Full matrix generation diagonal matrix fit¶
[Generated automatically as a Tutorial summary]
Model Description¶
| Name: | gen_full_fit_diag |
|---|---|
| Title: | Full matrix generation diagonal matrix fit |
| Author: | PoPy for PK/PD |
| Abstract: |
One compartment model with absorption compartment and CL/V parametrisation.
This script uses a full covariance matrix to generate the data, but a diagonal matrix to fit.
| Keywords: | dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix |
|---|---|
| Input Script: | gen_full_fit_diag_tut.pyml |
| Diagram: |
Comparison¶
True objective value¶
-2097.5793
Final fitted objective value¶
-2100.7508
Compare Main f[X]¶
No Main f[X] values to compare.
Compare Noise f[X]¶
No Noise f[X] values to compare.
Compare Variance f[X]¶
| Name | Initial | Fitted | True | Abs. Error | Prop. Error |
|---|---|---|---|---|---|
| f[CL_isv] | 0.01 | 0.123 | 0.15 | 2.72e-02 | 18.12% |
| f[CL_isv;V_isv] | 0 | 0 | 0.05 | 5.00e-02 | 100.00% |
| f[V_isv;CL_isv] | 0 | 0 | 0.05 | 5.00e-02 | 100.00% |
| f[V_isv] | 0.01 | 0.159 | 0.15 | 8.91e-03 | 5.94% |
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
[ 0.1228, 0.0000 ],
[ 0.0000, 0.1589 ],
]
Generated data .csv file¶
| Synthetic Data: | synthetic_data.csv |
|---|
Gen and Fit Summaries¶
- Gen: Full matrix generation diagonal matrix fit (gen)
- Fit: Full matrix generation diagonal matrix fit (fit)
Inputs¶
True f[X] values (for simulation)¶
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
[ 0.1500, 0.0500 ],
[ 0.0500, 0.1500 ],
]
Starting f[X] values (before fitting)¶
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
[ 0.0100, 0.0000 ],
[ 0.0000, 0.0100 ],
]