Population Two Compartment Model and Inter-subject Variance

[Generated automatically as a Tutorial summary]

Model Description

Name:: iv_two_cmp_isv
Title:: Population Two Compartment Model and Inter-subject Variance
Author:: PoPy for PK/PD
Abstract:

Population One Compartment Model and Inter-subject Variance

Keywords:: two compartment model; iv_two_cmp_k; proportional noise; additive noise
Input Script:: iv_two_cmp_isv.pyml
Diagram:

Comparison

True objective value

1799.2332

Final fitted objective value

1795.0662

Compare Main f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[K12]	0.5	0.189	0.2	1.07e-02	5.35%
f[K21]	0.5	0.123	0.15	2.71e-02	18.07%
f[KE]	0.5	0.103	0.1	3.43e-03	3.43%

Compare Noise f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[ANOISE_STD]	100	4.93	5	6.93e-02	1.39%

Compare Variance f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[KE_isv]	0.01	0.297	0.2	9.73e-02	48.63%
f[KE_isv;K12_isv]	0	0	0	0.00e+00	inf
f[KE_isv;K21_isv]	0	0	0	0.00e+00	inf
f[K12_isv;KE_isv]	0	0	0	0.00e+00	inf
f[K12_isv]	0.01	0.161	0.2	3.92e-02	19.62%
f[K12_isv;K21_isv]	0	0	0	0.00e+00	inf
f[K21_isv;KE_isv]	0	0	0	0.00e+00	inf
f[K21_isv;K12_isv]	0	0	0	0.00e+00	inf
f[K21_isv]	0.01	0.185	0.2	1.45e-02	7.27%

Outputs

Fitted f[X] values (after fitting)

f[K12] = 0.1893
f[K21] = 0.1229
f[KE] = 0.1034
f[KE_isv,K12_isv,K21_isv] = [
    [ 0.2973, 0.0000, 0.0000 ],
    [ 0.0000, 0.1608, 0.0000 ],
    [ 0.0000, 0.0000, 0.1855 ],
]
f[ANOISE_STD] = 4.9307

Generated data .csv file

Synthetic Data:: synthetic_data.csv

Gen and Fit Summaries

Gen: Population Two Compartment Model and Inter-subject Variance (gen)
Fit: Population Two Compartment Model and Inter-subject Variance (fit)

Inputs

True f[X] values (for simulation)

f[K12] = 0.2000
f[K21] = 0.1500
f[KE] = 0.1000
f[KE_isv,K12_isv,K21_isv] = [
    [ 0.2000, 0.0000, 0.0000 ],
    [ 0.0000, 0.2000, 0.0000 ],
    [ 0.0000, 0.0000, 0.2000 ],
]
f[ANOISE_STD] = 5.0000

Starting f[X] values (before fitting)

f[K12] = 0.5000
f[K21] = 0.5000
f[KE] = 0.5000
f[KE_isv,K12_isv,K21_isv] = [
    [ 0.0100, 0.0000, 0.0000 ],
    [ 0.0000, 0.0100, 0.0000 ],
    [ 0.0000, 0.0000, 0.0100 ],
]
f[ANOISE_STD] = 100.0000