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Depot + One compartment PK with BLQ

[Generated automatically as a Tutorial summary]

Inputs

Description

Name:blq_pk
Title:Depot + One compartment PK with BLQ
Author:J.R. Hartley
Abstract:
Depot One Comp PK model, with BLQ (below level of quantification) observations.
Keywords:tutorial; pk; advan4; dep_two_cmp; blq
Input Script:blq_pk_tut.pyml
Diagram:

True f[X] values

f[KA] = 0.2000
f[CL] = 2.0000
f[V1] = 50.0000
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.1000, 0.0200, 0.0100 ],
    [ 0.0200, 0.0300, 0.0200 ],
    [ 0.0100, 0.0200, 0.0900 ],
]
f[PNOISE] = 0.1500
f[ANOISE] = 0.0100

Starting f[X] values

f[KA] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0500, 0.0100, 0.0100 ],
    [ 0.0100, 0.0500, 0.0100 ],
    [ 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000
f[ANOISE] = 0.0100

Outputs

Generating and Fitting Summaries

Fitted f[X] values

f[KA] = 0.2224
f[CL] = 1.9543
f[V1] = 51.0073
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0697, 0.0196, 0.0527 ],
    [ 0.0196, 0.0115, 0.0117 ],
    [ 0.0527, 0.0117, 0.1872 ],
]
f[PNOISE] = 0.1473
f[ANOISE] = 0.0100

Plots

Dense comp plots

Alternatively see All dense_comp graph plots

Comparison

True objective value

-725.9095

Final fitted objective value

-727.7713

Compare Main f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA] 1 0.222 0.2 11.20% 2.24e-02
f[CL] 1 1.95 2 2.29% 4.57e-02
f[V1] 20 51 50 2.01% 1.01e+00

Compare Noise f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[PNOISE] 0.1 0.147 0.15 1.77% 2.65e-03

Compare Variance f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA_isv] 0.05 0.0697 0.1 30.27% 3.03e-02
f[KA_isv;CL_isv] 0.01 0.0196 0.02 2.16% 4.32e-04
f[KA_isv;V1_isv] 0.01 0.0527 0.01 427.44% 4.27e-02
f[CL_isv;KA_isv] 0.01 0.0196 0.02 2.16% 4.32e-04
f[CL_isv] 0.05 0.0115 0.03 61.61% 1.85e-02
f[CL_isv;V1_isv] 0.01 0.0117 0.02 41.42% 8.28e-03
f[V1_isv;KA_isv] 0.01 0.0527 0.01 427.44% 4.27e-02
f[V1_isv;CL_isv] 0.01 0.0117 0.02 41.42% 8.28e-03
f[V1_isv] 0.05 0.187 0.09 107.95% 9.72e-02
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