- 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] = 0.22502
f[CL] = 2.0883
f[V1] = 54.663
f[Q] = 0.94563
f[V2] = 105.35
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.14689, 0.013803, -0.056237, 0.101, -0.011802 ],
[ 0.013803, 0.033066, 0.0062645, -0.0050783, 0.00014454 ],
[ -0.056237, 0.0062645, 0.043295, -0.047294, 0.014371 ],
[ 0.101, -0.0050783, -0.047294, 0.23317, -0.033465 ],
[ -0.011802, 0.00014454, 0.014371, -0.033465, 0.05129 ]
]
f[PNOISE] = 0.14293
Plots¶
Comparison¶
True objective value¶
-881.002739381
Final fitted objective value¶
-896.875222682
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.225 | 0.2 | 12.51% | 2.50e-02 |
f[CL] | 1 | 2.09 | 2 | 4.41% | 8.83e-02 |
f[V1] | 20 | 54.7 | 50 | 9.33% | 4.66e+00 |
f[Q] | 0.5 | 0.946 | 1 | 5.44% | 5.44e-02 |
f[V2] | 100 | 105 | 80 | 31.69% | 2.54e+01 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.143 | 0.15 | 4.71% | 7.07e-03 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.147 | 0.1 | 46.89% | 4.69e-02 |
f[KA_isv;CL_isv] | 0.01 | 0.0138 | 0.01 | 38.03% | 3.80e-03 |
f[KA_isv;V1_isv] | 0.01 | -0.0562 | 0.01 | 662.37% | 6.62e-02 |
f[KA_isv;Q_isv] | 0.01 | 0.101 | 0.01 | 909.96% | 9.10e-02 |
f[KA_isv;V2_isv] | 0.01 | -0.0118 | 0.01 | 218.02% | 2.18e-02 |
f[CL_isv;KA_isv] | 0.01 | 0.0138 | 0.01 | 38.03% | 3.80e-03 |
f[CL_isv] | 0.05 | 0.0331 | 0.03 | 10.22% | 3.07e-03 |
f[CL_isv;V1_isv] | 0.01 | 0.00626 | -0.01 | 162.65% | 1.63e-02 |
f[CL_isv;Q_isv] | 0.01 | -0.00508 | 0.02 | 125.39% | 2.51e-02 |
f[CL_isv;V2_isv] | 0.01 | 0.000145 | 0.02 | 99.28% | 1.99e-02 |
f[V1_isv;KA_isv] | 0.01 | -0.0562 | 0.01 | 662.37% | 6.62e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.00626 | -0.01 | 162.65% | 1.63e-02 |
f[V1_isv] | 0.05 | 0.0433 | 0.09 | 51.89% | 4.67e-02 |
f[V1_isv;Q_isv] | 0.01 | -0.0473 | 0.01 | 572.94% | 5.73e-02 |
f[V1_isv;V2_isv] | 0.01 | 0.0144 | 0.01 | 43.71% | 4.37e-03 |
f[Q_isv;KA_isv] | 0.01 | 0.101 | 0.01 | 909.96% | 9.10e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.00508 | 0.02 | 125.39% | 2.51e-02 |
f[Q_isv;V1_isv] | 0.01 | -0.0473 | 0.01 | 572.94% | 5.73e-02 |
f[Q_isv] | 0.05 | 0.233 | 0.07 | 233.11% | 1.63e-01 |
f[Q_isv;V2_isv] | 0.01 | -0.0335 | 0.01 | 434.65% | 4.35e-02 |
f[V2_isv;KA_isv] | 0.01 | -0.0118 | 0.01 | 218.02% | 2.18e-02 |
f[V2_isv;CL_isv] | 0.01 | 0.000145 | 0.02 | 99.28% | 1.99e-02 |
f[V2_isv;V1_isv] | 0.01 | 0.0144 | 0.01 | 43.71% | 4.37e-03 |
f[V2_isv;Q_isv] | 0.01 | -0.0335 | 0.01 | 434.65% | 4.35e-02 |
f[V2_isv] | 0.05 | 0.0513 | 0.05 | 2.58% | 1.29e-03 |