- 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] = 0.2000
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] = 0.1207
f[CL] = 1.5708
f[V1] = 33.8896
f[Q] = 2.2287
f[V2] = 114.9338
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0630, 0.0208, 0.0700, -0.0019, 0.0091 ],
[ 0.0208, 0.1208, 0.1138, -0.0618, -0.2448 ],
[ 0.0700, 0.1138, 0.1965, -0.0260, -0.1460 ],
[ -0.0019, -0.0618, -0.0260, 0.0574, 0.1992 ],
[ 0.0091, -0.2448, -0.1460, 0.1992, 0.7660 ],
]
f[PNOISE] = 0.1478
Plots¶
Comparison¶
True objective value¶
-873.4691
Final fitted objective value¶
-887.8461
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.121 | 0.2 | 39.65% | 7.93e-02 |
f[CL] | 1 | 1.57 | 2 | 21.46% | 4.29e-01 |
f[V1] | 20 | 33.9 | 50 | 32.22% | 1.61e+01 |
f[Q] | 0.5 | 2.23 | 1 | 122.87% | 1.23e+00 |
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 | 0.148 | 0.15 | 1.44% | 2.16e-03 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.063 | 0.1 | 36.98% | 3.70e-02 |
f[KA_isv;CL_isv] | 0.01 | 0.0208 | 0.01 | 107.63% | 1.08e-02 |
f[KA_isv;V1_isv] | 0.01 | 0.07 | 0.01 | 600.41% | 6.00e-02 |
f[KA_isv;Q_isv] | 0.01 | -0.00193 | 0.01 | 119.30% | 1.19e-02 |
f[KA_isv;V2_isv] | 0.01 | 0.00914 | 0.01 | 8.59% | 8.59e-04 |
f[CL_isv;KA_isv] | 0.01 | 0.0208 | 0.01 | 107.63% | 1.08e-02 |
f[CL_isv] | 0.05 | 0.121 | 0.03 | 302.60% | 9.08e-02 |
f[CL_isv;V1_isv] | 0.01 | 0.114 | -0.01 | 1238.27% | 1.24e-01 |
f[CL_isv;Q_isv] | 0.01 | -0.0618 | 0.02 | 409.00% | 8.18e-02 |
f[CL_isv;V2_isv] | 0.01 | -0.245 | 0.02 | 1324.02% | 2.65e-01 |
f[V1_isv;KA_isv] | 0.01 | 0.07 | 0.01 | 600.41% | 6.00e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.114 | -0.01 | 1238.27% | 1.24e-01 |
f[V1_isv] | 0.05 | 0.197 | 0.09 | 118.38% | 1.07e-01 |
f[V1_isv;Q_isv] | 0.01 | -0.026 | 0.01 | 360.05% | 3.60e-02 |
f[V1_isv;V2_isv] | 0.01 | -0.146 | 0.01 | 1559.55% | 1.56e-01 |
f[Q_isv;KA_isv] | 0.01 | -0.00193 | 0.01 | 119.30% | 1.19e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.0618 | 0.02 | 409.00% | 8.18e-02 |
f[Q_isv;V1_isv] | 0.01 | -0.026 | 0.01 | 360.05% | 3.60e-02 |
f[Q_isv] | 0.05 | 0.0574 | 0.07 | 18.01% | 1.26e-02 |
f[Q_isv;V2_isv] | 0.01 | 0.199 | 0.01 | 1892.17% | 1.89e-01 |
f[V2_isv;KA_isv] | 0.01 | 0.00914 | 0.01 | 8.59% | 8.59e-04 |
f[V2_isv;CL_isv] | 0.01 | -0.245 | 0.02 | 1324.02% | 2.65e-01 |
f[V2_isv;V1_isv] | 0.01 | -0.146 | 0.01 | 1559.55% | 1.56e-01 |
f[V2_isv;Q_isv] | 0.01 | 0.199 | 0.01 | 1892.17% | 1.89e-01 |
f[V2_isv] | 0.05 | 0.766 | 0.05 | 1431.95% | 7.16e-01 |