# Model containing both proportional and additive error

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | pa_gen_pa_fit
Title: | Model containing both proportional and additive error
Author: | Wright Dose Ltd
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: |

Failed to create compartment diagram

### True f[X] values

    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05

### Starting f[X] values

    f[PNOISE_STD] = 0.5
    f[ANOISE_STD] = 0.25

## Outputs

### Generating and Fitting Summaries

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

### Fitted f[X] values

    f[PNOISE_STD] = 0.095119
    f[ANOISE_STD] = 0.045335

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    -395.516850454

### Final fitted objective value

    -396.659750362

### Compare Main f[X]

No Main f[X] values to compare.

### Compare Noise f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[PNOISE_STD] | 0.5 | 0.0951 | 0.1 | 4.88% | 4.88e-03
f[ANOISE_STD] | 0.25 | 0.0453 | 0.05 | 9.33% | 4.66e-03

### Compare Variance f[X]

No Variance f[X] values to compare.