- 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 |
---|---|
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¶
Generated data .csv file¶
Synthetic Data: | synthetic_data.csv |
---|
Generating and Fitting Summaries¶
Fitted f[X] values¶
f[KA] = 0.1209
f[CL] = 1.5555
f[V1] = 33.9425
f[Q] = 2.2223
f[V2] = 119.5563
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0564, 0.0202, 0.0672, -0.0035, 0.0082 ],
[ 0.0202, 0.1257, 0.1163, -0.0664, -0.2501 ],
[ 0.0672, 0.1163, 0.2004, -0.0317, -0.1448 ],
[ -0.0035, -0.0664, -0.0317, 0.0584, 0.2049 ],
[ 0.0082, -0.2501, -0.1448, 0.2049, 0.7798 ],
]
f[PNOISE] = 0.1480
Plots¶
Comparison¶
True objective value¶
-873.4691
Final fitted objective value¶
-887.8029
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.121 | 0.2 | 39.56% | 7.91e-02 |
f[CL] | 1 | 1.56 | 2 | 22.22% | 4.44e-01 |
f[V1] | 20 | 33.9 | 50 | 32.12% | 1.61e+01 |
f[Q] | 0.5 | 2.22 | 1 | 122.23% | 1.22e+00 |
f[V2] | 100 | 120 | 80 | 49.45% | 3.96e+01 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.148 | 0.15 | 1.37% | 2.05e-03 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.0564 | 0.1 | 43.63% | 4.36e-02 |
f[KA_isv;CL_isv] | 0.01 | 0.0202 | 0.01 | 102.06% | 1.02e-02 |
f[KA_isv;V1_isv] | 0.01 | 0.0672 | 0.01 | 572.04% | 5.72e-02 |
f[KA_isv;Q_isv] | 0.01 | -0.00347 | 0.01 | 134.71% | 1.35e-02 |
f[KA_isv;V2_isv] | 0.01 | 0.00815 | 0.01 | 18.48% | 1.85e-03 |
f[CL_isv;KA_isv] | 0.01 | 0.0202 | 0.01 | 102.06% | 1.02e-02 |
f[CL_isv] | 0.05 | 0.126 | 0.03 | 319.06% | 9.57e-02 |
f[CL_isv;V1_isv] | 0.01 | 0.116 | -0.01 | 1262.61% | 1.26e-01 |
f[CL_isv;Q_isv] | 0.01 | -0.0664 | 0.02 | 431.93% | 8.64e-02 |
f[CL_isv;V2_isv] | 0.01 | -0.25 | 0.02 | 1350.57% | 2.70e-01 |
f[V1_isv;KA_isv] | 0.01 | 0.0672 | 0.01 | 572.04% | 5.72e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.116 | -0.01 | 1262.61% | 1.26e-01 |
f[V1_isv] | 0.05 | 0.2 | 0.09 | 122.68% | 1.10e-01 |
f[V1_isv;Q_isv] | 0.01 | -0.0317 | 0.01 | 417.25% | 4.17e-02 |
f[V1_isv;V2_isv] | 0.01 | -0.145 | 0.01 | 1547.50% | 1.55e-01 |
f[Q_isv;KA_isv] | 0.01 | -0.00347 | 0.01 | 134.71% | 1.35e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.0664 | 0.02 | 431.93% | 8.64e-02 |
f[Q_isv;V1_isv] | 0.01 | -0.0317 | 0.01 | 417.25% | 4.17e-02 |
f[Q_isv] | 0.05 | 0.0584 | 0.07 | 16.53% | 1.16e-02 |
f[Q_isv;V2_isv] | 0.01 | 0.205 | 0.01 | 1949.01% | 1.95e-01 |
f[V2_isv;KA_isv] | 0.01 | 0.00815 | 0.01 | 18.48% | 1.85e-03 |
f[V2_isv;CL_isv] | 0.01 | -0.25 | 0.02 | 1350.57% | 2.70e-01 |
f[V2_isv;V1_isv] | 0.01 | -0.145 | 0.01 | 1547.50% | 1.55e-01 |
f[V2_isv;Q_isv] | 0.01 | 0.205 | 0.01 | 1949.01% | 1.95e-01 |
f[V2_isv] | 0.05 | 0.78 | 0.05 | 1459.52% | 7.30e-01 |