- 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] = 0.1015
f[CL] = 2.3617
f[V1] = 24.5062
f[Q] = 1.8140
f[V2] = 45.0126
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0677, 0.0196, 0.0351, 0.0427, -0.0393 ],
[ 0.0196, 0.0149, 0.0174, 0.0094, 0.0124 ],
[ 0.0351, 0.0174, 0.2453, 0.0783, -0.1548 ],
[ 0.0427, 0.0094, 0.0783, 0.0475, -0.0534 ],
[ -0.0393, 0.0124, -0.1548, -0.0534, 0.3086 ],
]
f[PNOISE] = 0.1400
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¶
Comparison¶
Compare Main f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA] | 1.0000 | 0.1015 | 0.8985 | 0.8985 |
f[CL] | 1.0000 | 2.3617 | 1.3617 | 1.3617 |
f[V1] | 20.0000 | 24.5062 | 0.2253 | 4.5062 |
f[Q] | 0.5000 | 1.8140 | 2.6280 | 1.3140 |
f[V2] | 100.0000 | 45.0126 | 0.5499 | 54.9874 |
Compare Noise f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1000 | 0.1400 | 0.4005 | 0.0400 |
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.0500 | 0.0677 | 0.3549 | 0.0177 |
f[KA_isv;CL_isv] | 0.0100 | 0.0196 | 0.9605 | 0.0096 |
f[KA_isv;V1_isv] | 0.0100 | 0.0351 | 2.5060 | 0.0251 |
f[KA_isv;Q_isv] | 0.0100 | 0.0427 | 3.2706 | 0.0327 |
f[KA_isv;V2_isv] | 0.0100 | -0.0393 | 4.9343 | 0.0493 |
f[CL_isv;KA_isv] | 0.0100 | 0.0196 | 0.9605 | 0.0096 |
f[CL_isv] | 0.0500 | 0.0149 | 0.7025 | 0.0351 |
f[CL_isv;V1_isv] | 0.0100 | 0.0174 | 0.7415 | 0.0074 |
f[CL_isv;Q_isv] | 0.0100 | 0.0094 | 0.0559 | 0.0006 |
f[CL_isv;V2_isv] | 0.0100 | 0.0124 | 0.2420 | 0.0024 |
f[V1_isv;KA_isv] | 0.0100 | 0.0351 | 2.5060 | 0.0251 |
f[V1_isv;CL_isv] | 0.0100 | 0.0174 | 0.7415 | 0.0074 |
f[V1_isv] | 0.0500 | 0.2453 | 3.9068 | 0.1953 |
f[V1_isv;Q_isv] | 0.0100 | 0.0783 | 6.8291 | 0.0683 |
f[V1_isv;V2_isv] | 0.0100 | -0.1548 | 16.4829 | 0.1648 |
f[Q_isv;KA_isv] | 0.0100 | 0.0427 | 3.2706 | 0.0327 |
f[Q_isv;CL_isv] | 0.0100 | 0.0094 | 0.0559 | 0.0006 |
f[Q_isv;V1_isv] | 0.0100 | 0.0783 | 6.8291 | 0.0683 |
f[Q_isv] | 0.0500 | 0.0475 | 0.0499 | 0.0025 |
f[Q_isv;V2_isv] | 0.0100 | -0.0534 | 6.3370 | 0.0634 |
f[V2_isv;KA_isv] | 0.0100 | -0.0393 | 4.9343 | 0.0493 |
f[V2_isv;CL_isv] | 0.0100 | 0.0124 | 0.2420 | 0.0024 |
f[V2_isv;V1_isv] | 0.0100 | -0.1548 | 16.4829 | 0.1648 |
f[V2_isv;Q_isv] | 0.0100 | -0.0534 | 6.3370 | 0.0634 |
f[V2_isv] | 0.0500 | 0.3086 | 5.1711 | 0.2586 |