# Diagonal matrix generation diagonal matrix fit using separate univariate normals

[Generated automatically as a Fitting summary]

## Model Description

Name:

  gen_indep_fit_indep

Title:

  Diagonal matrix generation diagonal matrix fit using separate univariate normals

Author:

  PoPy for PK/PD

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.
Note here the ‘diagonal matrix’ is implemented as two separate univariate normal distributions, which is equivalent.

Keywords:

  dep_one_cmp_cl; one compartment model; diagonal matrix

Input Script:

  `gen_indep_fit_indep_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 | 0.1784 | 0.1684 | 16.8414
f[V_isv] | 0.0100 | 0.0881 | 0.0781 | 7.8089

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

| allOBS_vs_TIME

## Outputs

### Final objective value

    -2172.8028

which required 1.8 iterations and took 149.85 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] = 0.1784
    f[V_isv] = 0.0881

### 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] = 0.0100
    f[V_isv] = 0.0100