- 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¶
Generating and Fitting Summaries¶
Fitted f[X] values¶
f[KA] = 0.1836
f[CL] = 1.5613
f[V1] = 46.1503
f[Q] = 1.9135
f[V2] = 121.3791
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1126, 0.0235, -0.0555, -0.0183, 0.0169 ],
[ 0.0235, 0.1664, 0.0113, -0.1084, -0.1801 ],
[ -0.0555, 0.0113, 0.0330, -0.0080, -0.0346 ],
[ -0.0183, -0.1084, -0.0080, 0.1897, 0.1934 ],
[ 0.0169, -0.1801, -0.0346, 0.1934, 0.2644 ],
]
f[PNOISE] = 0.1327
Plots¶
Comparison¶
True objective value¶
-881.0670
Final fitted objective value¶
-913.0781
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.184 | 0.2 | 8.20% | 1.64e-02 |
f[CL] | 1 | 1.56 | 2 | 21.93% | 4.39e-01 |
f[V1] | 20 | 46.2 | 50 | 7.70% | 3.85e+00 |
f[Q] | 0.5 | 1.91 | 1 | 91.35% | 9.14e-01 |
f[V2] | 100 | 121 | 80 | 51.72% | 4.14e+01 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.133 | 0.15 | 11.55% | 1.73e-02 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.113 | 0.1 | 12.58% | 1.26e-02 |
f[KA_isv;CL_isv] | 0.01 | 0.0235 | 0.01 | 134.62% | 1.35e-02 |
f[KA_isv;V1_isv] | 0.01 | -0.0555 | 0.01 | 654.90% | 6.55e-02 |
f[KA_isv;Q_isv] | 0.01 | -0.0183 | 0.01 | 283.45% | 2.83e-02 |
f[KA_isv;V2_isv] | 0.01 | 0.0169 | 0.01 | 69.21% | 6.92e-03 |
f[CL_isv;KA_isv] | 0.01 | 0.0235 | 0.01 | 134.62% | 1.35e-02 |
f[CL_isv] | 0.05 | 0.166 | 0.03 | 454.76% | 1.36e-01 |
f[CL_isv;V1_isv] | 0.01 | 0.0113 | -0.01 | 212.59% | 2.13e-02 |
f[CL_isv;Q_isv] | 0.01 | -0.108 | 0.02 | 642.19% | 1.28e-01 |
f[CL_isv;V2_isv] | 0.01 | -0.18 | 0.02 | 1000.73% | 2.00e-01 |
f[V1_isv;KA_isv] | 0.01 | -0.0555 | 0.01 | 654.90% | 6.55e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.0113 | -0.01 | 212.59% | 2.13e-02 |
f[V1_isv] | 0.05 | 0.033 | 0.09 | 63.28% | 5.70e-02 |
f[V1_isv;Q_isv] | 0.01 | -0.00798 | 0.01 | 179.78% | 1.80e-02 |
f[V1_isv;V2_isv] | 0.01 | -0.0346 | 0.01 | 445.74% | 4.46e-02 |
f[Q_isv;KA_isv] | 0.01 | -0.0183 | 0.01 | 283.45% | 2.83e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.108 | 0.02 | 642.19% | 1.28e-01 |
f[Q_isv;V1_isv] | 0.01 | -0.00798 | 0.01 | 179.78% | 1.80e-02 |
f[Q_isv] | 0.05 | 0.19 | 0.07 | 171.05% | 1.20e-01 |
f[Q_isv;V2_isv] | 0.01 | 0.193 | 0.01 | 1833.62% | 1.83e-01 |
f[V2_isv;KA_isv] | 0.01 | 0.0169 | 0.01 | 69.21% | 6.92e-03 |
f[V2_isv;CL_isv] | 0.01 | -0.18 | 0.02 | 1000.73% | 2.00e-01 |
f[V2_isv;V1_isv] | 0.01 | -0.0346 | 0.01 | 445.74% | 4.46e-02 |
f[V2_isv;Q_isv] | 0.01 | 0.193 | 0.01 | 1833.62% | 1.83e-01 |
f[V2_isv] | 0.05 | 0.264 | 0.05 | 428.90% | 2.14e-01 |