- 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] = 0.1019
f[CL] = 2.1528
f[V1] = 24.1372
f[Q] = 1.9547
f[V2] = 61.7683
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0322, 0.0145, 0.0383, -0.0011, -0.0925 ],
[ 0.0145, 0.0165, 0.0431, -0.0013, -0.0482 ],
[ 0.0383, 0.0431, 0.3030, 0.0110, -0.3540 ],
[ -0.0011, -0.0013, 0.0110, 0.0040, 0.0023 ],
[ -0.0925, -0.0482, -0.3540, 0.0023, 0.7273 ],
]
f[PNOISE] = 0.1399
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 | 0.1019 | 0.8981 | 0.8981 |
f[CL] | 1.0000 | 2.1528 | 1.1528 | 1.1528 |
f[V1] | 20.0000 | 24.1372 | 0.2069 | 4.1372 |
f[Q] | 0.5000 | 1.9547 | 2.9093 | 1.4547 |
f[V2] | 100.0000 | 61.7683 | 0.3823 | 38.2317 |
Compare Noise f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1000 | 0.1399 | 0.3988 | 0.0399 |
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.0500 | 0.0322 | 0.3564 | 0.0178 |
f[KA_isv;CL_isv] | 0.0100 | 0.0145 | 0.4470 | 0.0045 |
f[KA_isv;V1_isv] | 0.0100 | 0.0383 | 2.8265 | 0.0283 |
f[KA_isv;Q_isv] | 0.0100 | -0.0011 | 1.1127 | 0.0111 |
f[KA_isv;V2_isv] | 0.0100 | -0.0925 | 10.2460 | 0.1025 |
f[CL_isv;KA_isv] | 0.0100 | 0.0145 | 0.4470 | 0.0045 |
f[CL_isv] | 0.0500 | 0.0165 | 0.6706 | 0.0335 |
f[CL_isv;V1_isv] | 0.0100 | 0.0431 | 3.3118 | 0.0331 |
f[CL_isv;Q_isv] | 0.0100 | -0.0013 | 1.1291 | 0.0113 |
f[CL_isv;V2_isv] | 0.0100 | -0.0482 | 5.8199 | 0.0582 |
f[V1_isv;KA_isv] | 0.0100 | 0.0383 | 2.8265 | 0.0283 |
f[V1_isv;CL_isv] | 0.0100 | 0.0431 | 3.3118 | 0.0331 |
f[V1_isv] | 0.0500 | 0.3030 | 5.0596 | 0.2530 |
f[V1_isv;Q_isv] | 0.0100 | 0.0110 | 0.0974 | 0.0010 |
f[V1_isv;V2_isv] | 0.0100 | -0.3540 | 36.3998 | 0.3640 |
f[Q_isv;KA_isv] | 0.0100 | -0.0011 | 1.1127 | 0.0111 |
f[Q_isv;CL_isv] | 0.0100 | -0.0013 | 1.1291 | 0.0113 |
f[Q_isv;V1_isv] | 0.0100 | 0.0110 | 0.0974 | 0.0010 |
f[Q_isv] | 0.0500 | 0.0040 | 0.9198 | 0.0460 |
f[Q_isv;V2_isv] | 0.0100 | 0.0023 | 0.7675 | 0.0077 |
f[V2_isv;KA_isv] | 0.0100 | -0.0925 | 10.2460 | 0.1025 |
f[V2_isv;CL_isv] | 0.0100 | -0.0482 | 5.8199 | 0.0582 |
f[V2_isv;V1_isv] | 0.0100 | -0.3540 | 36.3998 | 0.3640 |
f[V2_isv;Q_isv] | 0.0100 | 0.0023 | 0.7675 | 0.0077 |
f[V2_isv] | 0.0500 | 0.7273 | 13.5452 | 0.6773 |