- 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 |
---|---|
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] = 0.1037
f[CL] = 2.1890
f[V1] = 24.4333
f[Q] = 1.9623
f[V2] = 57.4482
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0419, 0.0155, 0.0370, 0.0056, -0.0810 ],
[ 0.0155, 0.0148, 0.0388, 0.0040, -0.0306 ],
[ 0.0370, 0.0388, 0.2833, 0.0233, -0.3170 ],
[ 0.0056, 0.0040, 0.0233, 0.0046, -0.0198 ],
[ -0.0810, -0.0306, -0.3170, -0.0198, 0.6653 ],
]
f[PNOISE] = 0.1421
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 | 0.1037 | 0.8963 | 0.8963 |
f[CL] | 1.0000 | 2.1890 | 1.1890 | 1.1890 |
f[V1] | 20.0000 | 24.4333 | 0.2217 | 4.4333 |
f[Q] | 0.5000 | 1.9623 | 2.9246 | 1.4623 |
f[V2] | 100.0000 | 57.4482 | 0.4255 | 42.5518 |
Compare Noise f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1000 | 0.1421 | 0.4214 | 0.0421 |
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.0500 | 0.0419 | 0.1623 | 0.0081 |
f[KA_isv;CL_isv] | 0.0100 | 0.0155 | 0.5519 | 0.0055 |
f[KA_isv;V1_isv] | 0.0100 | 0.0370 | 2.7028 | 0.0270 |
f[KA_isv;Q_isv] | 0.0100 | 0.0056 | 0.4392 | 0.0044 |
f[KA_isv;V2_isv] | 0.0100 | -0.0810 | 9.1006 | 0.0910 |
f[CL_isv;KA_isv] | 0.0100 | 0.0155 | 0.5519 | 0.0055 |
f[CL_isv] | 0.0500 | 0.0148 | 0.7038 | 0.0352 |
f[CL_isv;V1_isv] | 0.0100 | 0.0388 | 2.8823 | 0.0288 |
f[CL_isv;Q_isv] | 0.0100 | 0.0040 | 0.5963 | 0.0060 |
f[CL_isv;V2_isv] | 0.0100 | -0.0306 | 4.0570 | 0.0406 |
f[V1_isv;KA_isv] | 0.0100 | 0.0370 | 2.7028 | 0.0270 |
f[V1_isv;CL_isv] | 0.0100 | 0.0388 | 2.8823 | 0.0288 |
f[V1_isv] | 0.0500 | 0.2833 | 4.6660 | 0.2333 |
f[V1_isv;Q_isv] | 0.0100 | 0.0233 | 1.3252 | 0.0133 |
f[V1_isv;V2_isv] | 0.0100 | -0.3170 | 32.7038 | 0.3270 |
f[Q_isv;KA_isv] | 0.0100 | 0.0056 | 0.4392 | 0.0044 |
f[Q_isv;CL_isv] | 0.0100 | 0.0040 | 0.5963 | 0.0060 |
f[Q_isv;V1_isv] | 0.0100 | 0.0233 | 1.3252 | 0.0133 |
f[Q_isv] | 0.0500 | 0.0046 | 0.9079 | 0.0454 |
f[Q_isv;V2_isv] | 0.0100 | -0.0198 | 2.9781 | 0.0298 |
f[V2_isv;KA_isv] | 0.0100 | -0.0810 | 9.1006 | 0.0910 |
f[V2_isv;CL_isv] | 0.0100 | -0.0306 | 4.0570 | 0.0406 |
f[V2_isv;V1_isv] | 0.0100 | -0.3170 | 32.7038 | 0.3270 |
f[V2_isv;Q_isv] | 0.0100 | -0.0198 | 2.9781 | 0.0298 |
f[V2_isv] | 0.0500 | 0.6653 | 12.3061 | 0.6153 |