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:
- Diagram:
Comparison
True objective value
-2181.8274
Final fitted objective value
-2188.3227
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 |
0.124 |
0.15 |
2.59e-02 |
17.28% |
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 |
0.119 |
0.15 |
3.11e-02 |
20.74% |
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] = [
[ 0.1241, 0.0000 ],
[ 0.0000, 0.1189 ],
]
Generated data .csv file
- Synthetic Data:
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 ],
]