# Full matrix generation full matrix fit

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | gen_full_fit_full
Title: | Full matrix generation full matrix fit
Author: | Wright Dose Ltd
Abstract: |

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

Keywords: | dep_one_cmp_cl; one compartment model; full matrix
Input Script: | `gen_full_fit_full_tut.pyml`
Diagram: |

Failed to create compartment diagram

### True f[X] values

    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

    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, 0.0010 ],
        [ 0.0010, 0.0100 ],
    ]

## Outputs

### Generating and Fitting Summaries

- Gen: Full matrix generation full matrix fit (gen)
- Fit: Full matrix generation full matrix fit (fit)

### Fitted f[X] values

    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] = [
        [ 0.1361, 0.0500 ],
        [ 0.0500, 0.1660 ],
    ]

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    -2106.7004

### Final fitted objective value

    -2108.2792

### 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 | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[CL_isv] | 0.01 | 0.136 | 0.15 | 9.29% | 1.39e-02
f[CL_isv;V_isv] | 0.001 | 0.05… | 0.05 | 0.01% | 5.03e-06
f[V_isv;CL_isv] | 0.001 | 0.05… | 0.05 | 0.01% | 5.03e-06
f[V_isv] | 0.01 | 0.166 | 0.15 | 10.66% | 1.60e-02