# Full matrix generation diagonal matrix fit

[Generated automatically as a Tutorial summary]

## Model Description

Name:

  gen_full_fit_diag

Title:

  Full matrix generation diagonal matrix fit

Author:

  PoPy for PK/PD

Abstract:

One compartment model with absorption compartment and CL/V parametrisation.
This script uses a full covariance matrix to generate the data, but a diagonal matrix to fit.

Keywords:

  dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix

Input Script:

  `gen_full_fit_diag_tut.pyml`

Diagram:

## Comparison

### True objective value

    -2181.8274

### Final fitted objective value

    -2188.3227

### Compare Main f[X]

No Main f[X] values to compare.

### Compare Noise f[X]

No Noise f[X] values to compare.

### Compare Variance f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[CL_isv] | 0.01 | 0.124 | 0.15 | 2.59e-02 | 17.28%
f[CL_isv;V_isv] | 0 | 0 | 0.05 | 5.00e-02 | 100.00%
f[V_isv;CL_isv] | 0 | 0 | 0.05 | 5.00e-02 | 100.00%
f[V_isv] | 0.01 | 0.119 | 0.15 | 3.11e-02 | 20.74%

## Outputs

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

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv,V_isv] = [
        [ 0.1241, 0.0000 ],
        [ 0.0000, 0.1189 ],
    ]

### Generated data .csv file

Synthetic Data:

  `synthetic_data.csv`

### Gen and Fit Summaries

- Gen: Full matrix generation diagonal matrix fit (gen)

- Fit: Full matrix generation diagonal matrix fit (fit)

## Inputs

### True f[X] values (for simulation)

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv,V_isv] = [
        [ 0.1500, 0.0500 ],
        [ 0.0500, 0.1500 ],
    ]

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

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv,V_isv] = [
        [ 0.0100, 0.0000 ],
        [ 0.0000, 0.0100 ],
    ]