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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Model Description

Name:builtin_tut_example
Title:First order absorption model with peripheral compartment
Author:PoPy for PK/PD
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
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 1.0000 0.1199 0.8801 0.8801
f[CL] 1.0000 1.5601 0.5601 0.5601
f[V1] 20.0000 33.6375 13.6375 0.6819
f[Q] 0.5000 2.2214 1.7214 3.4428
f[V2] 100.0000 116.6198 16.6198 0.1662

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.1481 0.0481 0.4807

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 0.0586 0.0086 0.1726
f[KA_isv;CL_isv] 0.0100 0.0210 0.0110 1.0993
f[KA_isv;V1_isv] 0.0100 0.0668 0.0568 5.6796
f[KA_isv;Q_isv] 0.0100 -0.0018 0.0118 1.1761
f[KA_isv;V2_isv] 0.0100 0.0072 0.0028 0.2789
f[CL_isv;KA_isv] 0.0100 0.0210 0.0110 1.0993
f[CL_isv] 0.0500 0.1218 0.0718 1.4351
f[CL_isv;V1_isv] 0.0100 0.1168 0.1068 10.6751
f[CL_isv;Q_isv] 0.0100 -0.0631 0.0731 7.3065
f[CL_isv;V2_isv] 0.0100 -0.2471 0.2571 25.7125
f[V1_isv;KA_isv] 0.0100 0.0668 0.0568 5.6796
f[V1_isv;CL_isv] 0.0100 0.1168 0.1068 10.6751
f[V1_isv] 0.0500 0.1978 0.1478 2.9556
f[V1_isv;Q_isv] 0.0100 -0.0291 0.0391 3.9078
f[V1_isv;V2_isv] 0.0100 -0.1546 0.1646 16.4598
f[Q_isv;KA_isv] 0.0100 -0.0018 0.0118 1.1761
f[Q_isv;CL_isv] 0.0100 -0.0631 0.0731 7.3065
f[Q_isv;V1_isv] 0.0100 -0.0291 0.0391 3.9078
f[Q_isv] 0.0500 0.0584 0.0084 0.1684
f[Q_isv;V2_isv] 0.0100 0.2039 0.1939 19.3899
f[V2_isv;KA_isv] 0.0100 0.0072 0.0028 0.2789
f[V2_isv;CL_isv] 0.0100 -0.2471 0.2571 25.7125
f[V2_isv;V1_isv] 0.0100 -0.1546 0.1646 16.4598
f[V2_isv;Q_isv] 0.0100 0.2039 0.1939 19.3899
f[V2_isv] 0.0500 0.7792 0.7292 14.5841

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-887.7873

which required 1.30 iterations and took 461.39 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1199
f[CL] = 1.5601
f[V1] = 33.6375
f[Q] = 2.2214
f[V2] = 116.6198
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0586, 0.0210, 0.0668, -0.0018, 0.0072 ],
    [ 0.0210, 0.1218, 0.1168, -0.0631, -0.2471 ],
    [ 0.0668, 0.1168, 0.1978, -0.0291, -0.1546 ],
    [ -0.0018, -0.0631, -0.0291, 0.0584, 0.2039 ],
    [ 0.0072, -0.2471, -0.1546, 0.2039, 0.7792 ],
]
f[PNOISE] = 0.1481

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)
Likelihoods:lx_params.csv (fit)

Inputs

Input Data:cx_obs_params.csv

Starting f[X] values (before fitting)

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
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