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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

-913.9529

which required N. iterations and took 1266.15 seconds

Final fitted fixed effects

f[KA] = 0.1846
f[CL] = 1.6740
f[V1] = 47.5042
f[Q] = 1.7764
f[V2] = 109.6770
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0965, 0.0418, -0.0532, -0.0200, -0.0172 ],
    [ 0.0418, 0.1443, 0.0079, -0.1026, -0.1679 ],
    [ -0.0532, 0.0079, 0.0369, -0.0123, -0.0301 ],
    [ -0.0200, -0.1026, -0.0123, 0.1982, 0.2090 ],
    [ -0.0172, -0.1679, -0.0301, 0.2090, 0.2667 ],
]
f[PNOISE] = 0.1337

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] 0.1846 1.0000 0.8154 0.8154
f[CL] 1.6740 1.0000 0.6740 0.6740
f[V1] 47.5042 20.0000 1.3752 27.5042
f[Q] 1.7764 0.5000 2.5527 1.2764
f[V2] 109.6770 100.0000 0.0968 9.6770

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.1337 0.1000 0.3372 0.0337

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.0965 0.0500 0.9301 0.0465
f[KA_isv;CL_isv] 0.0418 0.0100 3.1804 0.0318
f[KA_isv;V1_isv] -0.0532 0.0100 6.3232 0.0632
f[KA_isv;Q_isv] -0.0200 0.0100 3.0007 0.0300
f[KA_isv;V2_isv] -0.0172 0.0100 2.7245 0.0272
f[CL_isv;KA_isv] 0.0418 0.0100 3.1804 0.0318
f[CL_isv] 0.1443 0.0500 1.8854 0.0943
f[CL_isv;V1_isv] 0.0079 0.0100 0.2129 0.0021
f[CL_isv;Q_isv] -0.1026 0.0100 11.2632 0.1126
f[CL_isv;V2_isv] -0.1679 0.0100 17.7906 0.1779
f[V1_isv;KA_isv] -0.0532 0.0100 6.3232 0.0632
f[V1_isv;CL_isv] 0.0079 0.0100 0.2129 0.0021
f[V1_isv] 0.0369 0.0500 0.2610 0.0131
f[V1_isv;Q_isv] -0.0123 0.0100 2.2338 0.0223
f[V1_isv;V2_isv] -0.0301 0.0100 4.0071 0.0401
f[Q_isv;KA_isv] -0.0200 0.0100 3.0007 0.0300
f[Q_isv;CL_isv] -0.1026 0.0100 11.2632 0.1126
f[Q_isv;V1_isv] -0.0123 0.0100 2.2338 0.0223
f[Q_isv] 0.1982 0.0500 2.9646 0.1482
f[Q_isv;V2_isv] 0.2090 0.0100 19.9007 0.1990
f[V2_isv;KA_isv] -0.0172 0.0100 2.7245 0.0272
f[V2_isv;CL_isv] -0.1679 0.0100 17.7906 0.1779
f[V2_isv;V1_isv] -0.0301 0.0100 4.0071 0.0401
f[V2_isv;Q_isv] 0.2090 0.0100 19.9007 0.1990
f[V2_isv] 0.2667 0.0500 4.3341 0.2167
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