predict_outliers#
- pharmpy.tools.predict_outliers(model, results, cutoff=3.0)[source]#
Predict outliers for a model using a machine learning model.
See the simeval documentation for a definition of the residual
- Parameters:
model (Model) – Pharmpy model
results (ModelfitResults) – ModelfitResults for the model
cutoff (float) – Cutoff threshold for a residual singalling an outlier
- Returns:
pd.DataFrame – Dataframe over the individuals with a residual column containing the raw predicted residuals and a outlier column with a boolean to tell whether the individual is an outlier or not.
Examples
>>> from pharmpy.modeling import * >>> from pharmpy.tools import * >>> model = load_example_model("pheno") >>> results = load_example_modelfit_results("pheno") >>> predict_outliers(model, results) residual outlier ID 1 -0.281443 False 2 1.057392 False 3 -0.119105 False 4 -0.846849 False 5 0.600540 False 6 1.014008 False 7 -0.750734 False 8 0.247175 False 9 0.117002 False 10 -0.835389 False 11 3.529582 True 12 -0.035670 False 13 0.292333 False 14 0.303278 False 15 -0.565949 False 16 -0.078192 False 17 -0.291295 False 18 2.466421 False 19 -0.402343 False 20 -0.699996 False 21 -0.567987 False 22 -0.526776 False 23 0.303918 False 24 -0.177588 False 25 1.272142 False 26 -0.390000 False 27 0.775876 False 28 -0.501528 False 29 -0.700951 False 30 -0.352599 False 31 0.294196 False 32 0.744014 False 33 -0.215364 False 34 0.208691 False 35 1.713130 False 36 0.300293 False 37 -0.810736 False 38 0.459877 False 39 -0.675125 False 40 -0.563835 False 41 -0.526945 False 42 4.449199 True 43 0.720714 False 44 -0.792248 False 45 -0.860923 False 46 -0.731858 False 47 -0.247131 False 48 1.894190 False 49 -0.282737 False 50 -0.153398 False 51 1.200546 False 52 0.902774 False 53 0.586427 False 54 0.183329 False 55 1.036930 False 56 -0.639868 False 57 -0.765279 False 58 -0.209665 False 59 -0.225693 False