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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

-887.8461

which required 1.30 iterations and took 451.80 seconds

Final fitted fixed effects

f[KA] = 0.1207
f[CL] = 1.5708
f[V1] = 33.8896
f[Q] = 2.2287
f[V2] = 114.9338
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0630, 0.0208, 0.0700, -0.0019, 0.0091 ],
    [ 0.0208, 0.1208, 0.1138, -0.0618, -0.2448 ],
    [ 0.0700, 0.1138, 0.1965, -0.0260, -0.1460 ],
    [ -0.0019, -0.0618, -0.0260, 0.0574, 0.1992 ],
    [ 0.0091, -0.2448, -0.1460, 0.1992, 0.7660 ],
]
f[PNOISE] = 0.1478

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 0.1207 0.8793 0.8793
f[CL] 1.0000 1.5708 0.5708 0.5708
f[V1] 20.0000 33.8896 0.6945 13.8896
f[Q] 0.5000 2.2287 3.4574 1.7287
f[V2] 100.0000 114.9338 0.1493 14.9338

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1478 0.4784 0.0478

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.0630 0.2604 0.0130
f[KA_isv;CL_isv] 0.0100 0.0208 1.0763 0.0108
f[KA_isv;V1_isv] 0.0100 0.0700 6.0041 0.0600
f[KA_isv;Q_isv] 0.0100 -0.0019 1.1930 0.0119
f[KA_isv;V2_isv] 0.0100 0.0091 0.0859 0.0009
f[CL_isv;KA_isv] 0.0100 0.0208 1.0763 0.0108
f[CL_isv] 0.0500 0.1208 1.4156 0.0708
f[CL_isv;V1_isv] 0.0100 0.1138 10.3827 0.1038
f[CL_isv;Q_isv] 0.0100 -0.0618 7.1801 0.0718
f[CL_isv;V2_isv] 0.0100 -0.2448 25.4805 0.2548
f[V1_isv;KA_isv] 0.0100 0.0700 6.0041 0.0600
f[V1_isv;CL_isv] 0.0100 0.1138 10.3827 0.1038
f[V1_isv] 0.0500 0.1965 2.9309 0.1465
f[V1_isv;Q_isv] 0.0100 -0.0260 3.6005 0.0360
f[V1_isv;V2_isv] 0.0100 -0.1460 15.5955 0.1560
f[Q_isv;KA_isv] 0.0100 -0.0019 1.1930 0.0119
f[Q_isv;CL_isv] 0.0100 -0.0618 7.1801 0.0718
f[Q_isv;V1_isv] 0.0100 -0.0260 3.6005 0.0360
f[Q_isv] 0.0500 0.0574 0.1479 0.0074
f[Q_isv;V2_isv] 0.0100 0.1992 18.9217 0.1892
f[V2_isv;KA_isv] 0.0100 0.0091 0.0859 0.0009
f[V2_isv;CL_isv] 0.0100 -0.2448 25.4805 0.2548
f[V2_isv;V1_isv] 0.0100 -0.1460 15.5955 0.1560
f[V2_isv;Q_isv] 0.0100 0.1992 18.9217 0.1892
f[V2_isv] 0.0500 0.7660 14.3195 0.7160
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