- Language: en
First order absorption model with peripheral compartment¶
[Generated automatically as a Tutorial summary]
Inputs¶
Description¶
Name: | builtin_tut_example |
---|---|
Title: | First order absorption model with peripheral compartment |
Author: | J.R. Hartley |
Abstract: |
A two compartment PK model with bolus dose and
first order absorption, similar to a Nonmem advan4trans4 model.
Keywords: | tutorial; pk; advan4; dep_two_cmp; first order |
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Input Script: | builtin_tut_example.pyml |
Diagram: |
True f[X] values¶
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¶
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
Outputs¶
Generating and Fitting Summaries¶
Fitted f[X] values¶
f[KA] = 0.1019
f[CL] = 2.1528
f[V1] = 24.1372
f[Q] = 1.9547
f[V2] = 61.7683
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0322, 0.0145, 0.0383, -0.0011, -0.0925 ],
[ 0.0145, 0.0165, 0.0431, -0.0013, -0.0482 ],
[ 0.0383, 0.0431, 0.3030, 0.0110, -0.3540 ],
[ -0.0011, -0.0013, 0.0110, 0.0040, 0.0023 ],
[ -0.0925, -0.0482, -0.3540, 0.0023, 0.7273 ],
]
f[PNOISE] = 0.1399
Plots¶
Comparison¶
True objective value¶
-881.0061
Final fitted objective value¶
-912.2423
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.102 | 0.2 | 49.03% | 9.81e-02 |
f[CL] | 1 | 2.15 | 2 | 7.64% | 1.53e-01 |
f[V1] | 20 | 24.1 | 50 | 51.73% | 2.59e+01 |
f[Q] | 0.5 | 1.95 | 1 | 95.47% | 9.55e-01 |
f[V2] | 100 | 61.8 | 80 | 22.79% | 1.82e+01 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.14 | 0.15 | 6.75% | 1.01e-02 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.0322 | 0.1 | 67.82% | 6.78e-02 |
f[KA_isv;CL_isv] | 0.01 | 0.0145 | 0.01 | 44.70% | 4.47e-03 |
f[KA_isv;V1_isv] | 0.01 | 0.0383 | 0.01 | 282.65% | 2.83e-02 |
f[KA_isv;Q_isv] | 0.01 | -0.00113 | 0.01 | 111.27% | 1.11e-02 |
f[KA_isv;V2_isv] | 0.01 | -0.0925 | 0.01 | 1024.60% | 1.02e-01 |
f[CL_isv;KA_isv] | 0.01 | 0.0145 | 0.01 | 44.70% | 4.47e-03 |
f[CL_isv] | 0.05 | 0.0165 | 0.03 | 45.11% | 1.35e-02 |
f[CL_isv;V1_isv] | 0.01 | 0.0431 | -0.01 | 531.18% | 5.31e-02 |
f[CL_isv;Q_isv] | 0.01 | -0.00129 | 0.02 | 106.45% | 2.13e-02 |
f[CL_isv;V2_isv] | 0.01 | -0.0482 | 0.02 | 341.00% | 6.82e-02 |
f[V1_isv;KA_isv] | 0.01 | 0.0383 | 0.01 | 282.65% | 2.83e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.0431 | -0.01 | 531.18% | 5.31e-02 |
f[V1_isv] | 0.05 | 0.303 | 0.09 | 236.64% | 2.13e-01 |
f[V1_isv;Q_isv] | 0.01 | 0.011 | 0.01 | 9.74% | 9.74e-04 |
f[V1_isv;V2_isv] | 0.01 | -0.354 | 0.01 | 3639.98% | 3.64e-01 |
f[Q_isv;KA_isv] | 0.01 | -0.00113 | 0.01 | 111.27% | 1.11e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.00129 | 0.02 | 106.45% | 2.13e-02 |
f[Q_isv;V1_isv] | 0.01 | 0.011 | 0.01 | 9.74% | 9.74e-04 |
f[Q_isv] | 0.05 | 0.00401 | 0.07 | 94.27% | 6.60e-02 |
f[Q_isv;V2_isv] | 0.01 | 0.00233 | 0.01 | 76.75% | 7.67e-03 |
f[V2_isv;KA_isv] | 0.01 | -0.0925 | 0.01 | 1024.60% | 1.02e-01 |
f[V2_isv;CL_isv] | 0.01 | -0.0482 | 0.02 | 341.00% | 6.82e-02 |
f[V2_isv;V1_isv] | 0.01 | -0.354 | 0.01 | 3639.98% | 3.64e-01 |
f[V2_isv;Q_isv] | 0.01 | 0.00233 | 0.01 | 76.75% | 7.67e-03 |
f[V2_isv] | 0.05 | 0.727 | 0.05 | 1354.52% | 6.77e-01 |