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] = 0.5
f[ANOISE_STD] = 0.25

Outputs

Final objective value

-572.803857763

which required N. iterations and took 184.74 seconds

Final fitted fixed effects

f[PNOISE_STD] = 0.092518
f[ANOISE_STD] = 0.001

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]	0.092518	0.5	0.814964	0.407482
f[ANOISE_STD]	0.001	0.25	0.996	0.249

Compare Variance f[X]