- 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] = 0.1836
f[CL] = 1.5613
f[V1] = 46.1503
f[Q] = 1.9135
f[V2] = 121.3791
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
[ 0.1126, 0.0235, -0.0555, -0.0183, 0.0169 ],
[ 0.0235, 0.1664, 0.0113, -0.1084, -0.1801 ],
[ -0.0555, 0.0113, 0.0330, -0.0080, -0.0346 ],
[ -0.0183, -0.1084, -0.0080, 0.1897, 0.1934 ],
[ 0.0169, -0.1801, -0.0346, 0.1934, 0.2644 ],
]
f[PNOISE] = 0.1327
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] | 0.1836 | 1.0000 | 0.8164 | 0.8164 |
f[CL] | 1.5613 | 1.0000 | 0.5613 | 0.5613 |
f[V1] | 46.1503 | 20.0000 | 1.3075 | 26.1503 |
f[Q] | 1.9135 | 0.5000 | 2.8271 | 1.4135 |
f[V2] | 121.3791 | 100.0000 | 0.2138 | 21.3791 |
Compare Noise f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[PNOISE] | 0.1327 | 0.1000 | 0.3268 | 0.0327 |
Compare Variance f[X]¶
Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change |
---|---|---|---|---|
f[KA_isv] | 0.1126 | 0.0500 | 1.2517 | 0.0626 |
f[KA_isv;CL_isv] | 0.0235 | 0.0100 | 1.3462 | 0.0135 |
f[KA_isv;V1_isv] | -0.0555 | 0.0100 | 6.5490 | 0.0655 |
f[KA_isv;Q_isv] | -0.0183 | 0.0100 | 2.8345 | 0.0283 |
f[KA_isv;V2_isv] | 0.0169 | 0.0100 | 0.6921 | 0.0069 |
f[CL_isv;KA_isv] | 0.0235 | 0.0100 | 1.3462 | 0.0135 |
f[CL_isv] | 0.1664 | 0.0500 | 2.3286 | 0.1164 |
f[CL_isv;V1_isv] | 0.0113 | 0.0100 | 0.1259 | 0.0013 |
f[CL_isv;Q_isv] | -0.1084 | 0.0100 | 11.8438 | 0.1184 |
f[CL_isv;V2_isv] | -0.1801 | 0.0100 | 19.0146 | 0.1901 |
f[V1_isv;KA_isv] | -0.0555 | 0.0100 | 6.5490 | 0.0655 |
f[V1_isv;CL_isv] | 0.0113 | 0.0100 | 0.1259 | 0.0013 |
f[V1_isv] | 0.0330 | 0.0500 | 0.3391 | 0.0170 |
f[V1_isv;Q_isv] | -0.0080 | 0.0100 | 1.7978 | 0.0180 |
f[V1_isv;V2_isv] | -0.0346 | 0.0100 | 4.4574 | 0.0446 |
f[Q_isv;KA_isv] | -0.0183 | 0.0100 | 2.8345 | 0.0283 |
f[Q_isv;CL_isv] | -0.1084 | 0.0100 | 11.8438 | 0.1184 |
f[Q_isv;V1_isv] | -0.0080 | 0.0100 | 1.7978 | 0.0180 |
f[Q_isv] | 0.1897 | 0.0500 | 2.7946 | 0.1397 |
f[Q_isv;V2_isv] | 0.1934 | 0.0100 | 18.3362 | 0.1834 |
f[V2_isv;KA_isv] | 0.0169 | 0.0100 | 0.6921 | 0.0069 |
f[V2_isv;CL_isv] | -0.1801 | 0.0100 | 19.0146 | 0.1901 |
f[V2_isv;V1_isv] | -0.0346 | 0.0100 | 4.4574 | 0.0446 |
f[V2_isv;Q_isv] | 0.1934 | 0.0100 | 18.3362 | 0.1834 |
f[V2_isv] | 0.2644 | 0.0500 | 4.2890 | 0.2144 |