# Additive noise model fitted to proportional noise only synthetic data.

[Generated automatically as a Fitting summary]

## Model Description

Name:

  po_gen_ao_fit

Title:

  Additive noise model fitted to proportional noise only synthetic data.

Author:

  PoPy for PK/PD

Abstract:

One compartment model with a depot leading to a central compartment.
This model contains additive error only. The synthetic input data only contains additive noise too.

Keywords:

  one compartment model; dep_one_cmp_cl; additive error

Input Script:

  `po_gen_ao_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[ANOISE_STD] | 0.2500 | 0.1099 | 0.1401 | 0.5606

### Compare Variance f[X]

### Population observed (fit) plots

| indOBS_vs_TIME

### Population simulated (sim) plots

| indOBS_vs_TIME

## Outputs

### Final objective value

    -341.7118

which required 1.6 iterations and took 10.34 seconds

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

    f[ANOISE_STD] = 0.1099

### 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)`

Likelihoods:

  `lx_params.csv (fit)`

## Inputs

Input Data:

  `synthetic_data.csv`

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

    f[ANOISE_STD] = 0.2500