- 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] = 0.1089
f[CL] = 2.2693
f[V1] = 27.1342
f[Q] = 1.8655
f[V2] = 52.0726
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0807, 0.0287, 0.0288, 0.0131, -0.0713 ],
[ 0.0287, 0.0228, 0.0284, 0.0062, -0.0334 ],
[ 0.0288, 0.0284, 0.1865, 0.0233, -0.1768 ],
[ 0.0131, 0.0062, 0.0233, 0.0109, -0.0022 ],
[ -0.0713, -0.0334, -0.1768, -0.0022, 0.5211 ],
]
f[PNOISE] = 0.1395
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 | 0.1089 | 0.8911 | 0.8911 |
f[CL] | 1.0000 | 2.2693 | 1.2693 | 1.2693 |
f[V1] | 20.0000 | 27.1342 | 0.3567 | 7.1342 |
f[Q] | 0.5000 | 1.8655 | 2.7310 | 1.3655 |
f[V2] | 100.0000 | 52.0726 | 0.4793 | 47.9274 |
Compare Noise f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1000 | 0.1395 | 0.3946 | 0.0395 |
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.0500 | 0.0807 | 0.6132 | 0.0307 |
f[KA_isv;CL_isv] | 0.0100 | 0.0287 | 1.8699 | 0.0187 |
f[KA_isv;V1_isv] | 0.0100 | 0.0288 | 1.8843 | 0.0188 |
f[KA_isv;Q_isv] | 0.0100 | 0.0131 | 0.3122 | 0.0031 |
f[KA_isv;V2_isv] | 0.0100 | -0.0713 | 8.1342 | 0.0813 |
f[CL_isv;KA_isv] | 0.0100 | 0.0287 | 1.8699 | 0.0187 |
f[CL_isv] | 0.0500 | 0.0228 | 0.5430 | 0.0272 |
f[CL_isv;V1_isv] | 0.0100 | 0.0284 | 1.8411 | 0.0184 |
f[CL_isv;Q_isv] | 0.0100 | 0.0062 | 0.3750 | 0.0038 |
f[CL_isv;V2_isv] | 0.0100 | -0.0334 | 4.3411 | 0.0434 |
f[V1_isv;KA_isv] | 0.0100 | 0.0288 | 1.8843 | 0.0188 |
f[V1_isv;CL_isv] | 0.0100 | 0.0284 | 1.8411 | 0.0184 |
f[V1_isv] | 0.0500 | 0.1865 | 2.7307 | 0.1365 |
f[V1_isv;Q_isv] | 0.0100 | 0.0233 | 1.3299 | 0.0133 |
f[V1_isv;V2_isv] | 0.0100 | -0.1768 | 18.6798 | 0.1868 |
f[Q_isv;KA_isv] | 0.0100 | 0.0131 | 0.3122 | 0.0031 |
f[Q_isv;CL_isv] | 0.0100 | 0.0062 | 0.3750 | 0.0038 |
f[Q_isv;V1_isv] | 0.0100 | 0.0233 | 1.3299 | 0.0133 |
f[Q_isv] | 0.0500 | 0.0109 | 0.7829 | 0.0391 |
f[Q_isv;V2_isv] | 0.0100 | -0.0022 | 1.2245 | 0.0122 |
f[V2_isv;KA_isv] | 0.0100 | -0.0713 | 8.1342 | 0.0813 |
f[V2_isv;CL_isv] | 0.0100 | -0.0334 | 4.3411 | 0.0434 |
f[V2_isv;V1_isv] | 0.0100 | -0.1768 | 18.6798 | 0.1868 |
f[V2_isv;Q_isv] | 0.0100 | -0.0022 | 1.2245 | 0.0122 |
f[V2_isv] | 0.0500 | 0.5211 | 9.4217 | 0.4711 |