value for the prevalence is set at 0.1, the disease indicators to zero for all units, and the sensitivities and specificities to 0.9.

Note that when `gold.std`

is `TRUE`

, then the last column in
`X`

is assumed to be the gold standard item responses. Thus, the
sensitivities and specificities attached to this item is fixed to 1.

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