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Depot + One compartment PK with BLQ

[Generated automatically as a Fitting summary]

Model Description

Name:blq_pk
Title:Depot + One compartment PK with BLQ
Author:PoPy for PK/PD
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
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 1.0000 0.1955 0.8045 0.8045
f[CL] 1.0000 2.0248 1.0248 1.0248
f[V1] 20.0000 46.9689 26.9689 1.3484

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.1389 0.0389 0.3888

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 0.1374 0.0874 1.7471
f[KA_isv;CL_isv] 0.0100 0.0156 0.0056 0.5555
f[KA_isv;V1_isv] 0.0100 0.0621 0.0521 5.2097
f[CL_isv;KA_isv] 0.0100 0.0156 0.0056 0.5555
f[CL_isv] 0.0500 0.0392 0.0108 0.2169
f[CL_isv;V1_isv] 0.0100 0.0199 0.0099 0.9866
f[V1_isv;KA_isv] 0.0100 0.0621 0.0521 5.2097
f[V1_isv;CL_isv] 0.0100 0.0199 0.0099 0.9866
f[V1_isv] 0.0500 0.1255 0.0755 1.5110

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-763.9922

which required 1.30 iterations and took 2430.83 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1955
f[CL] = 2.0248
f[V1] = 46.9689
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.1374, 0.0156, 0.0621 ],
    [ 0.0156, 0.0392, 0.0199 ],
    [ 0.0621, 0.0199, 0.1255 ],
]
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)
Likelihoods:lx_params.csv (fit)

Inputs

Input Data:cx_obs_params.csv

Starting f[X] values (before fitting)

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
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