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Depot + One compartment PK with BLQ

[Generated automatically as a Fitting 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_fit.pyml
Input Data:synthetic_data.csv
Diagram:

Initial fixed effect estimates

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

Final objective value

-763.9910

which required 1.29 iterations and took 1422.02 seconds

Final fitted fixed effects

f[KA] = 0.1957
f[CL] = 2.0245
f[V1] = 47.0079
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.1365, 0.0159, 0.0617 ],
    [ 0.0159, 0.0390, 0.0200 ],
    [ 0.0617, 0.0200, 0.1256 ],
]
f[PNOISE] = 0.1389
f[ANOISE] = 0.0100

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)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA] 1.0000 0.1957 0.8043 0.8043
f[CL] 1.0000 2.0245 1.0245 1.0245
f[V1] 20.0000 47.0079 1.3504 27.0079

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1389 0.3893 0.0389

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.1365 1.7292 0.0865
f[KA_isv;CL_isv] 0.0100 0.0159 0.5945 0.0059
f[KA_isv;V1_isv] 0.0100 0.0617 5.1652 0.0517
f[CL_isv;KA_isv] 0.0100 0.0159 0.5945 0.0059
f[CL_isv] 0.0500 0.0390 0.2190 0.0110
f[CL_isv;V1_isv] 0.0100 0.0200 1.0026 0.0100
f[V1_isv;KA_isv] 0.0100 0.0617 5.1652 0.0517
f[V1_isv;CL_isv] 0.0100 0.0200 1.0026 0.0100
f[V1_isv] 0.0500 0.1256 1.5118 0.0756
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