predict_Nsurv_check {odeGUTS}

Checking goodness-of-fit method for survFitPredict and survFitPredict_Nsurv objects

Description

Function 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).

Usage

predict_Nsurv_check(object, ...)

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

Arguments

object

an object of class survFitPredict_Nsurv

...

Further arguments to be passed to generic methods

Value

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 50\% of the observations within the uncertainty limits indicate poor model performance. A fit of 100\% may hide too large uncertainties of prediction (so covering all data).

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 0 and -100\%) for an underestimation of effects, and positive (between 0 and 100) for an overestimation of effects. An SPPE value of 0 means an exact prediction of the observed survival probability at the end of the exposure profile.

@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