- 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: |
Failed to create compartment diagram
True f[X] values¶
f[KA] = 0.2
f[CL] = 2
f[V1] = 50
f[Q] = 1
f[V2] = 80
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1, 0.01, 0.01, 0.01, 0.01 ],
[ 0.01, 0.03, -0.01, 0.02, 0.02 ],
[ 0.01, -0.01, 0.09, 0.01, 0.01 ],
[ 0.01, 0.02, 0.01, 0.07, 0.01 ],
[ 0.01, 0.02, 0.01, 0.01, 0.05 ]
]
f[PNOISE] = 0.15
Starting f[X] values¶
f[KA] = 1
f[CL] = 1
f[V1] = 20
f[Q] = 0.5
f[V2] = 100
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.05, 0.01, 0.01, 0.01, 0.01 ],
[ 0.01, 0.05, 0.01, 0.01, 0.01 ],
[ 0.01, 0.01, 0.05, 0.01, 0.01 ],
[ 0.01, 0.01, 0.01, 0.05, 0.01 ],
[ 0.01, 0.01, 0.01, 0.01, 0.05 ]
]
f[PNOISE] = 0.1
Outputs¶
Generating and Fitting Summaries¶
Fitted f[X] values¶
f[KA] = 0.23326
f[CL] = 2.2423
f[V1] = 58.107
f[Q] = 0.69961
f[V2] = 114.93
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.16704, 0.02277, -0.041913, 0.085272, 0.0053418 ],
[ 0.02277, 0.02969, 0.0082606, 0.010176, 0.0073914 ],
[ -0.041913, 0.0082606, 0.031914, -0.022249, 0.0097421 ],
[ 0.085272, 0.010176, -0.022249, 0.12799, -0.001646 ],
[ 0.0053418, 0.0073914, 0.0097421, -0.001646, 0.052089 ]
]
f[PNOISE] = 0.14935
Plots¶
Comparison¶
True objective value¶
-881.004127542
Final fitted objective value¶
-890.877751333
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.233 | 0.2 | 16.63% | 3.33e-02 |
f[CL] | 1 | 2.24 | 2 | 12.11% | 2.42e-01 |
f[V1] | 20 | 58.1 | 50 | 16.21% | 8.11e+00 |
f[Q] | 0.5 | 0.7 | 1 | 30.04% | 3.00e-01 |
f[V2] | 100 | 115 | 80 | 43.67% | 3.49e+01 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.149 | 0.15 | 0.43% | 6.52e-04 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.167 | 0.1 | 67.04% | 6.70e-02 |
f[KA_isv;CL_isv] | 0.0100000000093 | 0.0228 | 0.01 | 127.70% | 1.28e-02 |
f[KA_isv;V1_isv] | 0.0100000000093 | -0.0419 | 0.01 | 519.13% | 5.19e-02 |
f[KA_isv;Q_isv] | 0.0100000000093 | 0.0853 | 0.01 | 752.72% | 7.53e-02 |
f[KA_isv;V2_isv] | 0.0100000000093 | 0.00534 | 0.01 | 46.58% | 4.66e-03 |
f[CL_isv;KA_isv] | 0.0100000000093 | 0.0228 | 0.01 | 127.70% | 1.28e-02 |
f[CL_isv] | 0.05 | 0.0297 | 0.03 | 1.03% | 3.10e-04 |
f[CL_isv;V1_isv] | 0.0100000000093 | 0.00826 | -0.01 | 182.61% | 1.83e-02 |
f[CL_isv;Q_isv] | 0.0100000000093 | 0.0102 | 0.02 | 49.12% | 9.82e-03 |
f[CL_isv;V2_isv] | 0.0100000000093 | 0.00739 | 0.02 | 63.04% | 1.26e-02 |
f[V1_isv;KA_isv] | 0.0100000000093 | -0.0419 | 0.01 | 519.13% | 5.19e-02 |
f[V1_isv;CL_isv] | 0.0100000000093 | 0.00826 | -0.01 | 182.61% | 1.83e-02 |
f[V1_isv] | 0.05 | 0.0319 | 0.09 | 64.54% | 5.81e-02 |
f[V1_isv;Q_isv] | 0.0100000000093 | -0.0222 | 0.01 | 322.49% | 3.22e-02 |
f[V1_isv;V2_isv] | 0.0100000000093 | 0.00974 | 0.01 | 2.58% | 2.58e-04 |
f[Q_isv;KA_isv] | 0.0100000000093 | 0.0853 | 0.01 | 752.72% | 7.53e-02 |
f[Q_isv;CL_isv] | 0.0100000000093 | 0.0102 | 0.02 | 49.12% | 9.82e-03 |
f[Q_isv;V1_isv] | 0.0100000000093 | -0.0222 | 0.01 | 322.49% | 3.22e-02 |
f[Q_isv] | 0.05 | 0.128 | 0.07 | 82.84% | 5.80e-02 |
f[Q_isv;V2_isv] | 0.0100000000093 | -0.00165 | 0.01 | 116.46% | 1.16e-02 |
f[V2_isv;KA_isv] | 0.0100000000093 | 0.00534 | 0.01 | 46.58% | 4.66e-03 |
f[V2_isv;CL_isv] | 0.0100000000093 | 0.00739 | 0.02 | 63.04% | 1.26e-02 |
f[V2_isv;V1_isv] | 0.0100000000093 | 0.00974 | 0.01 | 2.58% | 2.58e-04 |
f[V2_isv;Q_isv] | 0.0100000000093 | -0.00165 | 0.01 | 116.46% | 1.16e-02 |
f[V2_isv] | 0.05 | 0.0521 | 0.05 | 4.18% | 2.09e-03 |