fec_stan {eggCounts} | R Documentation |

Models the mean of faecal egg counts with Bayesian hierarchical models. See Details for a list of model choices.

```
fec_stan(fec, rawCounts = FALSE, CF = 50, zeroInflation = TRUE,
muPrior, kappaPrior, phiPrior, nsamples = 2000, nburnin = 1000,
thinning = 1, nchain = 2, ncore = 1, adaptDelta = 0.95,
saveAll = FALSE, verbose = FALSE)
```

`fec` |
numeric vector. Faecal egg counts. |

`rawCounts` |
logical. If TRUE, |

`CF` |
a positive integer or a vector of positive integers. Correction factor(s). |

`zeroInflation` |
logical. If true, uses the model with zero-inflation. Otherwise uses the model without zero-inflation |

`muPrior` |
named list. Prior for the group mean epg parameter |

`kappaPrior` |
named list. Prior for the group dispersion parameter |

`phiPrior` |
named list. Prior for the zero-inflation parameter |

`nsamples` |
a positive integer. Number of samples for each chain (including burn-in samples). |

`nburnin` |
a positive integer. Number of burn-in samples. |

`thinning` |
a positive integer. Thinning parameter, i.e. the period for saving samples. |

`nchain` |
a positive integer. Number of chains. |

`ncore` |
a positive integer. Number of cores to use when executing the chains in parallel. |

`adaptDelta` |
numeric. The target acceptance rate, a numeric value between 0 and 1. |

`saveAll` |
logical. If TRUE, posterior samples for all parameters are saved in the |

`verbose` |
logical. If true, prints progress and debugging information. |

without zero-inflation: set

`zeroInflation = FALSE`

with zero-inflation: set

`zeroInflation = TRUE`

Note that this function only models the mean of egg counts, see `fecr_stan()`

for modelling the reduction.

The first time each model with non-default priors is applied, it can take up to 20 seconds to compile the model. Currently the function only support prior distributions with two parameters. For a complete list of supported priors and their parameterization, please consult the list of distributions in Stan User Guide.

The default number of samples per chain is 2000, with 1000 burn-in samples. Normally this is sufficient in Stan. If the chains do not converge, one should tune the MCMC parameters until convergence is reached to ensure reliable results.

Prints out summary of `meanEPG`

as the posterior mean epg. The posterior summary contains the mean, standard deviation (sd), 2.5%, 50% and 97.5% percentiles, the 95% highest posterior density interval (HPDLow95 and HPDHigh95) and the posterior mode. NOTE: we recommend to use the 95% HPD interval and the mode for further statistical analysis.

The returned value is a list that consists of:

`stan.samples` |
an object of S4 class |

`posterior.summary` |
a data.frame that is the same as the printed posterior summary |

Craig Wang

`simData1s`

for simulating faecal egg count data with one sample

```
## load the sample data
data(epgs)
## apply zero-infation model
model <- fec_stan(epgs$before, rawCounts = FALSE, CF = 50)
```

[Package *eggCounts* version 2.3-2 Index]