- 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] = 0.1713
f[CL] = 1.8060
f[V1] = 43.8081
f[Q] = 1.8123
f[V2] = 85.0498
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1113, 0.0309, -0.0452, -0.0276, 0.0024 ],
[ 0.0309, 0.1342, 0.0223, -0.1133, -0.2044 ],
[ -0.0452, 0.0223, 0.0280, -0.0182, -0.0579 ],
[ -0.0276, -0.1133, -0.0182, 0.2058, 0.2750 ],
[ 0.0024, -0.2044, -0.0579, 0.2750, 0.4314 ],
]
f[PNOISE] = 0.1339
Plots¶
Comparison¶
True objective value¶
-881.0670
Final fitted objective value¶
-913.5629
Compare Main f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA] | 1 | 0.171 | 0.2 | 14.34% | 2.87e-02 |
f[CL] | 1 | 1.81 | 2 | 9.70% | 1.94e-01 |
f[V1] | 20 | 43.8 | 50 | 12.38% | 6.19e+00 |
f[Q] | 0.5 | 1.81 | 1 | 81.23% | 8.12e-01 |
f[V2] | 100 | 85 | 80 | 6.31% | 5.05e+00 |
Compare Noise f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[PNOISE] | 0.1 | 0.134 | 0.15 | 10.71% | 1.61e-02 |
Compare Variance f[X]¶
Name | Initial | Fitted | True | Prop. Error | Abs. Error |
---|---|---|---|---|---|
f[KA_isv] | 0.05 | 0.111 | 0.1 | 11.27% | 1.13e-02 |
f[KA_isv;CL_isv] | 0.01 | 0.0309 | 0.01 | 208.70% | 2.09e-02 |
f[KA_isv;V1_isv] | 0.01 | -0.0452 | 0.01 | 551.55% | 5.52e-02 |
f[KA_isv;Q_isv] | 0.01 | -0.0276 | 0.01 | 375.59% | 3.76e-02 |
f[KA_isv;V2_isv] | 0.01 | 0.00244 | 0.01 | 75.60% | 7.56e-03 |
f[CL_isv;KA_isv] | 0.01 | 0.0309 | 0.01 | 208.70% | 2.09e-02 |
f[CL_isv] | 0.05 | 0.134 | 0.03 | 347.48% | 1.04e-01 |
f[CL_isv;V1_isv] | 0.01 | 0.0223 | -0.01 | 323.46% | 3.23e-02 |
f[CL_isv;Q_isv] | 0.01 | -0.113 | 0.02 | 666.75% | 1.33e-01 |
f[CL_isv;V2_isv] | 0.01 | -0.204 | 0.02 | 1122.14% | 2.24e-01 |
f[V1_isv;KA_isv] | 0.01 | -0.0452 | 0.01 | 551.55% | 5.52e-02 |
f[V1_isv;CL_isv] | 0.01 | 0.0223 | -0.01 | 323.46% | 3.23e-02 |
f[V1_isv] | 0.05 | 0.028 | 0.09 | 68.88% | 6.20e-02 |
f[V1_isv;Q_isv] | 0.01 | -0.0182 | 0.01 | 281.78% | 2.82e-02 |
f[V1_isv;V2_isv] | 0.01 | -0.0579 | 0.01 | 679.42% | 6.79e-02 |
f[Q_isv;KA_isv] | 0.01 | -0.0276 | 0.01 | 375.59% | 3.76e-02 |
f[Q_isv;CL_isv] | 0.01 | -0.113 | 0.02 | 666.75% | 1.33e-01 |
f[Q_isv;V1_isv] | 0.01 | -0.0182 | 0.01 | 281.78% | 2.82e-02 |
f[Q_isv] | 0.05 | 0.206 | 0.07 | 194.06% | 1.36e-01 |
f[Q_isv;V2_isv] | 0.01 | 0.275 | 0.01 | 2650.26% | 2.65e-01 |
f[V2_isv;KA_isv] | 0.01 | 0.00244 | 0.01 | 75.60% | 7.56e-03 |
f[V2_isv;CL_isv] | 0.01 | -0.204 | 0.02 | 1122.14% | 2.24e-01 |
f[V2_isv;V1_isv] | 0.01 | -0.0579 | 0.01 | 679.42% | 6.79e-02 |
f[V2_isv;Q_isv] | 0.01 | 0.275 | 0.01 | 2650.26% | 2.65e-01 |
f[V2_isv] | 0.05 | 0.431 | 0.05 | 762.75% | 3.81e-01 |