Population Two Compartment Model and Inter-subject Variance
[Generated automatically as a Tutorial summary]
Model Description
- Name:
iv_two_cmp_cl_isv
- Title:
Population Two Compartment Model and Inter-subject Variance
- Author:
PoPy for PK/PD
- Abstract:
- Keywords:
two compartment model; iv_two_cmp_cl; proportional noise; additive noise
- Input Script:
- Diagram:
Comparison
True objective value
-1688.3290
Final fitted objective value
-1654.2501
Compare Main f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[CL] |
1 |
2.33 |
2 |
3.26e-01 |
16.29% |
f[V1] |
15 |
7.37 |
10 |
2.63e+00 |
26.31% |
f[Q] |
1 |
1.07 |
1.2 |
1.35e-01 |
11.23% |
f[V2] |
25 |
10.2 |
20 |
9.80e+00 |
49.00% |
Compare Noise f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[PNOISE] |
0.05 |
0.0123 |
0.01 |
2.32e-03 |
23.16% |
f[ANOISE] |
0.05 |
0.00925 |
0.01 |
7.46e-04 |
7.46% |
Compare Variance f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[CL_isv] |
0.2 |
0.191 |
0.1 |
9.14e-02 |
91.40% |
f[CL_isv;V1_isv] |
0 |
0.209 |
0 |
2.09e-01 |
inf |
f[CL_isv;Q_isv] |
0 |
-0.0294 |
0 |
2.94e-02 |
inf |
f[CL_isv;V2_isv] |
0 |
-0.094 |
0 |
9.40e-02 |
inf |
f[V1_isv;CL_isv] |
0 |
0.209 |
0 |
2.09e-01 |
inf |
f[V1_isv] |
0.1 |
0.686 |
0.2 |
4.86e-01 |
243.14% |
f[V1_isv;Q_isv] |
0 |
-0.0546 |
0 |
5.46e-02 |
inf |
f[V1_isv;V2_isv] |
0 |
0.152 |
0 |
1.52e-01 |
inf |
f[Q_isv;CL_isv] |
0 |
-0.0294 |
0 |
2.94e-02 |
inf |
f[Q_isv;V1_isv] |
0 |
-0.0546 |
0 |
5.46e-02 |
inf |
f[Q_isv] |
0.05 |
0.146 |
0.1 |
4.62e-02 |
46.19% |
f[Q_isv;V2_isv] |
0 |
0.0294 |
0 |
2.94e-02 |
inf |
f[V2_isv;CL_isv] |
0 |
-0.094 |
0 |
9.40e-02 |
inf |
f[V2_isv;V1_isv] |
0 |
0.152 |
0 |
1.52e-01 |
inf |
f[V2_isv;Q_isv] |
0 |
0.0294 |
0 |
2.94e-02 |
inf |
f[V2_isv] |
0.2 |
0.683 |
0.5 |
1.83e-01 |
36.64% |
Outputs
Fitted f[X] values (after fitting)
f[CL] = 2.3258
f[V1] = 7.3693
f[Q] = 1.0653
f[V2] = 10.1994
f[PNOISE] = 0.0123
f[ANOISE] = 0.0093
f[CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1914, 0.2094, -0.0294, -0.0940 ],
[ 0.2094, 0.6863, -0.0546, 0.1520 ],
[ -0.0294, -0.0546, 0.1462, 0.0294 ],
[ -0.0940, 0.1520, 0.0294, 0.6832 ],
]
Generated data .csv file
- Synthetic Data:
Gen and Fit Summaries
Inputs
True f[X] values (for simulation)
f[CL] = 2.0000
f[V1] = 10.0000
f[Q] = 1.2000
f[V2] = 20.0000
f[PNOISE] = 0.0100
f[ANOISE] = 0.0100
f[CL_isv,V1_isv,Q_isv,V2_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[V1] = 15.0000
f[Q] = 1.0000
f[V2] = 25.0000
f[PNOISE] = 0.0500
f[ANOISE] = 0.0500
f[CL_isv,V1_isv,Q_isv,V2_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 ],
]