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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] = 0.2185
f[CL] = 1.9944
f[V1] = 50.4071
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0203, 0.0106, 0.0251 ],
    [ 0.0106, 0.0126, 0.0145 ],
    [ 0.0251, 0.0145, 0.1816 ],
]
f[PNOISE] = 0.1509
f[ANOISE] = 0.0100

Plots

Dense comp plots

Alternatively see All dense_comp graph plots

Comparison

True objective value

-725.9090

Final fitted objective value

-732.3632

Compare Main f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA] 1 0.219 0.2 9.26% 1.85e-02
f[CL] 1 1.99 2 0.28% 5.56e-03
f[V1] 20 50.4 50 0.81% 4.07e-01

Compare Noise f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[PNOISE] 0.1 0.151 0.15 0.59% 8.91e-04

Compare Variance f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA_isv] 0.05 0.0203 0.1 79.70% 7.97e-02
f[KA_isv;CL_isv] 0.01 0.0106 0.02 46.80% 9.36e-03
f[KA_isv;V1_isv] 0.01 0.0251 0.01 151.35% 1.51e-02
f[CL_isv;KA_isv] 0.01 0.0106 0.02 46.80% 9.36e-03
f[CL_isv] 0.05 0.0126 0.03 58.11% 1.74e-02
f[CL_isv;V1_isv] 0.01 0.0145 0.02 27.70% 5.54e-03
f[V1_isv;KA_isv] 0.01 0.0251 0.01 151.35% 1.51e-02
f[V1_isv;CL_isv] 0.01 0.0145 0.02 27.70% 5.54e-03
f[V1_isv] 0.05 0.182 0.09 101.77% 9.16e-02
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