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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

-911.1884

which required 1.30 iterations and took 466.38 seconds

Final fitted fixed effects

f[KA] = 0.1037
f[CL] = 2.1890
f[V1] = 24.4333
f[Q] = 1.9623
f[V2] = 57.4482
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0419, 0.0155, 0.0370, 0.0056, -0.0810 ],
    [ 0.0155, 0.0148, 0.0388, 0.0040, -0.0306 ],
    [ 0.0370, 0.0388, 0.2833, 0.0233, -0.3170 ],
    [ 0.0056, 0.0040, 0.0233, 0.0046, -0.0198 ],
    [ -0.0810, -0.0306, -0.3170, -0.0198, 0.6653 ],
]
f[PNOISE] = 0.1421

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 0.1037 0.8963 0.8963
f[CL] 1.0000 2.1890 1.1890 1.1890
f[V1] 20.0000 24.4333 0.2217 4.4333
f[Q] 0.5000 1.9623 2.9246 1.4623
f[V2] 100.0000 57.4482 0.4255 42.5518

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1421 0.4214 0.0421

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.0419 0.1623 0.0081
f[KA_isv;CL_isv] 0.0100 0.0155 0.5519 0.0055
f[KA_isv;V1_isv] 0.0100 0.0370 2.7028 0.0270
f[KA_isv;Q_isv] 0.0100 0.0056 0.4392 0.0044
f[KA_isv;V2_isv] 0.0100 -0.0810 9.1006 0.0910
f[CL_isv;KA_isv] 0.0100 0.0155 0.5519 0.0055
f[CL_isv] 0.0500 0.0148 0.7038 0.0352
f[CL_isv;V1_isv] 0.0100 0.0388 2.8823 0.0288
f[CL_isv;Q_isv] 0.0100 0.0040 0.5963 0.0060
f[CL_isv;V2_isv] 0.0100 -0.0306 4.0570 0.0406
f[V1_isv;KA_isv] 0.0100 0.0370 2.7028 0.0270
f[V1_isv;CL_isv] 0.0100 0.0388 2.8823 0.0288
f[V1_isv] 0.0500 0.2833 4.6660 0.2333
f[V1_isv;Q_isv] 0.0100 0.0233 1.3252 0.0133
f[V1_isv;V2_isv] 0.0100 -0.3170 32.7038 0.3270
f[Q_isv;KA_isv] 0.0100 0.0056 0.4392 0.0044
f[Q_isv;CL_isv] 0.0100 0.0040 0.5963 0.0060
f[Q_isv;V1_isv] 0.0100 0.0233 1.3252 0.0133
f[Q_isv] 0.0500 0.0046 0.9079 0.0454
f[Q_isv;V2_isv] 0.0100 -0.0198 2.9781 0.0298
f[V2_isv;KA_isv] 0.0100 -0.0810 9.1006 0.0910
f[V2_isv;CL_isv] 0.0100 -0.0306 4.0570 0.0406
f[V2_isv;V1_isv] 0.0100 -0.3170 32.7038 0.3270
f[V2_isv;Q_isv] 0.0100 -0.0198 2.9781 0.0298
f[V2_isv] 0.0500 0.6653 12.3061 0.6153
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