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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

-913.5629

which required N. iterations and took 736.41 seconds

Final fitted fixed effects

f[KA] = 0.1713
f[CL] = 1.8060
f[V1] = 43.8081
f[Q] = 1.8123
f[V2] = 85.0498
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.1113, 0.0309, -0.0452, -0.0276, 0.0024 ],
    [ 0.0309, 0.1342, 0.0223, -0.1133, -0.2044 ],
    [ -0.0452, 0.0223, 0.0280, -0.0182, -0.0579 ],
    [ -0.0276, -0.1133, -0.0182, 0.2058, 0.2750 ],
    [ 0.0024, -0.2044, -0.0579, 0.2750, 0.4314 ],
]
f[PNOISE] = 0.1339

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 Fitted Value Starting Value Prop Change Abs Change
f[KA] 0.1713 1.0000 0.8287 0.8287
f[CL] 1.8060 1.0000 0.8060 0.8060
f[V1] 43.8081 20.0000 1.1904 23.8081
f[Q] 1.8123 0.5000 2.6247 1.3123
f[V2] 85.0498 100.0000 0.1495 14.9502

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.1339 0.1000 0.3393 0.0339

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.1113 0.0500 1.2255 0.0613
f[KA_isv;CL_isv] 0.0309 0.0100 2.0870 0.0209
f[KA_isv;V1_isv] -0.0452 0.0100 5.5155 0.0552
f[KA_isv;Q_isv] -0.0276 0.0100 3.7559 0.0376
f[KA_isv;V2_isv] 0.0024 0.0100 0.7560 0.0076
f[CL_isv;KA_isv] 0.0309 0.0100 2.0870 0.0209
f[CL_isv] 0.1342 0.0500 1.6849 0.0842
f[CL_isv;V1_isv] 0.0223 0.0100 1.2346 0.0123
f[CL_isv;Q_isv] -0.1133 0.0100 12.3350 0.1233
f[CL_isv;V2_isv] -0.2044 0.0100 21.4427 0.2144
f[V1_isv;KA_isv] -0.0452 0.0100 5.5155 0.0552
f[V1_isv;CL_isv] 0.0223 0.0100 1.2346 0.0123
f[V1_isv] 0.0280 0.0500 0.4398 0.0220
f[V1_isv;Q_isv] -0.0182 0.0100 2.8178 0.0282
f[V1_isv;V2_isv] -0.0579 0.0100 6.7942 0.0679
f[Q_isv;KA_isv] -0.0276 0.0100 3.7559 0.0376
f[Q_isv;CL_isv] -0.1133 0.0100 12.3350 0.1233
f[Q_isv;V1_isv] -0.0182 0.0100 2.8178 0.0282
f[Q_isv] 0.2058 0.0500 3.1168 0.1558
f[Q_isv;V2_isv] 0.2750 0.0100 26.5026 0.2650
f[V2_isv;KA_isv] 0.0024 0.0100 0.7560 0.0076
f[V2_isv;CL_isv] -0.2044 0.0100 21.4427 0.2144
f[V2_isv;V1_isv] -0.0579 0.0100 6.7942 0.0679
f[V2_isv;Q_isv] 0.2750 0.0100 26.5026 0.2650
f[V2_isv] 0.4314 0.0500 7.6275 0.3814
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