# Body Weight Covariate

[Generated automatically as a Fitting 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_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

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

### Compare Noise f[X]

### Compare Variance f[X]

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

| allOBS_vs_TIME

## Outputs

### Final objective value

    -486.0798

which required 1.6 iterations and took 11.74 seconds

### 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

### 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] = 15.0000
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0500
    f[WT_EFFECT] = 1.0000