- Language: en
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 |
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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 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) |
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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 | 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 |