- 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] = 0.2115
f[CL] = 2.0587
f[V1] = 53.0562
f[Q] = 0.9970
f[V2] = 104.3821
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1078, 0.0156, -0.0551, 0.0615, -0.0371 ],
[ 0.0156, 0.0658, 0.0054, -0.0648, -0.0879 ],
[ -0.0551, 0.0054, 0.0535, -0.0610, 0.0215 ],
[ 0.0615, -0.0648, -0.0610, 0.3495, 0.0985 ],
[ -0.0371, -0.0879, 0.0215, 0.0985, 0.1857 ]
]
f[PNOISE] = 0.1415
Plots¶
Comparison¶
True objective value¶
-881.0041
Final fitted objective value¶
-898.8908
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.212 | 0.2 | 5.76% | 1.15e-02 |
f[CL] | 1 | 2.06 | 2 | 2.94% | 5.87e-02 |
f[V1] | 20 | 53.1 | 50 | 6.11% | 3.06e+00 |
f[Q] | 0.5 | 0.997 | 1 | 0.30% | 2.95e-03 |
f[V2] | 100 | 104 | 80 | 30.48% | 2.44e+01 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.141 | 0.15 | 5.70% | 8.54e-03 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.108 | 0.1 | 7.81% | 7.81e-03 |
f[KA_isv;CL_isv] | 0.01 | 0.0156 | 0.01 | 55.61% | 5.56e-03 |
f[KA_isv;V1_isv] | 0.01 | -0.0551 | 0.01 | 651.13% | 6.51e-02 |
f[KA_isv;Q_isv] | 0.01 | 0.0615 | 0.01 | 515.16% | 5.15e-02 |
f[KA_isv;V2_isv] | 0.01 | -0.0371 | 0.01 | 471.17% | 4.71e-02 |
f[CL_isv;KA_isv] | 0.01 | 0.0156 | 0.01 | 55.61% | 5.56e-03 |
f[CL_isv] | 0.05 | 0.0658 | 0.03 | 119.22% | 3.58e-02 |
f[CL_isv;V1_isv] | 0.01 | 0.00539 | -0.01 | 153.88% | 1.54e-02 |
f[CL_isv;Q_isv] | 0.01 | -0.0648 | 0.02 | 424.17% | 8.48e-02 |
f[CL_isv;V2_isv] | 0.01 | -0.0879 | 0.02 | 539.42% | 1.08e-01 |
f[V1_isv;KA_isv] | 0.01 | -0.0551 | 0.01 | 651.13% | 6.51e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.00539 | -0.01 | 153.88% | 1.54e-02 |
f[V1_isv] | 0.05 | 0.0535 | 0.09 | 40.51% | 3.65e-02 |
f[V1_isv;Q_isv] | 0.01 | -0.061 | 0.01 | 710.03% | 7.10e-02 |
f[V1_isv;V2_isv] | 0.01 | 0.0215 | 0.01 | 115.32% | 1.15e-02 |
f[Q_isv;KA_isv] | 0.01 | 0.0615 | 0.01 | 515.16% | 5.15e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.0648 | 0.02 | 424.17% | 8.48e-02 |
f[Q_isv;V1_isv] | 0.01 | -0.061 | 0.01 | 710.03% | 7.10e-02 |
f[Q_isv] | 0.05 | 0.349 | 0.07 | 399.22% | 2.79e-01 |
f[Q_isv;V2_isv] | 0.01 | 0.0985 | 0.01 | 884.73% | 8.85e-02 |
f[V2_isv;KA_isv] | 0.01 | -0.0371 | 0.01 | 471.17% | 4.71e-02 |
f[V2_isv;CL_isv] | 0.01 | -0.0879 | 0.02 | 539.42% | 1.08e-01 |
f[V2_isv;V1_isv] | 0.01 | 0.0215 | 0.01 | 115.32% | 1.15e-02 |
f[V2_isv;Q_isv] | 0.01 | 0.0985 | 0.01 | 884.73% | 8.85e-02 |
f[V2_isv] | 0.05 | 0.186 | 0.05 | 271.41% | 1.36e-01 |