- Language: en
- Documentation version: 1.3.1
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
-847.4635
Final fitted objective value
-863.1496
Compare Main f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[KA] |
1 |
0.215 |
0.2 |
1.48e-02 |
7.41% |
f[CL] |
1 |
1.79 |
2 |
2.10e-01 |
10.52% |
f[V1] |
20 |
55.5 |
50 |
5.46e+00 |
10.92% |
f[Q] |
0.5 |
1.06 |
1 |
5.64e-02 |
5.64% |
f[V2] |
100 |
487 |
80 |
4.07e+02 |
508.21% |
Compare Noise f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[PNOISE] |
0.1 |
0.142 |
0.15 |
8.50e-03 |
5.67% |
Compare Variance f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[KA_isv] |
0.05 |
0.139 |
0.1 |
3.93e-02 |
39.25% |
f[KA_isv;CL_isv] |
0.01 |
-0.0553 |
0.01 |
6.53e-02 |
653.38% |
f[KA_isv;V1_isv] |
0.01 |
0.0414 |
0.01 |
3.14e-02 |
314.14% |
f[KA_isv;Q_isv] |
0.01 |
-0.0256 |
0.01 |
3.56e-02 |
356.13% |
f[KA_isv;V2_isv] |
0.01 |
0.122 |
0.01 |
1.12e-01 |
1116.86% |
f[CL_isv;KA_isv] |
0.01 |
-0.0553 |
0.01 |
6.53e-02 |
653.38% |
f[CL_isv] |
0.05 |
0.0808 |
0.03 |
5.08e-02 |
169.21% |
f[CL_isv;V1_isv] |
0.01 |
-0.00702 |
-0.01 |
2.98e-03 |
29.83% |
f[CL_isv;Q_isv] |
0.01 |
-0.00606 |
0.02 |
2.61e-02 |
130.30% |
f[CL_isv;V2_isv] |
0.01 |
-0.178 |
0.02 |
1.98e-01 |
992.15% |
f[V1_isv;KA_isv] |
0.01 |
0.0414 |
0.01 |
3.14e-02 |
314.14% |
f[V1_isv;CL_isv] |
0.01 |
-0.00702 |
-0.01 |
2.98e-03 |
29.83% |
f[V1_isv] |
0.05 |
0.11 |
0.09 |
1.98e-02 |
21.97% |
f[V1_isv;Q_isv] |
0.01 |
-0.0827 |
0.01 |
9.27e-02 |
927.02% |
f[V1_isv;V2_isv] |
0.01 |
0.214 |
0.01 |
2.04e-01 |
2039.38% |
f[Q_isv;KA_isv] |
0.01 |
-0.0256 |
0.01 |
3.56e-02 |
356.13% |
f[Q_isv;CL_isv] |
0.01 |
-0.00606 |
0.02 |
2.61e-02 |
130.30% |
f[Q_isv;V1_isv] |
0.01 |
-0.0827 |
0.01 |
9.27e-02 |
927.02% |
f[Q_isv] |
0.05 |
0.317 |
0.07 |
2.47e-01 |
353.28% |
f[Q_isv;V2_isv] |
0.01 |
-0.324 |
0.01 |
3.34e-01 |
3339.14% |
f[V2_isv;KA_isv] |
0.01 |
0.122 |
0.01 |
1.12e-01 |
1116.86% |
f[V2_isv;CL_isv] |
0.01 |
-0.178 |
0.02 |
1.98e-01 |
992.15% |
f[V2_isv;V1_isv] |
0.01 |
0.214 |
0.01 |
2.04e-01 |
2039.38% |
f[V2_isv;Q_isv] |
0.01 |
-0.324 |
0.01 |
3.34e-01 |
3339.14% |
f[V2_isv] |
0.05 |
0.944 |
0.05 |
8.94e-01 |
1788.59% |
Outputs
Fitted f[X] values (after fitting)
f[KA] = 0.2148
f[CL] = 1.7896
f[V1] = 55.4580
f[Q] = 1.0564
f[V2] = 486.5705
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1393, -0.0553, 0.0414, -0.0256, 0.1217 ],
[ -0.0553, 0.0808, -0.0070, -0.0061, -0.1784 ],
[ 0.0414, -0.0070, 0.1098, -0.0827, 0.2139 ],
[ -0.0256, -0.0061, -0.0827, 0.3173, -0.3239 ],
[ 0.1217, -0.1784, 0.2139, -0.3239, 0.9443 ],
]
f[PNOISE] = 0.1415
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