Description Usage Arguments Examples

View source: R/prior_pattern.R

This function modifies default hyper prior parameter values in the type of selection model selected according to the type of missingness mechanism and distributions for the outcomes assumed.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
prior_pattern(
type,
dist_e,
dist_c,
pe_fixed,
pc_fixed,
model_e_random,
model_c_random,
pe_random,
pc_random,
d_list,
restriction
)
``` |

`type` |
Type of missingness mechanism assumed. Choices are Missing At Random (MAR), Missing Not At Random for the effects (MNAR_eff), Missing Not At Random for the costs (MNAR_cost), and Missing Not At Random for both (MNAR). For a complete list of all available hyper parameters and types of models see the manual. |

`dist_e` |
distribution assumed for the effects. Current available chocies are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'), Weibull ('weibull'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('nbinom') or Bernoulli ('bern') |

`dist_c` |
Distribution assumed for the costs. Current available chocies are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm') |

`pe_fixed` |
Number of fixed effects for the effectiveness model |

`pc_fixed` |
Number of fixed effects for the cost model |

`model_e_random` |
Random effects formula for the effectiveness model |

`model_c_random` |
Random effects formula for the costs model |

`pe_random` |
Number of random effects for the effectiveness model |

`pc_random` |
Number of random effects for the cost model |

`d_list` |
a list of the number and types of patterns in the data |

`restriction` |
type of identifying restriction to be imposed |

1 2 3 4 | ```
#Internal function only
#no examples
#
#
``` |

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