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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

-908.9331

which required 1.30 iterations and took 359.38 seconds

Final fitted fixed effects

f[KA] = 0.1089
f[CL] = 2.2693
f[V1] = 27.1342
f[Q] = 1.8655
f[V2] = 52.0726
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0807, 0.0287, 0.0288, 0.0131, -0.0713 ],
    [ 0.0287, 0.0228, 0.0284, 0.0062, -0.0334 ],
    [ 0.0288, 0.0284, 0.1865, 0.0233, -0.1768 ],
    [ 0.0131, 0.0062, 0.0233, 0.0109, -0.0022 ],
    [ -0.0713, -0.0334, -0.1768, -0.0022, 0.5211 ],
]
f[PNOISE] = 0.1395

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 0.1089 0.8911 0.8911
f[CL] 1.0000 2.2693 1.2693 1.2693
f[V1] 20.0000 27.1342 0.3567 7.1342
f[Q] 0.5000 1.8655 2.7310 1.3655
f[V2] 100.0000 52.0726 0.4793 47.9274

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1395 0.3946 0.0395

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.0807 0.6132 0.0307
f[KA_isv;CL_isv] 0.0100 0.0287 1.8699 0.0187
f[KA_isv;V1_isv] 0.0100 0.0288 1.8843 0.0188
f[KA_isv;Q_isv] 0.0100 0.0131 0.3122 0.0031
f[KA_isv;V2_isv] 0.0100 -0.0713 8.1342 0.0813
f[CL_isv;KA_isv] 0.0100 0.0287 1.8699 0.0187
f[CL_isv] 0.0500 0.0228 0.5430 0.0272
f[CL_isv;V1_isv] 0.0100 0.0284 1.8411 0.0184
f[CL_isv;Q_isv] 0.0100 0.0062 0.3750 0.0038
f[CL_isv;V2_isv] 0.0100 -0.0334 4.3411 0.0434
f[V1_isv;KA_isv] 0.0100 0.0288 1.8843 0.0188
f[V1_isv;CL_isv] 0.0100 0.0284 1.8411 0.0184
f[V1_isv] 0.0500 0.1865 2.7307 0.1365
f[V1_isv;Q_isv] 0.0100 0.0233 1.3299 0.0133
f[V1_isv;V2_isv] 0.0100 -0.1768 18.6798 0.1868
f[Q_isv;KA_isv] 0.0100 0.0131 0.3122 0.0031
f[Q_isv;CL_isv] 0.0100 0.0062 0.3750 0.0038
f[Q_isv;V1_isv] 0.0100 0.0233 1.3299 0.0133
f[Q_isv] 0.0500 0.0109 0.7829 0.0391
f[Q_isv;V2_isv] 0.0100 -0.0022 1.2245 0.0122
f[V2_isv;KA_isv] 0.0100 -0.0713 8.1342 0.0813
f[V2_isv;CL_isv] 0.0100 -0.0334 4.3411 0.0434
f[V2_isv;V1_isv] 0.0100 -0.1768 18.6798 0.1868
f[V2_isv;Q_isv] 0.0100 -0.0022 1.2245 0.0122
f[V2_isv] 0.0500 0.5211 9.4217 0.4711
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