survFitPredict and
survFitPredict_Nsurv objectsIt returns measures of goodness-of-fit for predictions.
Provide various criteria for assessment of the model performance: (i) percentage of observation within the 95% credible interval of the Posterior Prediction Check (PPC), the Normalised Root Mean Square Error (NRMSE) and the Survival Probability Prediction Error (SPPE) as reccommended by the recent Scientific Opinion from EFSA (2018).
predict_Nsurv_check(object, ...)# S3 method for survFitPredict_Nsurv
predict_Nsurv_check(object, ...)
return a list of data.frame.
The function return a list with three items:
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).
percentage of PPC for the whole data set by gathering replicates.
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 for the whole data set by gathering replicates.
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
an object of class survFitPredict_Nsurv
Further arguments to be passed to generic methods