- 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 |
---|---|
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] = 0.1209
f[CL] = 1.5555
f[V1] = 33.9425
f[Q] = 2.2223
f[V2] = 119.5563
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.0564, 0.0202, 0.0672, -0.0035, 0.0082 ],
[ 0.0202, 0.1257, 0.1163, -0.0664, -0.2501 ],
[ 0.0672, 0.1163, 0.2004, -0.0317, -0.1448 ],
[ -0.0035, -0.0664, -0.0317, 0.0584, 0.2049 ],
[ 0.0082, -0.2501, -0.1448, 0.2049, 0.7798 ],
]
f[PNOISE] = 0.1480
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 | 0.1209 | 0.8791 | 0.8791 |
f[CL] | 1.0000 | 1.5555 | 0.5555 | 0.5555 |
f[V1] | 20.0000 | 33.9425 | 0.6971 | 13.9425 |
f[Q] | 0.5000 | 2.2223 | 3.4447 | 1.7223 |
f[V2] | 100.0000 | 119.5563 | 0.1956 | 19.5563 |
Compare Noise f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1000 | 0.1480 | 0.4795 | 0.0480 |
Compare Variance f[X]¶
Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.0500 | 0.0564 | 0.1273 | 0.0064 |
f[KA_isv;CL_isv] | 0.0100 | 0.0202 | 1.0206 | 0.0102 |
f[KA_isv;V1_isv] | 0.0100 | 0.0672 | 5.7204 | 0.0572 |
f[KA_isv;Q_isv] | 0.0100 | -0.0035 | 1.3471 | 0.0135 |
f[KA_isv;V2_isv] | 0.0100 | 0.0082 | 0.1848 | 0.0018 |
f[CL_isv;KA_isv] | 0.0100 | 0.0202 | 1.0206 | 0.0102 |
f[CL_isv] | 0.0500 | 0.1257 | 1.5144 | 0.0757 |
f[CL_isv;V1_isv] | 0.0100 | 0.1163 | 10.6261 | 0.1063 |
f[CL_isv;Q_isv] | 0.0100 | -0.0664 | 7.6386 | 0.0764 |
f[CL_isv;V2_isv] | 0.0100 | -0.2501 | 26.0114 | 0.2601 |
f[V1_isv;KA_isv] | 0.0100 | 0.0672 | 5.7204 | 0.0572 |
f[V1_isv;CL_isv] | 0.0100 | 0.1163 | 10.6261 | 0.1063 |
f[V1_isv] | 0.0500 | 0.2004 | 3.0082 | 0.1504 |
f[V1_isv;Q_isv] | 0.0100 | -0.0317 | 4.1725 | 0.0417 |
f[V1_isv;V2_isv] | 0.0100 | -0.1448 | 15.4750 | 0.1548 |
f[Q_isv;KA_isv] | 0.0100 | -0.0035 | 1.3471 | 0.0135 |
f[Q_isv;CL_isv] | 0.0100 | -0.0664 | 7.6386 | 0.0764 |
f[Q_isv;V1_isv] | 0.0100 | -0.0317 | 4.1725 | 0.0417 |
f[Q_isv] | 0.0500 | 0.0584 | 0.1686 | 0.0084 |
f[Q_isv;V2_isv] | 0.0100 | 0.2049 | 19.4901 | 0.1949 |
f[V2_isv;KA_isv] | 0.0100 | 0.0082 | 0.1848 | 0.0018 |
f[V2_isv;CL_isv] | 0.0100 | -0.2501 | 26.0114 | 0.2601 |
f[V2_isv;V1_isv] | 0.0100 | -0.1448 | 15.4750 | 0.1548 |
f[V2_isv;Q_isv] | 0.0100 | 0.2049 | 19.4901 | 0.1949 |
f[V2_isv] | 0.0500 | 0.7798 | 14.5952 | 0.7298 |