- Language: en
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)