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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] = 0.23326
f[CL] = 2.2423
f[V1] = 58.107
f[Q] = 0.69961
f[V2] = 114.93
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.16704, 0.02277, -0.041913, 0.085272, 0.0053418 ],
    [ 0.02277, 0.02969, 0.0082606, 0.010176, 0.0073914 ],
    [ -0.041913, 0.0082606, 0.031914, -0.022249, 0.0097421 ],
    [ 0.085272, 0.010176, -0.022249, 0.12799, -0.001646 ],
    [ 0.0053418, 0.0073914, 0.0097421, -0.001646, 0.052089 ]
]
f[PNOISE] = 0.14935

Plots

Dense comp plots

Alternatively see All dense_comp graph plots

Comparison

True objective value

-881.004127542

Final fitted objective value

-890.877751333

Compare Main f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA] 1 0.233 0.2 16.63% 3.33e-02
f[CL] 1 2.24 2 12.11% 2.42e-01
f[V1] 20 58.1 50 16.21% 8.11e+00
f[Q] 0.5 0.7 1 30.04% 3.00e-01
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 0.149 0.15 0.43% 6.52e-04

Compare Variance f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA_isv] 0.05 0.167 0.1 67.04% 6.70e-02
f[KA_isv;CL_isv] 0.0100000000093 0.0228 0.01 127.70% 1.28e-02
f[KA_isv;V1_isv] 0.0100000000093 -0.0419 0.01 519.13% 5.19e-02
f[KA_isv;Q_isv] 0.0100000000093 0.0853 0.01 752.72% 7.53e-02
f[KA_isv;V2_isv] 0.0100000000093 0.00534 0.01 46.58% 4.66e-03
f[CL_isv;KA_isv] 0.0100000000093 0.0228 0.01 127.70% 1.28e-02
f[CL_isv] 0.05 0.0297 0.03 1.03% 3.10e-04
f[CL_isv;V1_isv] 0.0100000000093 0.00826 -0.01 182.61% 1.83e-02
f[CL_isv;Q_isv] 0.0100000000093 0.0102 0.02 49.12% 9.82e-03
f[CL_isv;V2_isv] 0.0100000000093 0.00739 0.02 63.04% 1.26e-02
f[V1_isv;KA_isv] 0.0100000000093 -0.0419 0.01 519.13% 5.19e-02
f[V1_isv;CL_isv] 0.0100000000093 0.00826 -0.01 182.61% 1.83e-02
f[V1_isv] 0.05 0.0319 0.09 64.54% 5.81e-02
f[V1_isv;Q_isv] 0.0100000000093 -0.0222 0.01 322.49% 3.22e-02
f[V1_isv;V2_isv] 0.0100000000093 0.00974 0.01 2.58% 2.58e-04
f[Q_isv;KA_isv] 0.0100000000093 0.0853 0.01 752.72% 7.53e-02
f[Q_isv;CL_isv] 0.0100000000093 0.0102 0.02 49.12% 9.82e-03
f[Q_isv;V1_isv] 0.0100000000093 -0.0222 0.01 322.49% 3.22e-02
f[Q_isv] 0.05 0.128 0.07 82.84% 5.80e-02
f[Q_isv;V2_isv] 0.0100000000093 -0.00165 0.01 116.46% 1.16e-02
f[V2_isv;KA_isv] 0.0100000000093 0.00534 0.01 46.58% 4.66e-03
f[V2_isv;CL_isv] 0.0100000000093 0.00739 0.02 63.04% 1.26e-02
f[V2_isv;V1_isv] 0.0100000000093 0.00974 0.01 2.58% 2.58e-04
f[V2_isv;Q_isv] 0.0100000000093 -0.00165 0.01 116.46% 1.16e-02
f[V2_isv] 0.05 0.0521 0.05 4.18% 2.09e-03
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