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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:

Failed to create compartment diagram

True f[X] values

f[KA] = 0.2
f[CL] = 2
f[V1] = 50
f[Q] = 1
f[V2] = 80
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.1, 0.01, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.03, -0.01, 0.02, 0.02 ],
    [ 0.01, -0.01, 0.09, 0.01, 0.01 ],
    [ 0.01, 0.02, 0.01, 0.07, 0.01 ],
    [ 0.01, 0.02, 0.01, 0.01, 0.05 ]
]
f[PNOISE] = 0.15

Starting f[X] values

f[KA] = 1
f[CL] = 1
f[V1] = 20
f[Q] = 0.5
f[V2] = 100
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.05, 0.01, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.05, 0.01, 0.01, 0.01 ],
    [ 0.01, 0.01, 0.05, 0.01, 0.01 ],
    [ 0.01, 0.01, 0.01, 0.05, 0.01 ],
    [ 0.01, 0.01, 0.01, 0.01, 0.05 ]
]
f[PNOISE] = 0.1

Outputs

Fitted f[X] values

f[KA] = 0.22502
f[CL] = 2.0883
f[V1] = 54.663
f[Q] = 0.94563
f[V2] = 105.35
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.14689, 0.013803, -0.056237, 0.101, -0.011802 ],
    [ 0.013803, 0.033066, 0.0062645, -0.0050783, 0.00014454 ],
    [ -0.056237, 0.0062645, 0.043295, -0.047294, 0.014371 ],
    [ 0.101, -0.0050783, -0.047294, 0.23317, -0.033465 ],
    [ -0.011802, 0.00014454, 0.014371, -0.033465, 0.05129 ]
]
f[PNOISE] = 0.14293

Plots

Dense comp plots

Alternatively see All dense_comp graph plots

Comparison

True objective value

-881.002739381

Final fitted objective value

-896.875222682

Compare Main f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA] 1 0.225 0.2 12.51% 2.50e-02
f[CL] 1 2.09 2 4.41% 8.83e-02
f[V1] 20 54.7 50 9.33% 4.66e+00
f[Q] 0.5 0.946 1 5.44% 5.44e-02
f[V2] 100 105 80 31.69% 2.54e+01

Compare Noise f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[PNOISE] 0.1 0.143 0.15 4.71% 7.07e-03

Compare Variance f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA_isv] 0.05 0.147 0.1 46.89% 4.69e-02
f[KA_isv;CL_isv] 0.01 0.0138 0.01 38.03% 3.80e-03
f[KA_isv;V1_isv] 0.01 -0.0562 0.01 662.37% 6.62e-02
f[KA_isv;Q_isv] 0.01 0.101 0.01 909.96% 9.10e-02
f[KA_isv;V2_isv] 0.01 -0.0118 0.01 218.02% 2.18e-02
f[CL_isv;KA_isv] 0.01 0.0138 0.01 38.03% 3.80e-03
f[CL_isv] 0.05 0.0331 0.03 10.22% 3.07e-03
f[CL_isv;V1_isv] 0.01 0.00626 -0.01 162.65% 1.63e-02
f[CL_isv;Q_isv] 0.01 -0.00508 0.02 125.39% 2.51e-02
f[CL_isv;V2_isv] 0.01 0.000145 0.02 99.28% 1.99e-02
f[V1_isv;KA_isv] 0.01 -0.0562 0.01 662.37% 6.62e-02
f[V1_isv;CL_isv] 0.01 0.00626 -0.01 162.65% 1.63e-02
f[V1_isv] 0.05 0.0433 0.09 51.89% 4.67e-02
f[V1_isv;Q_isv] 0.01 -0.0473 0.01 572.94% 5.73e-02
f[V1_isv;V2_isv] 0.01 0.0144 0.01 43.71% 4.37e-03
f[Q_isv;KA_isv] 0.01 0.101 0.01 909.96% 9.10e-02
f[Q_isv;CL_isv] 0.01 -0.00508 0.02 125.39% 2.51e-02
f[Q_isv;V1_isv] 0.01 -0.0473 0.01 572.94% 5.73e-02
f[Q_isv] 0.05 0.233 0.07 233.11% 1.63e-01
f[Q_isv;V2_isv] 0.01 -0.0335 0.01 434.65% 4.35e-02
f[V2_isv;KA_isv] 0.01 -0.0118 0.01 218.02% 2.18e-02
f[V2_isv;CL_isv] 0.01 0.000145 0.02 99.28% 1.99e-02
f[V2_isv;V1_isv] 0.01 0.0144 0.01 43.71% 4.37e-03
f[V2_isv;Q_isv] 0.01 -0.0335 0.01 434.65% 4.35e-02
f[V2_isv] 0.05 0.0513 0.05 2.58% 1.29e-03
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