# Full matrix generation diagonal matrix fit

[Generated automatically as a Fitting summary]

## Model Description

Name:

  gen_full_fit_diag

Title:

  Full matrix generation diagonal matrix fit

Author:

  PoPy for PK/PD

Abstract:

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

Keywords:

  dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix

Input Script:

  `gen_full_fit_diag_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

### Compare Noise f[X]

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[CL_isv] | 0.0100 | 0.1241 | 0.1141 | 11.4085
f[CL_isv;V_isv] | 0.0000 | 0.0000 | 0.0000 | INF
f[V_isv;CL_isv] | 0.0000 | 0.0000 | 0.0000 | INF
f[V_isv] | 0.0100 | 0.1189 | 0.1089 | 10.8889

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

| allOBS_vs_TIME

## Outputs

### Final objective value

    -2188.3227

which required 1.13 iterations and took 181.52 seconds

### Fitted f[X] values (after fitting)

    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] = [
        [ 0.1241, 0.0000 ],
        [ 0.0000, 0.1189 ],
    ]

### 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)`

Likelihoods:

  `lx_params.csv (fit)`

## Inputs

Input Data:

  `cx_obs_params.csv`

### Starting f[X] values (before fitting)

    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 ],
    ]