# Model containing both proportional and additive error

[Generated automatically as a Fitting 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_gen_pa_fit_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

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

## Outputs

### Final objective value

    -396.6598

which required 1.6 iterations and took 0.54 seconds

### Final fitted fixed effects

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

### Fitted parameter .csv files

Fixed Effects: | `fx_params.csv (fit)`
Random Effects: | `rx_params.csv (fit)`
Model params: | `mx_params.csv (fit)`
State values: | `sx_params.csv (fit)`
Predictions: | `px_params.csv (fit)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.5000 | 0.0951 | 0.8098 | 0.4049
f[ANOISE_STD] | 0.2500 | 0.0453 | 0.8187 | 0.2047

### Compare Variance f[X]