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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Model Description

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

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 1.0000 0.1032 0.8968 0.8968
f[CL] 1.0000 2.1793 1.1793 1.1793
f[V1] 20.0000 25.0475 5.0475 0.2524
f[Q] 0.5000 1.8821 1.3821 2.7643
f[V2] 100.0000 60.2802 39.7198 0.3972

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.1390 0.0390 0.3904

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 0.0476 0.0024 0.0473
f[KA_isv;CL_isv] 0.0100 0.0265 0.0165 1.6469
f[KA_isv;V1_isv] 0.0100 0.0354 0.0254 2.5391
f[KA_isv;Q_isv] 0.0100 0.0016 0.0084 0.8389
f[KA_isv;V2_isv] 0.0100 -0.0896 0.0996 9.9573
f[CL_isv;KA_isv] 0.0100 0.0265 0.0165 1.6469
f[CL_isv] 0.0500 0.0253 0.0247 0.4946
f[CL_isv;V1_isv] 0.0100 0.0438 0.0338 3.3823
f[CL_isv;Q_isv] 0.0100 0.0015 0.0085 0.8484
f[CL_isv;V2_isv] 0.0100 -0.0546 0.0646 6.4625
f[V1_isv;KA_isv] 0.0100 0.0354 0.0254 2.5391
f[V1_isv;CL_isv] 0.0100 0.0438 0.0338 3.3823
f[V1_isv] 0.0500 0.2507 0.2007 4.0143
f[V1_isv;Q_isv] 0.0100 0.0071 0.0029 0.2898
f[V1_isv;V2_isv] 0.0100 -0.2664 0.2764 27.6369
f[Q_isv;KA_isv] 0.0100 0.0016 0.0084 0.8389
f[Q_isv;CL_isv] 0.0100 0.0015 0.0085 0.8484
f[Q_isv;V1_isv] 0.0100 0.0071 0.0029 0.2898
f[Q_isv] 0.0500 0.0028 0.0472 0.9438
f[Q_isv;V2_isv] 0.0100 0.0116 0.0016 0.1611
f[V2_isv;KA_isv] 0.0100 -0.0896 0.0996 9.9573
f[V2_isv;CL_isv] 0.0100 -0.0546 0.0646 6.4625
f[V2_isv;V1_isv] 0.0100 -0.2664 0.2764 27.6369
f[V2_isv;Q_isv] 0.0100 0.0116 0.0016 0.1611
f[V2_isv] 0.0500 0.6682 0.6182 12.3631

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-910.6570

which required 1.30 iterations and took 474.54 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1032
f[CL] = 2.1793
f[V1] = 25.0475
f[Q] = 1.8821
f[V2] = 60.2802
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0476, 0.0265, 0.0354, 0.0016, -0.0896 ],
    [ 0.0265, 0.0253, 0.0438, 0.0015, -0.0546 ],
    [ 0.0354, 0.0438, 0.2507, 0.0071, -0.2664 ],
    [ 0.0016, 0.0015, 0.0071, 0.0028, 0.0116 ],
    [ -0.0896, -0.0546, -0.2664, 0.0116, 0.6682 ],
]
f[PNOISE] = 0.1390

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:builtin_fit_example_data.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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