Description Usage Arguments Value Author(s) Examples

This function fits offspring data to a special case of the normal distribution, in which zero and negative values of offspring are excluded, and estimates P2 based on that distribution and the specificed priors.

1 | ```
ABC_P2_norm(n, ObsMean, M_Lo, M_Hi, SD_Lo, SD_Hi, delta, iter)
``` |

`n` |
number of observations. |

`ObsMean` |
the observed mean number of offspring sired by the second male. |

`M_Lo` |
minimum mean value for the distribution. |

`M_Hi` |
maximum mean value for the distribution. |

`SD_Lo` |
minimum standard deviation value for the distribution. |

`SD_Hi` |
maximum standard deviation value for the distribution. |

`delta` |
maximum allowed difference between the estimated mean and observed mean number of offspring produced by the second male. |

`iter` |
number of iterations used to build the posterior. |

`posterior` |
Posterior distribution of P2 values. |

`Avg` |
Vector of values for the mean parameter. |

`Std` |
Vector of values for the standard deviation parameter. |

M. Catherine Duryea, Andrew D. Kern, Robert M. Cox, and Ryan Calsbeek

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
#Fit the Mean and Standard Deviation hyperpriors to a distribution of offspring.
data(fungus)
fit_dist_norm(fungus$Total_Offspring)
#Use hyperiors and priors calculated from the data to estimate P2.
#Plot the saved distributions for the Mean and Standard Deviation parameters.
#Adjust, if necessary.
fungus_P2<-ABC_P2_norm(12, 9.9, 11.35, 17.31, 8.22, 12.44, 0.1, 100)
hist(fungus_P2$posterior)
hist(fungus_P2$Avg)
hist(fungus_P2$Std)
``` |

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