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

[Generated automatically as a Fitting summary]

Model Description

Name:

pa_gen_po_fit

Title:

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

Author:

PoPy for PK/PD

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

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	0.5223	0.0223	0.0446

Compare Variance f[X]

Population observed (fit) plots

indOBS_vs_TIME

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

-228.0882

which required 1.4 iterations and took 10.42 seconds

Fitted f[X] values (after fitting)

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)

Likelihoods:

lx_params.csv (fit)

Inputs

Input Data:

synthetic_data.csv

Starting f[X] values (before fitting)

f[PNOISE_STD] = 0.5000