criteriaCheck {BeeGUTS}

Computes PPC and NRMSE as defined in EFSA 2018

Description

Computes PPC and NRMSE as defined in EFSA 2018

Usage

criteriaCheck(x)

Arguments

x

an object of class beeSurvFit or beeSurvPred

Value

The function returns a list with three items:

PPC

The criterion, in percent, compares the predicted median number of survivors associated to their uncertainty limits with the observed numbers of survivors. Based on experience, PPC resulting in more than 50\% of the observations within the uncertainty limits indicate good model performance (EFSA 2018). A fit of 100\% may hide too large uncertainties of prediction (so covering all data).

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. EFSA (2018) recognised that a NRMSE of less than 50% indicates good model performance

SPPE

A list with the Survival Probability Prediction Error per dataset and condition. Each dataset is in a sublist.

@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

@example data(fitBetacyfluthrin_Chronic) out <- criteriaCheck(fitBetacyfluthrin_Chronic)