# Simulate faecal egg count data (2-sample situation)

### Description

Generates two samples of (zero-inflated) egg count data

### Usage

1 2 3 |

### Arguments

`n` |
sample size (number of faecal samples collected pre- and post-treatment) |

`preMean` |
true number of eggs per gram (epg) (i.e. worm burden) before treatment |

`delta` |
proportion of epg left after treatment, between 0 and 1. 1 - |

`kappa` |
overdispersion parameter, |

`phiPre` |
pre-treatment prevalence (i.e. proportion of infected animals), between 0 and 1 |

`phiPost` |
post-treatment prevalence, between 0 and 1 |

`f` |
correction factor of the egg counting technique, either an integer or a vector of integers with length |

`paired` |
logical. If true, paired samples are simulated. Otherwise unpaired samples are simulated. |

`rounding` |
logical. If true, the Poisson mean for the raw counts is rounded. The rounding applies since the mean epg is frequently reported as an integer value. For more information, please see Details. |

### Details

The simulation process does not exactly match the proposed models in [ref:paper], however the simulated data resembles the data observed in real world.

In the simulation of raw (`master`

) counts, it follows a Poisson distribution with some mean. The mean is frequently rounded down if it has a very low value and `rounding = TRUE`

,there expects to be a up to 3-10% positive bias in the mean reduction when *μ* < 150 and *δ* < 0.1. Set `rounding = FALSE`

if one does not wish to have any bias.

### Value

A matrix with six columns, namely the observed epg (`obs`

),
number of eggs counted on microscope slide (`master`

) and true egg counts (`true`

) for both pre- and post- treatment.

### Author(s)

Michaela Paul michaela.paul@uzh.ch

Craig Wang craig.wang@uzh.ch

### See Also

`fecr_stan`

for analyzing faecal egg count data with two samples

### Examples

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