Description Usage Arguments Details

Fit a finite mixture model using MCMC.

1 2 3 |

`X` |
(matrix) 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. |

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, probabilities of correctly diagnosing patients (eta) to 0.1, and probabilities of the tests correctly diagnosing patients when the patient truly has or does not have the diseas as 0.9 and 0.7 respectively.

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.

haziqj/diagacc documentation built on May 9, 2019, 10:42 a.m.

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