Population Two Compartment Model using VSS transform and Inter-subject Variance
[Generated automatically as a Tutorial summary]
Model Description
- Name:
iv_two_cmp_vss_isv
- Title:
Population Two Compartment Model using VSS transform and Inter-subject Variance
- Author:
PoPy for PK/PD
- Abstract:
- Keywords:
two compartment model; iv_two_cmp_vss; proportional noise; additive noise; vss_transform
- Input Script:
- Diagram:
Comparison
True objective value
-1693.2929
Final fitted objective value
-1710.7957
Compare Main f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[CL] |
1 |
2.02 |
2 |
2.09e-02 |
1.05% |
f[V] |
15 |
9.89 |
10 |
1.09e-01 |
1.09% |
f[Q] |
1 |
1.08 |
1.2 |
1.25e-01 |
10.39% |
f[VSS] |
25 |
38.4 |
50 |
1.16e+01 |
23.20% |
Compare Noise f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[PNOISE] |
0.05 |
0.00793 |
0.01 |
2.07e-03 |
20.67% |
f[ANOISE] |
0.05 |
0.00982 |
0.01 |
1.82e-04 |
1.82% |
Compare Variance f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[CL_isv] |
0.2 |
0.1… |
0.1 |
2.71e-05 |
0.03% |
f[CL_isv;V_isv] |
0 |
0.039 |
0 |
3.90e-02 |
inf |
f[CL_isv;Q_isv] |
0 |
0.000642 |
0 |
6.42e-04 |
inf |
f[CL_isv;VSS_isv] |
0 |
-0.0263 |
0 |
2.63e-02 |
inf |
f[V_isv;CL_isv] |
0 |
0.039 |
0 |
3.90e-02 |
inf |
f[V_isv] |
0.1 |
0.169 |
0.2 |
3.14e-02 |
15.69% |
f[V_isv;Q_isv] |
0 |
0.0224 |
0 |
2.24e-02 |
inf |
f[V_isv;VSS_isv] |
0 |
0.0106 |
0 |
1.06e-02 |
inf |
f[Q_isv;CL_isv] |
0 |
0.000642 |
0 |
6.42e-04 |
inf |
f[Q_isv;V_isv] |
0 |
0.0224 |
0 |
2.24e-02 |
inf |
f[Q_isv] |
0.05 |
0.0526 |
0.1 |
4.74e-02 |
47.38% |
f[Q_isv;VSS_isv] |
0 |
0.00838 |
0 |
8.38e-03 |
inf |
f[VSS_isv;CL_isv] |
0 |
-0.0263 |
0 |
2.63e-02 |
inf |
f[VSS_isv;V_isv] |
0 |
0.0106 |
0 |
1.06e-02 |
inf |
f[VSS_isv;Q_isv] |
0 |
0.00838 |
0 |
8.38e-03 |
inf |
f[VSS_isv] |
0.2 |
0.334 |
0.5 |
1.66e-01 |
33.29% |
Outputs
Fitted f[X] values (after fitting)
f[CL] = 2.0209
f[V] = 9.8915
f[Q] = 1.0753
f[VSS] = 38.3983
f[PNOISE] = 0.0079
f[ANOISE] = 0.0098
f[CL_isv,V_isv,Q_isv,VSS_isv] = [
[ 0.1000, 0.0390, 0.0006, -0.0263 ],
[ 0.0390, 0.1686, 0.0224, 0.0106 ],
[ 0.0006, 0.0224, 0.0526, 0.0084 ],
[ -0.0263, 0.0106, 0.0084, 0.3335 ],
]
Generated data .csv file
- Synthetic Data:
Gen and Fit Summaries
Inputs
True f[X] values (for simulation)
f[CL] = 2.0000
f[V] = 10.0000
f[Q] = 1.2000
f[VSS] = 50.0000
f[PNOISE] = 0.0100
f[ANOISE] = 0.0100
f[CL_isv,V_isv,Q_isv,VSS_isv] = [
[ 0.1000, 0.0000, 0.0000, 0.0000 ],
[ 0.0000, 0.2000, 0.0000, 0.0000 ],
[ 0.0000, 0.0000, 0.1000, 0.0000 ],
[ 0.0000, 0.0000, 0.0000, 0.5000 ],
]
Starting f[X] values (before fitting)
f[CL] = 1.0000
f[V] = 15.0000
f[Q] = 1.0000
f[VSS] = 25.0000
f[PNOISE] = 0.0500
f[ANOISE] = 0.0500
f[CL_isv,V_isv,Q_isv,VSS_isv] = [
[ 0.2000, 0.0000, 0.0000, 0.0000 ],
[ 0.0000, 0.1000, 0.0000, 0.0000 ],
[ 0.0000, 0.0000, 0.0500, 0.0000 ],
[ 0.0000, 0.0000, 0.0000, 0.2000 ],
]