- Language: en
First order absorption model with peripheral compartment
[Generated automatically as a Tutorial summary]
Model Description
- Name:
builtin_tut_example
- Title:
First order absorption model with peripheral compartment
- Author:
PoPy for PK/PD
- Abstract:
- Keywords:
tutorial; pk; advan4; dep_two_cmp; first order
- Input Script:
- Diagram:
Comparison
True objective value
-873.1410
Final fitted objective value
-894.0829
Compare Main f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[KA] |
1 |
0.176 |
0.2 |
2.38e-02 |
11.89% |
f[CL] |
1 |
2.05 |
2 |
4.90e-02 |
2.45% |
f[V1] |
20 |
47 |
50 |
2.97e+00 |
5.94% |
f[Q] |
0.5 |
1.24 |
1 |
2.40e-01 |
24.03% |
f[V2] |
100 |
62.1 |
80 |
1.79e+01 |
22.36% |
Compare Noise f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[PNOISE] |
0.1 |
0.141 |
0.15 |
8.87e-03 |
5.92% |
Compare Variance f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[KA_isv] |
0.05 |
0.0714 |
0.1 |
2.86e-02 |
28.60% |
f[KA_isv;CL_isv] |
0.01 |
0.0735 |
0.01 |
6.35e-02 |
635.03% |
f[KA_isv;V1_isv] |
0.01 |
-0.00747 |
0.01 |
1.75e-02 |
174.72% |
f[KA_isv;Q_isv] |
0.01 |
-0.14 |
0.01 |
1.50e-01 |
1499.49% |
f[KA_isv;V2_isv] |
0.01 |
-0.0601 |
0.01 |
7.01e-02 |
701.41% |
f[CL_isv;KA_isv] |
0.01 |
0.0735 |
0.01 |
6.35e-02 |
635.03% |
f[CL_isv] |
0.05 |
0.142 |
0.03 |
1.12e-01 |
373.92% |
f[CL_isv;V1_isv] |
0.01 |
-0.00395 |
-0.01 |
6.05e-03 |
60.53% |
f[CL_isv;Q_isv] |
0.01 |
-0.126 |
0.02 |
1.46e-01 |
728.10% |
f[CL_isv;V2_isv] |
0.01 |
-0.232 |
0.02 |
2.52e-01 |
1258.86% |
f[V1_isv;KA_isv] |
0.01 |
-0.00747 |
0.01 |
1.75e-02 |
174.72% |
f[V1_isv;CL_isv] |
0.01 |
-0.00395 |
-0.01 |
6.05e-03 |
60.53% |
f[V1_isv] |
0.05 |
0.0894 |
0.09 |
5.51e-04 |
0.61% |
f[V1_isv;Q_isv] |
0.01 |
0.0483 |
0.01 |
3.83e-02 |
383.12% |
f[V1_isv;V2_isv] |
0.01 |
0.106 |
0.01 |
9.59e-02 |
958.84% |
f[Q_isv;KA_isv] |
0.01 |
-0.14 |
0.01 |
1.50e-01 |
1499.49% |
f[Q_isv;CL_isv] |
0.01 |
-0.126 |
0.02 |
1.46e-01 |
728.10% |
f[Q_isv;V1_isv] |
0.01 |
0.0483 |
0.01 |
3.83e-02 |
383.12% |
f[Q_isv] |
0.05 |
0.301 |
0.07 |
2.31e-01 |
330.59% |
f[Q_isv;V2_isv] |
0.01 |
0.0929 |
0.01 |
8.29e-02 |
829.25% |
f[V2_isv;KA_isv] |
0.01 |
-0.0601 |
0.01 |
7.01e-02 |
701.41% |
f[V2_isv;CL_isv] |
0.01 |
-0.232 |
0.02 |
2.52e-01 |
1258.86% |
f[V2_isv;V1_isv] |
0.01 |
0.106 |
0.01 |
9.59e-02 |
958.84% |
f[V2_isv;Q_isv] |
0.01 |
0.0929 |
0.01 |
8.29e-02 |
829.25% |
f[V2_isv] |
0.05 |
0.655 |
0.05 |
6.05e-01 |
1210.21% |
Outputs
Fitted f[X] values (after fitting)
f[KA] = 0.1762
f[CL] = 2.0490
f[V1] = 47.0285
f[Q] = 1.2403
f[V2] = 62.1150
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0714, 0.0735, -0.0075, -0.1399, -0.0601 ],
[ 0.0735, 0.1422, -0.0039, -0.1256, -0.2318 ],
[ -0.0075, -0.0039, 0.0894, 0.0483, 0.1059 ],
[ -0.1399, -0.1256, 0.0483, 0.3014, 0.0929 ],
[ -0.0601, -0.2318, 0.1059, 0.0929, 0.6551 ],
]
f[PNOISE] = 0.1411
Generated data .csv file
- Synthetic Data:
Gen and Fit Summaries
Inputs
True f[X] values (for simulation)
f[KA] = 0.2000
f[CL] = 2.0000
f[V1] = 50.0000
f[Q] = 1.0000
f[V2] = 80.0000
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1000, 0.0100, 0.0100, 0.0100, 0.0100 ],
[ 0.0100, 0.0300, -0.0100, 0.0200, 0.0200 ],
[ 0.0100, -0.0100, 0.0900, 0.0100, 0.0100 ],
[ 0.0100, 0.0200, 0.0100, 0.0700, 0.0100 ],
[ 0.0100, 0.0200, 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1500
Starting f[X] values (before fitting)
f[KA] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[Q] = 0.5000
f[V2] = 100.0000
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0500, 0.0100, 0.0100, 0.0100, 0.0100 ],
[ 0.0100, 0.0500, 0.0100, 0.0100, 0.0100 ],
[ 0.0100, 0.0100, 0.0500, 0.0100, 0.0100 ],
[ 0.0100, 0.0100, 0.0100, 0.0500, 0.0100 ],
[ 0.0100, 0.0100, 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000