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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Model Description

Name:builtin_tut_example
Title:First order absorption model with peripheral compartment
Author:PoPy for PK/PD
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_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 1.0000 0.1105 0.8895 0.8895
f[CL] 1.0000 2.2942 1.2942 1.2942
f[V1] 20.0000 31.0117 11.0117 0.5506
f[Q] 0.5000 1.6599 1.1599 2.3199
f[V2] 100.0000 44.8060 55.1940 0.5519

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.1411 0.0411 0.4111

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 0.0847 0.0347 0.6949
f[KA_isv;CL_isv] 0.0100 0.0348 0.0248 2.4798
f[KA_isv;V1_isv] 0.0100 -0.0029 0.0129 1.2948
f[KA_isv;Q_isv] 0.0100 -0.0471 0.0571 5.7063
f[KA_isv;V2_isv] 0.0100 -0.0914 0.1014 10.1360
f[CL_isv;KA_isv] 0.0100 0.0348 0.0248 2.4798
f[CL_isv] 0.0500 0.0160 0.0340 0.6808
f[CL_isv;V1_isv] 0.0100 0.0065 0.0035 0.3463
f[CL_isv;Q_isv] 0.0100 -0.0282 0.0382 3.8244
f[CL_isv;V2_isv] 0.0100 -0.0181 0.0281 2.8052
f[V1_isv;KA_isv] 0.0100 -0.0029 0.0129 1.2948
f[V1_isv;CL_isv] 0.0100 0.0065 0.0035 0.3463
f[V1_isv] 0.0500 0.1368 0.0868 1.7354
f[V1_isv;Q_isv] 0.0100 -0.0275 0.0375 3.7549
f[V1_isv;V2_isv] 0.0100 0.1315 0.1215 12.1466
f[Q_isv;KA_isv] 0.0100 -0.0471 0.0571 5.7063
f[Q_isv;CL_isv] 0.0100 -0.0282 0.0382 3.8244
f[Q_isv;V1_isv] 0.0100 -0.0275 0.0375 3.7549
f[Q_isv] 0.0500 0.0830 0.0330 0.6602
f[Q_isv;V2_isv] 0.0100 -0.0602 0.0702 7.0164
f[V2_isv;KA_isv] 0.0100 -0.0914 0.1014 10.1360
f[V2_isv;CL_isv] 0.0100 -0.0181 0.0281 2.8052
f[V2_isv;V1_isv] 0.0100 0.1315 0.1215 12.1466
f[V2_isv;Q_isv] 0.0100 -0.0602 0.0702 7.0164
f[V2_isv] 0.0500 0.3610 0.3110 6.2208

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-907.0764

which required 1.30 iterations and took 459.52 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1105
f[CL] = 2.2942
f[V1] = 31.0117
f[Q] = 1.6599
f[V2] = 44.8060
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0847, 0.0348, -0.0029, -0.0471, -0.0914 ],
    [ 0.0348, 0.0160, 0.0065, -0.0282, -0.0181 ],
    [ -0.0029, 0.0065, 0.1368, -0.0275, 0.1315 ],
    [ -0.0471, -0.0282, -0.0275, 0.0830, -0.0602 ],
    [ -0.0914, -0.0181, 0.1315, -0.0602, 0.3610 ],
]
f[PNOISE] = 0.1411

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)
Likelihoods:lx_params.csv (fit)

Inputs

Input Data:cx_obs_params.csv

Starting f[X] values (before fitting)

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
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