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Model containing both proportional and additive error

[Generated automatically as a Fitting summary]

Model Description

Name:pa_gen_pa_fit
Title:Model containing both proportional and additive error
Author:PoPy for PK/PD
Abstract:
One compartment model with a depot leading to a central compartment.
This model contains both proportional and additive error.
Keywords:one compartment model; one_two_cmp_cl; proportional and additive error
Input Script:pa_gen_pa_fit_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[PNOISE_STD] 0.5000 0.0951 0.4049 0.8098
f[ANOISE_STD] 0.2500 0.0453 0.2047 0.8187

Compare Variance f[X]

Population simulated (sim) plots

allOBS_vs_TIME
CWRES(DV_CENTRAL)_vs_IPRED(DV_CENTRAL)
indOBS_vs_TIME
IRES(DV_CENTRAL)_vs_IPRED(DV_CENTRAL)
WRES(DV_CENTRAL)_vs_PPRED(DV_CENTRAL)

Outputs

Final objective value

-396.6598

which required 1.6 iterations and took 0.67 seconds

Fitted f[X] values (after fitting)

f[PNOISE_STD] = 0.0951
f[ANOISE_STD] = 0.0453

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[PNOISE_STD] = 0.5000
f[ANOISE_STD] = 0.2500
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