# Body Weight Covariate

[Generated automatically as a Tutorial summary]

## Model Description

Name:

  weight_covariate

Title:

  Body Weight Covariate

Author:

  PoPy for PK/PD

Abstract:

One compartment model with absorption compartment and CL/V parametrisation.
There are no random effects here. Each individual just has a different weight.
The weight is a covariate for the m[CL] clearance parameter for each individual.
Only the f[WT_EFFECT] and f[V] fixed effect parameters are estimated, other f[X] are fixed.

Keywords:

  one compartment model; dep_one_cmp_cl; weight; covariate effect

Input Script:

  `weight_covariate.pyml`

Diagram:

## Comparison

### True objective value

    -483.3718

### Final fitted objective value

    -486.0798

### Compare Main f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[V] | 15 | 20.3 | 20 | 2.61e-01 | 1.31%
f[WT_EFFECT] | 1 | 0.666 | 0.75 | 8.43e-02 | 11.25%

### Compare Noise f[X]

No Noise f[X] values to compare.

### Compare Variance f[X]

No Variance f[X] values to compare.

## Outputs

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

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.2610
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 0.6657

### Generated data .csv file

Synthetic Data:

  `synthetic_data.csv`

### Gen and Fit Summaries

- Gen: Body Weight Covariate (gen)

- Fit: Body Weight Covariate (fit)

## Inputs

### True f[X] values (for simulation)

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 0.7500

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

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 15.0000
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 1.0000