- 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] = 0.1846
f[CL] = 1.6740
f[V1] = 47.5042
f[Q] = 1.7764
f[V2] = 109.6770
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0965, 0.0418, -0.0532, -0.0200, -0.0172 ],
[ 0.0418, 0.1443, 0.0079, -0.1026, -0.1679 ],
[ -0.0532, 0.0079, 0.0369, -0.0123, -0.0301 ],
[ -0.0200, -0.1026, -0.0123, 0.1982, 0.2090 ],
[ -0.0172, -0.1679, -0.0301, 0.2090, 0.2667 ],
]
f[PNOISE] = 0.1337
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] | 0.1846 | 1.0000 | 0.8154 | 0.8154 |
f[CL] | 1.6740 | 1.0000 | 0.6740 | 0.6740 |
f[V1] | 47.5042 | 20.0000 | 1.3752 | 27.5042 |
f[Q] | 1.7764 | 0.5000 | 2.5527 | 1.2764 |
f[V2] | 109.6770 | 100.0000 | 0.0968 | 9.6770 |
Compare Noise f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1337 | 0.1000 | 0.3372 | 0.0337 |
Compare Variance f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.0965 | 0.0500 | 0.9301 | 0.0465 |
f[KA_isv;CL_isv] | 0.0418 | 0.0100 | 3.1804 | 0.0318 |
f[KA_isv;V1_isv] | -0.0532 | 0.0100 | 6.3232 | 0.0632 |
f[KA_isv;Q_isv] | -0.0200 | 0.0100 | 3.0007 | 0.0300 |
f[KA_isv;V2_isv] | -0.0172 | 0.0100 | 2.7245 | 0.0272 |
f[CL_isv;KA_isv] | 0.0418 | 0.0100 | 3.1804 | 0.0318 |
f[CL_isv] | 0.1443 | 0.0500 | 1.8854 | 0.0943 |
f[CL_isv;V1_isv] | 0.0079 | 0.0100 | 0.2129 | 0.0021 |
f[CL_isv;Q_isv] | -0.1026 | 0.0100 | 11.2632 | 0.1126 |
f[CL_isv;V2_isv] | -0.1679 | 0.0100 | 17.7906 | 0.1779 |
f[V1_isv;KA_isv] | -0.0532 | 0.0100 | 6.3232 | 0.0632 |
f[V1_isv;CL_isv] | 0.0079 | 0.0100 | 0.2129 | 0.0021 |
f[V1_isv] | 0.0369 | 0.0500 | 0.2610 | 0.0131 |
f[V1_isv;Q_isv] | -0.0123 | 0.0100 | 2.2338 | 0.0223 |
f[V1_isv;V2_isv] | -0.0301 | 0.0100 | 4.0071 | 0.0401 |
f[Q_isv;KA_isv] | -0.0200 | 0.0100 | 3.0007 | 0.0300 |
f[Q_isv;CL_isv] | -0.1026 | 0.0100 | 11.2632 | 0.1126 |
f[Q_isv;V1_isv] | -0.0123 | 0.0100 | 2.2338 | 0.0223 |
f[Q_isv] | 0.1982 | 0.0500 | 2.9646 | 0.1482 |
f[Q_isv;V2_isv] | 0.2090 | 0.0100 | 19.9007 | 0.1990 |
f[V2_isv;KA_isv] | -0.0172 | 0.0100 | 2.7245 | 0.0272 |
f[V2_isv;CL_isv] | -0.1679 | 0.0100 | 17.7906 | 0.1779 |
f[V2_isv;V1_isv] | -0.0301 | 0.0100 | 4.0071 | 0.0401 |
f[V2_isv;Q_isv] | 0.2090 | 0.0100 | 19.9007 | 0.1990 |
f[V2_isv] | 0.2667 | 0.0500 | 4.3341 | 0.2167 |