Description Usage Arguments Value Examples

Compute and plot posterior predictive p-value (Bayesian p-value) from samples of a distribution. The simulations and observations are first summarised into a test statistics, then the test statistic of the observations is compared to the test statistic of the empirical distribution.

1 2 3 4 5 6 7 | ```
post_pred_pval(
yrep,
y,
test_statistic = mean,
alternative = c("two.sided", "less", "greater"),
plot = FALSE
)
``` |

`yrep` |
Matrix of posterior replications with rows corresponding to samples and columns to simulated observations. |

`y` |
Vector of observations. |

`test_statistic` |
Function of the test statistic to compute the p-value for |

`alternative` |
Indicates the alternative hypothesis: must be one of "two.sided", "greater" or "less". |

`plot` |
Whether to output a plot visualising the distribution of the test statistic |

List containing the p-value and (optionally) a ggplot

1 | ```
post_pred_pval(matrix(rnorm(1e3), ncol = 10), rnorm(10))
``` |

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