• Language: en

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

-834.3309

which required N. iterations and took 204.98 seconds

Final fitted fixed effects

f[KA] = 0.2299
f[CL] = 1.8348
f[V1] = 53.1476
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0177, 0.0129, 0.0214 ],
    [ 0.0129, 0.0140, -0.0088 ],
    [ 0.0214, -0.0088, 0.1722 ],
]
f[PNOISE] = 0.1436
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 Fitted Value Starting Value Prop Change Abs Change
f[KA] 0.2299 1.0000 0.7701 0.7701
f[CL] 1.8348 1.0000 0.8348 0.8348
f[V1] 53.1476 20.0000 1.6574 33.1476

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE] 0.1436 0.1000 0.4358 0.0436

Compare Variance f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[KA_isv] 0.0177 0.0500 0.6459 0.0323
f[KA_isv;CL_isv] 0.0129 0.0100 0.2928 0.0029
f[KA_isv;V1_isv] 0.0214 0.0100 1.1421 0.0114
f[CL_isv;KA_isv] 0.0129 0.0100 0.2928 0.0029
f[CL_isv] 0.0140 0.0500 0.7201 0.0360
f[CL_isv;V1_isv] -0.0088 0.0100 1.8827 0.0188
f[V1_isv;KA_isv] 0.0214 0.0100 1.1421 0.0114
f[V1_isv;CL_isv] -0.0088 0.0100 1.8827 0.0188
f[V1_isv] 0.1722 0.0500 2.4436 0.1222
Back to Top