# Diagonal matrix generation diagonal matrix fit

[Generated automatically as a Fitting 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_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    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

### Final objective value

    -2172.76714255

which required N. iterations and took 1878.88 seconds

### Final fitted fixed effects

    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] = [
        [ 0.175, 0 ],
        [ 0, 0.087425 ]
    ]

### 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] | 0.174996 | 0.01 | 16.4996 | 0.164996
f[CL_isv;V_isv] | 0 | 0 |  | 0
f[V_isv;CL_isv] | 0 | 0 |  | 0
f[V_isv] | 0.0874254 | 0.01 | 7.74254 | 0.0774254