• Language: en

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 0.2104 0.7896 0.7896
f[CL] 1.0000 2.0601 1.0601 1.0601
f[V1] 20.0000 47.8386 27.8386 1.3919

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE] 0.1000 0.1513 0.0513 0.5128

Compare Variance f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[KA_isv] 0.0500 0.0597 0.0097 0.1944
f[KA_isv;CL_isv] 0.0100 0.0033 0.0067 0.6730
f[KA_isv;V1_isv] 0.0100 0.0142 0.0042 0.4177
f[CL_isv;KA_isv] 0.0100 0.0033 0.0067 0.6730
f[CL_isv] 0.0500 0.0319 0.0181 0.3614
f[CL_isv;V1_isv] 0.0100 0.0093 0.0007 0.0663
f[V1_isv;KA_isv] 0.0100 0.0142 0.0042 0.4177
f[V1_isv;CL_isv] 0.0100 0.0093 0.0007 0.0663
f[V1_isv] 0.0500 0.1106 0.0606 1.2122

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-701.3736

which required 1.27 iterations and took 1162.59 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2104
f[CL] = 2.0601
f[V1] = 47.8386
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0597, 0.0033, 0.0142 ],
    [ 0.0033, 0.0319, 0.0093 ],
    [ 0.0142, 0.0093, 0.1106 ],
]
f[PNOISE] = 0.1513
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
Back to Top