# Diagonal matrix generation full matrix fit

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | gen_diag_fit_full
Title: | Diagonal matrix generation full 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 full covariance matrix to fit.

Keywords: | dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix
Input Script: | `gen_diag_fit_full_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    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

### Final objective value

    -2173.2849

which required 11 iterations and took 89.15 seconds

### Final fitted fixed effects

    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] = [
        [ 0.1775, 0.0106 ],
        [ 0.0106, 0.0883 ]
    ]

### Fitted parameter .csv files

Fixed Effects: | `fx_params.csv (fit)`
Random Effects: | `rx_params.csv (fit)`
Model params: | `mx_params.csv (fit)`
State values: | `sx_params.csv (fit)`
Predictions: | `px_params.csv (fit)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

### Compare Noise f[X]

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[CL_isv] | 0.1775 | 0.0100 | 16.7509 | 0.1675
f[CL_isv;V_isv] | 0.0106 | 0.0000 | INF | 0.0106
f[V_isv;CL_isv] | 0.0106 | 0.0000 | INF | 0.0106
f[V_isv] | 0.0883 | 0.0100 | 7.8253 | 0.0783