# 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] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05
    f[CL_isv,V_isv] = [
        [ 0.15, 0.05 ],
        [ 0.05, 0.15 ]
    ]

### Starting f[X] values

    f[KA] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05
    f[CL_isv,V_isv] = [
        [ 0.01, 0 ],
        [ 0, 0.01 ]
    ]

## 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] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05
    f[CL_isv,V_isv] = [
        [ 0.13157, 0.060025 ],
        [ 0.060025, 0.13958 ]
    ]

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    -2209.43447167

### Final fitted objective value

    -2214.69986838

### 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 | 0.132 | 0.15 | 12.29% | 1.84e-02
f[CL_isv;V_isv] | 0 | 0.06 | 0.05 | 20.05% | 1.00e-02
f[V_isv;CL_isv] | 0 | 0.06 | 0.05 | 20.05% | 1.00e-02
f[V_isv] | 0.01 | 0.14 | 0.15 | 6.95% | 1.04e-02