predict_inactivation_MCMC {bioinactivation} | R Documentation |

## Dynamic Prediction Intervals from a Monte Carlo Adjustment

### Description

Given a model adjustment of a dynamic microbial inactivation process
performed using any of the functions in `bioinactivation`

calculates
probability intervals at each time point using a Monte Carlo method.

### Usage

```
predict_inactivation_MCMC(fit_object, temp_profile, n_simulations = 100,
times = NULL, quantiles = c(2.5, 97.5), additional_pars = NULL)
```

### Arguments

`fit_object` |
An object of classes |

`temp_profile` |
data frame with discrete values of the temperature for
each time. It must have one column named |

`n_simulations` |
a numeric indicating how many Monte Carlo simulations
to perform. |

`times` |
numeric vector specifying the time points when results are
desired. If |

`quantiles` |
numeric vector indicating the quantiles to calculate in
percentage. By default, it is set to c(2.5, 97.5) which generates a
prediction interval with confidence 0.95. If |

`additional_pars` |
Additional parameters not included in the adjustment (e.g. the initial number of microorganism in an isothermal fit). |

### Value

A data frame of class `PredInactivationMCMC`

. On its first column,
time at which the calculation has been made is indicated.
If `quantiles = NULL`

, the following columns contain the
results of each simulation. Otherwise, the second and third columns
provide the mean and median of the simulations at the given time
point. Following columns contain the quantiles of the results.

*bioinactivation*version 1.2.3 Index]