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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

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

Dense comp plots

Alternatively see All dense_comp graph 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
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