Proportional error model fitted to proportional + additive noise synthetic data.

[Generated automatically as a Fitting summary]

Inputs

Description

Name:pa_gen_po_fit
Title:Proportional error model fitted to proportional + additive noise synthetic data.
Author:Wright Dose Ltd
Abstract:
One compartment model with a depot leading to a central compartment
This model contains both proportional error only. Input data contains proportional and additive noise.
Keywords:one compartment model; dep_one_cmp_cl; proportional error
Input Script:pa_gen_po_fit.pyml
Input Data:synthetic_data.csv
Diagram:

Initial fixed effect estimates

f[PNOISE_STD] = 0.5000

Outputs

Final objective value

-228.0882

which required N. iterations and took 0.52 seconds

Final fitted fixed effects

f[PNOISE_STD] = 0.5223

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] 0.5223 0.5000 0.0446 0.0223

Compare Variance f[X]