# Mixed error model fitted to mixed error data, but with incorrect variance definition

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | pa_gen_pa_fit_badvar
Title: | Mixed error model fitted to mixed error data, but with incorrect variance definition
Author: | Wright Dose Ltd
Abstract: |

One compartment model with a depot leading to a central compartment
This model contains both proportional and additive error, but erroneously sums the standard deviations.

Keywords: | one compartment model; dep_one_cmp_cl; proportional and additive error
Input Script: | `pa_gen_pa_fit_badvar.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

    -396.750814736

which required N. iterations and took 230.81 seconds

### Final fitted fixed effects

    f[PNOISE_STD] = 0.069974
    f[ANOISE_STD] = 0.040035

### 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] | 0.0699739 | 0.5 | 0.860052 | 0.430026
f[ANOISE_STD] | 0.0400346 | 0.25 | 0.839862 | 0.209965

### Compare Variance f[X]