Proportional and Additive error model fitted to proportional noise only synthetic data.

[Generated automatically as a Fitting summary]

Inputs

Description

Name:po_gen_pa_fit
Title:Proportional and Additive error model fitted to proportional noise only synthetic data.
Author:Wright Dose Ltd
Abstract:
One compartment model with a depot leading to a central compartment.
This model contains both proportional and additive error. The synthetic input data contains only proportional error, no additive error.
Keywords:one compartment model; dep_one_cmp_cl; proportional and additive error
Input Script:po_gen_pa_fit.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

-572.8039

which required N. iterations and took 0.62 seconds

Final fitted fixed effects

f[PNOISE_STD] = 0.0925
f[ANOISE_STD] = 0.0010

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] 0.0925 0.5000 0.8150 0.4075
f[ANOISE_STD] 0.0010 0.2500 0.9960 0.2490

Compare Variance f[X]