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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 objective value

-913.0781

which required N. iterations and took 384.88 seconds

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

Dense sim plots

Alternatively see All dense_sim graph 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
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