predict_Nsurv_check | R Documentation |

`survFitPredict`

and
`survFitPredict_Nsurv`

objectsFunction from the `morse v 3.3.1`

package.
It returns measures of goodness-of-fit for predictions.

Function from the `morse v 3.3.1`

package.
Provide various criteria for assessment of the model performance:
(i) percentage of observation within the 95\
interval of the Posterior Prediction Check (PPC), the Normalised Root Mean
Square Error (NRMSE) and the Survival Probability Prediction Error (SPPE) as
recommended by the recent Scientific Opinion from EFSA (2018).

```
predict_Nsurv_check(object, ...)
## S3 method for class 'survFitPredict_Nsurv'
predict_Nsurv_check(object, ...)
```

`object` |
an object of class |

`...` |
Further arguments to be passed to generic methods |

The function return a list with three items:

`PPC` |
The criterion, in percent, compares the predicted median numbers
of survivors associated to their uncertainty limits with the observed numbers
of survivors. Based on experience, PPC resulting in less than |

`PPC_global` |
percentage of PPC for the whole data set by gathering replicates. |

`NRMSE` |
The criterion, in percent, is based on the classical root-mean-square error (RMSE), used to aggregate the magnitudes of the errors in predictions for various time-points into a single measure of predictive power. In order to provide a criterion expressed as a percentage, NRMSE is the normalised RMSE by the mean of the observations. |

`NRMSE_global` |
NRMSE for the whole data set by gathering replicates. |

`SPPE` |
The SPPE indicator, in percent, is negative (between |

@references
EFSA PPR Scientific Opinion (2018)
*Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms*
https://www.efsa.europa.eu/en/efsajournal/pub/5377

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