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Mixed error model fitted to mixed error data, but with incorrect variance definition

[Generated automatically as a Fitting summary]

Inputs

Description

Name:pa_gen_pa_fit_badvar
Title:Mixed error model fitted to mixed error data, but with incorrect variance definition
Author:Wright Dose Ltd
Abstract:
One compartment model with a depot leading to a central compartment
This model contains both proportional and additive error, but erroneously sums the standard deviations.
Keywords:one compartment model; dep_one_cmp_cl; proportional and additive error
Input Script:pa_gen_pa_fit_badvar.pyml
Input Data:synthetic_data.csv
Diagram:

Initial fixed effect estimates

f[PNOISE_STD] = 0.5
f[ANOISE_STD] = 0.25

Outputs

Final objective value

-396.750814736

which required N. iterations and took 230.81 seconds

Final fitted fixed effects

f[PNOISE_STD] = 0.069974
f[ANOISE_STD] = 0.040035

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]

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PNOISE_STD] 0.0699739 0.5 0.860052 0.430026
f[ANOISE_STD] 0.0400346 0.25 0.839862 0.209965

Compare Variance f[X]

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