Diagonal matrix generation diagonal matrix fit¶
[Generated automatically as a Tutorial summary]
Model Description¶
| Name: | gen_diag_fit_diag |
|---|---|
| Title: | Diagonal 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 diagonal covariance matrix to generate the data and a diagonal covariance matrix to fit.
| Keywords: | one compartment model; dep_one_cmp_cl; diagonal matrix |
|---|---|
| Input Script: | gen_diag_fit_diag_tut.pyml |
| Diagram: |
Comparison¶
True objective value¶
-2214.8455
Final fitted objective value¶
-2215.9658
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 | 0.198 | 0.2 | 2.28e-03 | 1.14% |
| f[CL_isv;V_isv] | 0 | 0 | 0 | 0.00e+00 | inf |
| f[V_isv;CL_isv] | 0 | 0 | 0 | 0.00e+00 | inf |
| f[V_isv] | 0.01 | 0.117 | 0.1 | 1.67e-02 | 16.70% |
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] = [
[ 0.1977, 0.0000 ],
[ 0.0000, 0.1167 ],
]
Generated data .csv file¶
| Synthetic Data: | synthetic_data.csv |
|---|
Gen and Fit Summaries¶
- Gen: Diagonal matrix generation diagonal matrix fit (gen)
- Fit: Diagonal 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] = [
[ 0.2000, 0.0000 ],
[ 0.0000, 0.1000 ],
]
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 ],
]