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Depot One Comp PK with BLQ observations set to LLQ

[Generated automatically as a Fitting summary]

Model Description

Name:blq_pk_norm_fit
Title:Depot One Comp PK with BLQ observations set to LLQ
Author:PoPy for PK/PD
Abstract:
Depot One Comp PK model, with BLQ (below level of quantification)
observations set to LLQ (lower limit of quantification).
Keywords:tutorial; pk; advan4; dep_two_cmp; blq
Input Script:blq_pk_norm_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA] 1.0000 2.5908 1.5908 1.5908
f[CL] 1.0000 0.9559 0.0441 0.0441
f[V1] 20.0000 86.1210 66.1210 3.3060

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.2288 0.1288 1.2877

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 1.1061 1.0561 21.1229
f[KA_isv;CL_isv] 0.0100 -0.0133 0.0233 2.3297
f[KA_isv;V1_isv] 0.0100 0.2332 0.2232 22.3169
f[CL_isv;KA_isv] 0.0100 -0.0133 0.0233 2.3297
f[CL_isv] 0.0500 0.0002 0.0498 0.9967
f[CL_isv;V1_isv] 0.0100 -0.0028 0.0128 1.2814
f[V1_isv;KA_isv] 0.0100 0.2332 0.2232 22.3169
f[V1_isv;CL_isv] 0.0100 -0.0028 0.0128 1.2814
f[V1_isv] 0.0500 0.0506 0.0006 0.0113

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

121987.0288

which required 1.30 iterations and took 691.60 seconds

Fitted f[X] values (after fitting)

f[KA] = 2.5908
f[CL] = 0.9559
f[V1] = 86.1210
f[KA_isv,CL_isv,V1_isv] = [
    [ 1.1061, -0.0133, 0.2332 ],
    [ -0.0133, 0.0002, -0.0028 ],
    [ 0.2332, -0.0028, 0.0506 ],
]
f[PNOISE] = 0.2288
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:synthetic_data.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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