- 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] = 0.1713
f[CL] = 1.8060
f[V1] = 43.8081
f[Q] = 1.8123
f[V2] = 85.0498
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1113, 0.0309, -0.0452, -0.0276, 0.0024 ],
[ 0.0309, 0.1342, 0.0223, -0.1133, -0.2044 ],
[ -0.0452, 0.0223, 0.0280, -0.0182, -0.0579 ],
[ -0.0276, -0.1133, -0.0182, 0.2058, 0.2750 ],
[ 0.0024, -0.2044, -0.0579, 0.2750, 0.4314 ],
]
f[PNOISE] = 0.1339
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 | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA] | 0.1713 | 1.0000 | 0.8287 | 0.8287 |
f[CL] | 1.8060 | 1.0000 | 0.8060 | 0.8060 |
f[V1] | 43.8081 | 20.0000 | 1.1904 | 23.8081 |
f[Q] | 1.8123 | 0.5000 | 2.6247 | 1.3123 |
f[V2] | 85.0498 | 100.0000 | 0.1495 | 14.9502 |
Compare Noise f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1339 | 0.1000 | 0.3393 | 0.0339 |
Compare Variance f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.1113 | 0.0500 | 1.2255 | 0.0613 |
f[KA_isv;CL_isv] | 0.0309 | 0.0100 | 2.0870 | 0.0209 |
f[KA_isv;V1_isv] | -0.0452 | 0.0100 | 5.5155 | 0.0552 |
f[KA_isv;Q_isv] | -0.0276 | 0.0100 | 3.7559 | 0.0376 |
f[KA_isv;V2_isv] | 0.0024 | 0.0100 | 0.7560 | 0.0076 |
f[CL_isv;KA_isv] | 0.0309 | 0.0100 | 2.0870 | 0.0209 |
f[CL_isv] | 0.1342 | 0.0500 | 1.6849 | 0.0842 |
f[CL_isv;V1_isv] | 0.0223 | 0.0100 | 1.2346 | 0.0123 |
f[CL_isv;Q_isv] | -0.1133 | 0.0100 | 12.3350 | 0.1233 |
f[CL_isv;V2_isv] | -0.2044 | 0.0100 | 21.4427 | 0.2144 |
f[V1_isv;KA_isv] | -0.0452 | 0.0100 | 5.5155 | 0.0552 |
f[V1_isv;CL_isv] | 0.0223 | 0.0100 | 1.2346 | 0.0123 |
f[V1_isv] | 0.0280 | 0.0500 | 0.4398 | 0.0220 |
f[V1_isv;Q_isv] | -0.0182 | 0.0100 | 2.8178 | 0.0282 |
f[V1_isv;V2_isv] | -0.0579 | 0.0100 | 6.7942 | 0.0679 |
f[Q_isv;KA_isv] | -0.0276 | 0.0100 | 3.7559 | 0.0376 |
f[Q_isv;CL_isv] | -0.1133 | 0.0100 | 12.3350 | 0.1233 |
f[Q_isv;V1_isv] | -0.0182 | 0.0100 | 2.8178 | 0.0282 |
f[Q_isv] | 0.2058 | 0.0500 | 3.1168 | 0.1558 |
f[Q_isv;V2_isv] | 0.2750 | 0.0100 | 26.5026 | 0.2650 |
f[V2_isv;KA_isv] | 0.0024 | 0.0100 | 0.7560 | 0.0076 |
f[V2_isv;CL_isv] | -0.2044 | 0.0100 | 21.4427 | 0.2144 |
f[V2_isv;V1_isv] | -0.0579 | 0.0100 | 6.7942 | 0.0679 |
f[V2_isv;Q_isv] | 0.2750 | 0.0100 | 26.5026 | 0.2650 |
f[V2_isv] | 0.4314 | 0.0500 | 7.6275 | 0.3814 |