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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

-829.1270

which required 1.30 iterations and took 246.45 seconds

Final fitted fixed effects

f[KA] = 0.2308
f[CL] = 1.8474
f[V1] = 52.2821
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0000, 0.0002, 0.0004 ],
    [ 0.0002, 0.0399, 0.0067 ],
    [ 0.0004, 0.0067, 0.0987 ],
]
f[PNOISE] = 0.1491
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 0.2308 0.7692 0.7692
f[CL] 1.0000 1.8474 0.8474 0.8474
f[V1] 20.0000 52.2821 1.6141 32.2821

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1491 0.4912 0.0491

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 0.9820 0.0098
f[KA_isv;V1_isv] 0.0100 0.0004 0.9641 0.0096
f[CL_isv;KA_isv] 0.0100 0.0002 0.9820 0.0098
f[CL_isv] 0.0500 0.0399 0.2015 0.0101
f[CL_isv;V1_isv] 0.0100 0.0067 0.3270 0.0033
f[V1_isv;KA_isv] 0.0100 0.0004 0.9641 0.0096
f[V1_isv;CL_isv] 0.0100 0.0067 0.3270 0.0033
f[V1_isv] 0.0500 0.0987 0.9748 0.0487
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