# Body Weight Covariate

[Generated automatically as a Generation summary]

## Inputs

### Description

Name: | weight_covariate
Title: | Body Weight Covariate
Author: | Wright Dose Ltd
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: |

### True f[X] values

    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

## Outputs

### 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)`
Synthetic Data: | `synthetic_data.csv (gen)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots