# Full matrix generation diagonal matrix fit

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | gen_full_fit_diag
Title: | Full matrix generation diagonal 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, 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: |

### 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, 0.0000 ],
        [ 0.0000, 0.0100 ]
    ]

## Outputs

### Generating and Fitting Summaries

- Gen: Full matrix generation diagonal matrix fit (gen)
- Fit: Full matrix generation diagonal 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] = [
        [ 0.1176, 0.0000 ],
        [ 0.0000, 0.1240 ]
    ]

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    -2181.8259

### Final fitted objective value

    -2187.5436

### 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 | 0.118 | 0.15 | 21.61% | 3.24e-02
f[CL_isv;V_isv] | 0 | 0 | 0.05 | 100.00% | 5.00e-02
f[V_isv;CL_isv] | 0 | 0 | 0.05 | 100.00% | 5.00e-02
f[V_isv] | 0.01 | 0.124 | 0.15 | 17.32% | 2.60e-02