# Proportional and Additive error model fitted to proportional noise only synthetic data.

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | po_gen_pa_fit
Title: | Proportional and Additive error model fitted to proportional noise only synthetic data.
Author: | Wright Dose Ltd
Abstract: |

One compartment model with a depot leading to a central compartment.
This model contains both proportional and additive error. The synthetic input data contains only proportional error, no additive error.

Keywords: | one compartment model; dep_one_cmp_cl; proportional and additive error
Input Script: | `po_gen_pa_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

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

## Outputs

### Final objective value

    -572.803857763

which required N. iterations and took 184.74 seconds

### Final fitted fixed effects

    f[PNOISE_STD] = 0.092518
    f[ANOISE_STD] = 0.001

### 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 | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.092518 | 0.5 | 0.814964 | 0.407482
f[ANOISE_STD] | 0.001 | 0.25 | 0.996 | 0.249

### Compare Variance f[X]