# Body Weight Covariate

[Generated automatically as a Fitting 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_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    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

## Outputs

### Final objective value

    -486.0798

which required 1.6 iterations and took 4.97 seconds

### Final fitted fixed effects

    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

### 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)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[V] | 15.0000 | 20.2610 | 0.3507 | 5.2610
f[WT_EFFECT] | 1.0000 | 0.6657 | 0.3343 | 0.3343

### Compare Noise f[X]

### Compare Variance f[X]