# Model containing additive error only and additive error only input data

[Generated automatically as a Fitting summary]

## Model Description

Name:

  ao_gen_ao_fit

Title:

  Model containing additive error only and additive error only input data

Author:

  PoPy for PK/PD

Abstract:

One compartment model with a depot leading to a central compartment.
This model contains additive error only and the synthetic input data contains additive error only.

Keywords:

  one compartment model; dep_one_cmp_cl; additive error

Input Script:

  `ao_gen_ao_fit_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 | 0.0464 | 0.2036 | 0.8145

### Compare Variance f[X]

### Population simulated (sim) plots

| allOBS_vs_TIME
| CWRES(DV_CENTRAL)_wrt_IPRED(DV_CENTRAL)
| indOBS_vs_TIME
| RES(DV_CENTRAL)_wrt_IPRED(DV_CENTRAL)
| WRES(DV_CENTRAL)_wrt_PPRED(DV_CENTRAL)

## Outputs

### Final objective value

    -514.1953

which required 1.7 iterations and took 9.03 seconds

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

    f[ANOISE_STD] = 0.0464

### 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:

  `cx_obs_params.csv`

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

    f[ANOISE_STD] = 0.2500