# Diagonal matrix generation diagonal matrix fit

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | gen_diag_fit_diag
Title: | Diagonal matrix generation diagonal matrix fit
Author: | Wright Dose Ltd
Abstract: |

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

Keywords: | one compartment model; dep_one_cmp_cl; diagonal matrix
Input Script: | `gen_diag_fit_diag_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] = [
        [ 0.2, 0 ],
        [ 0, 0.1 ]
    ]

### 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: Diagonal matrix generation diagonal matrix fit (gen)
- Fit: Diagonal matrix generation diagonal 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] = [
        [ 0.17503, 0 ],
        [ 0, 0.087207 ]
    ]

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    -2170.87328898

### Final fitted objective value

    -2172.76865244

### 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 | 0.175 | 0.2 | 12.49% | 2.50e-02
f[CL_isv;V_isv] | 0 | 0 | 0 | inf | 0.00e+00
f[V_isv;CL_isv] | 0 | 0 | 0 | inf | 0.00e+00
f[V_isv] | 0.01 | 0.0872 | 0.1 | 12.79% | 1.28e-02