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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

-727.7713

which required N. iterations and took 145.15 seconds

Final fitted fixed effects

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

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 Fitted Value Starting Value Prop Change Abs Change
f[KA] 0.2224 1.0000 0.7776 0.7776
f[CL] 1.9543 1.0000 0.9543 0.9543
f[V1] 51.0073 20.0000 1.5504 31.0073

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.1473 0.1000 0.4735 0.0473

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.0697 0.0500 0.3947 0.0197
f[KA_isv;CL_isv] 0.0196 0.0100 0.9568 0.0096
f[KA_isv;V1_isv] 0.0527 0.0100 4.2744 0.0427
f[CL_isv;KA_isv] 0.0196 0.0100 0.9568 0.0096
f[CL_isv] 0.0115 0.0500 0.7697 0.0385
f[CL_isv;V1_isv] 0.0117 0.0100 0.1715 0.0017
f[V1_isv;KA_isv] 0.0527 0.0100 4.2744 0.0427
f[V1_isv;CL_isv] 0.0117 0.0100 0.1715 0.0017
f[V1_isv] 0.1872 0.0500 2.7431 0.1372
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