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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Inputs

Description

Name:builtin_fit_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:fitting; pk; advan4; dep_two_cmp; first order
Input Script:builtin_fit_example.pyml
Input Data:builtin_fit_example_data.csv
Diagram:

Initial fixed effect estimates

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

Final objective value

-898.8908

which required 33 iterations and took 90.04 seconds

Final fitted fixed effects

f[KA] = 0.2115
f[CL] = 2.0587
f[V1] = 53.0562
f[Q] = 0.9970
f[V2] = 104.3821
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.1078, 0.0156, -0.0551, 0.0615, -0.0371 ],
    [ 0.0156, 0.0658, 0.0054, -0.0648, -0.0879 ],
    [ -0.0551, 0.0054, 0.0535, -0.0610, 0.0215 ],
    [ 0.0615, -0.0648, -0.0610, 0.3495, 0.0985 ],
    [ -0.0371, -0.0879, 0.0215, 0.0985, 0.1857 ]
]
f[PNOISE] = 0.1415

Fitted parameter .csv files

Fixed Effects:fx_params.csv (fit)
Random Effects:rx_params.csv (fit)
Model params:mx_params.csv (fit)
State values:sx_params.csv (fit)
Predictions:px_params.csv (fit)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA] 0.2115 1.0000 0.7885 0.7885
f[CL] 2.0587 1.0000 1.0587 1.0587
f[V1] 53.0562 20.0000 1.6528 33.0562
f[Q] 0.9970 0.5000 0.9941 0.4970
f[V2] 104.3821 100.0000 0.0438 4.3821

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.1415 0.1000 0.4146 0.0415

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.1078 0.0500 1.1563 0.0578
f[KA_isv;CL_isv] 0.0156 0.0100 0.5561 0.0056
f[KA_isv;V1_isv] -0.0551 0.0100 6.5113 0.0651
f[KA_isv;Q_isv] 0.0615 0.0100 5.1516 0.0515
f[KA_isv;V2_isv] -0.0371 0.0100 4.7117 0.0471
f[CL_isv;KA_isv] 0.0156 0.0100 0.5561 0.0056
f[CL_isv] 0.0658 0.0500 0.3153 0.0158
f[CL_isv;V1_isv] 0.0054 0.0100 0.4612 0.0046
f[CL_isv;Q_isv] -0.0648 0.0100 7.4834 0.0748
f[CL_isv;V2_isv] -0.0879 0.0100 9.7885 0.0979
f[V1_isv;KA_isv] -0.0551 0.0100 6.5113 0.0651
f[V1_isv;CL_isv] 0.0054 0.0100 0.4612 0.0046
f[V1_isv] 0.0535 0.0500 0.0708 0.0035
f[V1_isv;Q_isv] -0.0610 0.0100 7.1003 0.0710
f[V1_isv;V2_isv] 0.0215 0.0100 1.1532 0.0115
f[Q_isv;KA_isv] 0.0615 0.0100 5.1516 0.0515
f[Q_isv;CL_isv] -0.0648 0.0100 7.4834 0.0748
f[Q_isv;V1_isv] -0.0610 0.0100 7.1003 0.0710
f[Q_isv] 0.3495 0.0500 5.9891 0.2995
f[Q_isv;V2_isv] 0.0985 0.0100 8.8473 0.0885
f[V2_isv;KA_isv] -0.0371 0.0100 4.7117 0.0471
f[V2_isv;CL_isv] -0.0879 0.0100 9.7885 0.0979
f[V2_isv;V1_isv] 0.0215 0.0100 1.1532 0.0115
f[V2_isv;Q_isv] 0.0985 0.0100 8.8473 0.0885
f[V2_isv] 0.1857 0.0500 2.7141 0.1357
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