evaluate_individual_prediction#

pharmpy.modeling.evaluate_individual_prediction(model, etas=None, parameters=None, dataset=None)[source]#

Evaluate the numeric individual prediction

The prediction is evaluated at the current model parameter values or optionally at the given parameter values. The evaluation is done for each data record in the model dataset or optionally using the dataset argument. The evaluation is done at the current eta values or optionally at the given eta values.

This function currently only support models without ODE systems

Parameters:

model (Model) – Pharmpy model
etas (dict) – Optional dictionary of eta values
parameters (dict) – Optional dictionary of parameters and values
dataset (pd.DataFrame) – Optional dataset

Returns:

pd.Series – Individual predictions

Examples

>>> from pharmpy.modeling import load_example_model, evaluate_individual_prediction
>>> from pharmpy.tools import load_example_modelfit_results
>>> model = load_example_model("pheno_linear")
>>> results = load_example_modelfit_results("pheno_linear")
>>> etas = results.individual_estimates
>>> evaluate_individual_prediction(model, etas=etas)
0      17.771084
1      28.881859
2      11.441728
3      21.113050
4      29.783055
         ...
150    25.375041
151    31.833395
152    22.876707
153    31.905095
154    38.099690
Name: IPRED, Length: 155, dtype: float64