# Body Weight Covariate

[Generated automatically as a Generation 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_gen.pyml`

Diagram:

## Outputs

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

| allOBS_vs_TIME

### Generated parameter .csv files

Fixed Effects:

  `fx_params.csv (gen)`

Random Effects:

  `rx_params.csv (gen)`

Model params:

  `mx_params.csv (gen)`

State values:

  `sx_params.csv (gen)`

Predictions:

  `px_params.csv (gen)`

Observations:

  `synthetic_data.csv (gen)`

## 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