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Depot One Comp PK ignoring BLQ observations.

[Generated automatically as a Fitting summary]

Inputs

Description

Name:blq_pk_norm_fit_ignore
Title:Depot One Comp PK ignoring BLQ observations.
Author:J.R. Hartley
Abstract:
Depot One Comp PK model, with BLQ (below level of quantification)
observations removed from data set.
Keywords:tutorial; pk; advan4; dep_two_cmp; blq
Input Script:blq_pk_norm_fit_ignore.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

-829.1239

which required 1.30 iterations and took 320.00 seconds

Final fitted fixed effects

f[KA] = 0.2310
f[CL] = 1.8489
f[V1] = 52.3484
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0000, 0.0002, 0.0004 ],
    [ 0.0002, 0.0400, 0.0068 ],
    [ 0.0004, 0.0068, 0.0991 ],
]
f[PNOISE] = 0.1494
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 0.2310 0.7690 0.7690
f[CL] 1.0000 1.8489 0.8489 0.8489
f[V1] 20.0000 52.3484 1.6174 32.3484

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1494 0.4937 0.0494

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.0000 0.9999 0.0500
f[KA_isv;CL_isv] 0.0100 0.0002 0.9831 0.0098
f[KA_isv;V1_isv] 0.0100 0.0004 0.9641 0.0096
f[CL_isv;KA_isv] 0.0100 0.0002 0.9831 0.0098
f[CL_isv] 0.0500 0.0400 0.2002 0.0100
f[CL_isv;V1_isv] 0.0100 0.0068 0.3226 0.0032
f[V1_isv;KA_isv] 0.0100 0.0004 0.9641 0.0096
f[V1_isv;CL_isv] 0.0100 0.0068 0.3226 0.0032
f[V1_isv] 0.0500 0.0991 0.9823 0.0491
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