# Model containing both proportional and additive error

[Generated automatically as a Tutorial summary]

## Model Description

Name:

  pa_gen_pa_fit

Title:

  Model containing both proportional and additive error

Author:

  PoPy for PK/PD

Abstract:

One compartment model with a depot leading to a central compartment.
This model contains both proportional and additive error.

Keywords:

  one compartment model; one_two_cmp_cl; proportional and additive error

Input Script:

  `pa_tut.pyml`

Diagram:

## Comparison

### True objective value

    -395.5169

### Final fitted objective value

    -396.6598

### Compare Main f[X]

No Main f[X] values to compare.

### Compare Noise f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[PNOISE_STD] | 0.5 | 0.0951 | 0.1 | 4.91e-03 | 4.91%
f[ANOISE_STD] | 0.25 | 0.0453 | 0.05 | 4.68e-03 | 9.36%

### Compare Variance f[X]

No Variance f[X] values to compare.

## Outputs

### Fitted f[X] values (after fitting)

    f[PNOISE_STD] = 0.0951
    f[ANOISE_STD] = 0.0453

### Generated data .csv file

Synthetic Data:

  `synthetic_data.csv`

### Gen and Fit Summaries

- Gen: Model containing both proportional and additive error (gen)

- Fit: Model containing both proportional and additive error (fit)

## Inputs

### True f[X] values (for simulation)

    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500

### Starting f[X] values (before fitting)

    f[PNOISE_STD] = 0.5000
    f[ANOISE_STD] = 0.2500