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

[Generated automatically as a Fitting summary]

Model Description

Name:

ao_gen_pa_fit

Title:

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

Author:

PoPy for PK/PD

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 additive noise and no proportional error.

Keywords:

one compartment model; dep_one_cmp_cl; proportional and additive error

Input Script:

ao_gen_pa_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE_STD]	0.5000	0.0010	0.4990	0.9980
f[ANOISE_STD]	0.2500	0.0464	0.2036	0.8145

Compare Variance f[X]

Population observed (fit) plots

indOBS_vs_TIME

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

-514.1872

which required 1.19 iterations and took 11.15 seconds

Fitted f[X] values (after fitting)

f[PNOISE_STD] = 0.0010
f[ANOISE_STD] = 0.0464

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)

Likelihoods:

lx_params.csv (fit)

Inputs

Input Data:

synthetic_data.csv

Starting f[X] values (before fitting)

f[PNOISE_STD] = 0.5000
f[ANOISE_STD] = 0.2500