- Language: en
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 |
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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 fitted fixed effects¶
f[KA] = 0.2115
f[CL] = 2.0587
f[V1] = 53.0562
f[Q] = 0.9970
f[V2] = 104.3821
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1078, 0.0156, -0.0551, 0.0615, -0.0371 ],
[ 0.0156, 0.0658, 0.0054, -0.0648, -0.0879 ],
[ -0.0551, 0.0054, 0.0535, -0.0610, 0.0215 ],
[ 0.0615, -0.0648, -0.0610, 0.3495, 0.0985 ],
[ -0.0371, -0.0879, 0.0215, 0.0985, 0.1857 ]
]
f[PNOISE] = 0.1415
Fitted parameter .csv files¶
Fixed Effects: | fx_params.csv (fit) |
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Random Effects: | rx_params.csv (fit) |
Model params: | mx_params.csv (fit) |
State values: | sx_params.csv (fit) |
Predictions: | px_params.csv (fit) |
Plots¶
Comparison¶
Compare Main f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA] | 0.2115 | 1.0000 | 0.7885 | 0.7885 |
f[CL] | 2.0587 | 1.0000 | 1.0587 | 1.0587 |
f[V1] | 53.0562 | 20.0000 | 1.6528 | 33.0562 |
f[Q] | 0.9970 | 0.5000 | 0.9941 | 0.4970 |
f[V2] | 104.3821 | 100.0000 | 0.0438 | 4.3821 |
Compare Noise f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1415 | 0.1000 | 0.4146 | 0.0415 |
Compare Variance f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.1078 | 0.0500 | 1.1563 | 0.0578 |
f[KA_isv;CL_isv] | 0.0156 | 0.0100 | 0.5561 | 0.0056 |
f[KA_isv;V1_isv] | -0.0551 | 0.0100 | 6.5113 | 0.0651 |
f[KA_isv;Q_isv] | 0.0615 | 0.0100 | 5.1516 | 0.0515 |
f[KA_isv;V2_isv] | -0.0371 | 0.0100 | 4.7117 | 0.0471 |
f[CL_isv;KA_isv] | 0.0156 | 0.0100 | 0.5561 | 0.0056 |
f[CL_isv] | 0.0658 | 0.0500 | 0.3153 | 0.0158 |
f[CL_isv;V1_isv] | 0.0054 | 0.0100 | 0.4612 | 0.0046 |
f[CL_isv;Q_isv] | -0.0648 | 0.0100 | 7.4834 | 0.0748 |
f[CL_isv;V2_isv] | -0.0879 | 0.0100 | 9.7885 | 0.0979 |
f[V1_isv;KA_isv] | -0.0551 | 0.0100 | 6.5113 | 0.0651 |
f[V1_isv;CL_isv] | 0.0054 | 0.0100 | 0.4612 | 0.0046 |
f[V1_isv] | 0.0535 | 0.0500 | 0.0708 | 0.0035 |
f[V1_isv;Q_isv] | -0.0610 | 0.0100 | 7.1003 | 0.0710 |
f[V1_isv;V2_isv] | 0.0215 | 0.0100 | 1.1532 | 0.0115 |
f[Q_isv;KA_isv] | 0.0615 | 0.0100 | 5.1516 | 0.0515 |
f[Q_isv;CL_isv] | -0.0648 | 0.0100 | 7.4834 | 0.0748 |
f[Q_isv;V1_isv] | -0.0610 | 0.0100 | 7.1003 | 0.0710 |
f[Q_isv] | 0.3495 | 0.0500 | 5.9891 | 0.2995 |
f[Q_isv;V2_isv] | 0.0985 | 0.0100 | 8.8473 | 0.0885 |
f[V2_isv;KA_isv] | -0.0371 | 0.0100 | 4.7117 | 0.0471 |
f[V2_isv;CL_isv] | -0.0879 | 0.0100 | 9.7885 | 0.0979 |
f[V2_isv;V1_isv] | 0.0215 | 0.0100 | 1.1532 | 0.0115 |
f[V2_isv;Q_isv] | 0.0985 | 0.0100 | 8.8473 | 0.0885 |
f[V2_isv] | 0.1857 | 0.0500 | 2.7141 | 0.1357 |