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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] = 0.1950
f[CL] = 2.0509
f[V1] = 48.4223
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0137, 0.0118, -0.0107 ],
    [ 0.0118, 0.0221, 0.0270 ],
    [ -0.0107, 0.0270, 0.1372 ],
]
f[PNOISE] = 0.1486
f[ANOISE] = 0.0100

Plots

Dense comp plots

Alternatively see All dense_comp graph plots

Comparison

True objective value

-725.8063

Final fitted objective value

-740.9643

Compare Main f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA] 1 0.195 0.2 2.50% 5.00e-03
f[CL] 1 2.05 2 2.54% 5.09e-02
f[V1] 20 48.4 50 3.16% 1.58e+00

Compare Noise f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[PNOISE] 0.1 0.149 0.15 0.93% 1.40e-03

Compare Variance f[X]

Name Initial Fitted True Prop. Error Abs. Error
f[KA_isv] 0.05 0.0137 0.1 86.29% 8.63e-02
f[KA_isv;CL_isv] 0.01 0.0118 0.02 40.98% 8.20e-03
f[KA_isv;V1_isv] 0.01 -0.0107 0.01 207.29% 2.07e-02
f[CL_isv;KA_isv] 0.01 0.0118 0.02 40.98% 8.20e-03
f[CL_isv] 0.05 0.0221 0.03 26.29% 7.89e-03
f[CL_isv;V1_isv] 0.01 0.027 0.02 34.95% 6.99e-03
f[V1_isv;KA_isv] 0.01 -0.0107 0.01 207.29% 2.07e-02
f[V1_isv;CL_isv] 0.01 0.027 0.02 34.95% 6.99e-03
f[V1_isv] 0.05 0.137 0.09 52.42% 4.72e-02
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