# Full matrix generation full matrix fit

[Generated automatically as a Fitting 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_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, 0.0010 ],
        [ 0.0010, 0.0100 ],
    ]

## Outputs

### Final objective value

    -2108.2792

which required 1.11 iterations and took 1008.95 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] = [
        [ 0.1361, 0.0500 ],
        [ 0.0500, 0.1660 ],
    ]

### 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 | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[CL_isv] | 0.0100 | 0.1361 | 12.6072 | 0.1261
f[CL_isv;V_isv] | 0.0010 | 0.0500 | 48.9950 | 0.0490
f[V_isv;CL_isv] | 0.0010 | 0.0500 | 48.9950 | 0.0490
f[V_isv] | 0.0100 | 0.1660 | 15.5991 | 0.1560