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] = 0.5000
f[ANOISE_STD] = 0.2500

Outputs

Final objective value

-396.7510

which required 5 iterations and took 0.27 seconds

Final fitted fixed effects

f[PNOISE_STD] = 0.0699
f[ANOISE_STD] = 0.0400

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]	0.0699	0.5000	0.8603	0.4301
f[ANOISE_STD]	0.0400	0.2500	0.8398	0.2100

Compare Variance f[X]