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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 objective value

-912.2423

which required 1.30 iterations and took 361.27 seconds

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

Dense sim plots

Alternatively see All dense_sim graph 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
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