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First order absorption model with peripheral compartment

[Generated automatically as a Fitting 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_fit.pyml
Input Data:synthetic_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

-887.8029

which required 1.30 iterations and took 341.06 seconds

Final fitted fixed effects

f[KA] = 0.1209
f[CL] = 1.5555
f[V1] = 33.9425
f[Q] = 2.2223
f[V2] = 119.5563
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0564, 0.0202, 0.0672, -0.0035, 0.0082 ],
    [ 0.0202, 0.1257, 0.1163, -0.0664, -0.2501 ],
    [ 0.0672, 0.1163, 0.2004, -0.0317, -0.1448 ],
    [ -0.0035, -0.0664, -0.0317, 0.0584, 0.2049 ],
    [ 0.0082, -0.2501, -0.1448, 0.2049, 0.7798 ],
]
f[PNOISE] = 0.1480

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 Starting Value Fitted Value Prop Change Abs Change
f[KA] 1.0000 0.1209 0.8791 0.8791
f[CL] 1.0000 1.5555 0.5555 0.5555
f[V1] 20.0000 33.9425 0.6971 13.9425
f[Q] 0.5000 2.2223 3.4447 1.7223
f[V2] 100.0000 119.5563 0.1956 19.5563

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1480 0.4795 0.0480

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.0564 0.1273 0.0064
f[KA_isv;CL_isv] 0.0100 0.0202 1.0206 0.0102
f[KA_isv;V1_isv] 0.0100 0.0672 5.7204 0.0572
f[KA_isv;Q_isv] 0.0100 -0.0035 1.3471 0.0135
f[KA_isv;V2_isv] 0.0100 0.0082 0.1848 0.0018
f[CL_isv;KA_isv] 0.0100 0.0202 1.0206 0.0102
f[CL_isv] 0.0500 0.1257 1.5144 0.0757
f[CL_isv;V1_isv] 0.0100 0.1163 10.6261 0.1063
f[CL_isv;Q_isv] 0.0100 -0.0664 7.6386 0.0764
f[CL_isv;V2_isv] 0.0100 -0.2501 26.0114 0.2601
f[V1_isv;KA_isv] 0.0100 0.0672 5.7204 0.0572
f[V1_isv;CL_isv] 0.0100 0.1163 10.6261 0.1063
f[V1_isv] 0.0500 0.2004 3.0082 0.1504
f[V1_isv;Q_isv] 0.0100 -0.0317 4.1725 0.0417
f[V1_isv;V2_isv] 0.0100 -0.1448 15.4750 0.1548
f[Q_isv;KA_isv] 0.0100 -0.0035 1.3471 0.0135
f[Q_isv;CL_isv] 0.0100 -0.0664 7.6386 0.0764
f[Q_isv;V1_isv] 0.0100 -0.0317 4.1725 0.0417
f[Q_isv] 0.0500 0.0584 0.1686 0.0084
f[Q_isv;V2_isv] 0.0100 0.2049 19.4901 0.1949
f[V2_isv;KA_isv] 0.0100 0.0082 0.1848 0.0018
f[V2_isv;CL_isv] 0.0100 -0.2501 26.0114 0.2601
f[V2_isv;V1_isv] 0.0100 -0.1448 15.4750 0.1548
f[V2_isv;Q_isv] 0.0100 0.2049 19.4901 0.1949
f[V2_isv] 0.0500 0.7798 14.5952 0.7298
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