Mixed error model fitted to mixed error data, but with incorrect variance definition

[Generated automatically as a Fitting summary]

Model Description

Name:

pa_gen_pa_fit_badvar

Title:

Mixed error model fitted to mixed error data, but with incorrect variance definition

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, but erroneously sums the standard deviations.

Keywords:

one compartment model; dep_one_cmp_cl; proportional and additive error

Input Script:

pa_gen_pa_fit_badvar.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	0.0699	0.4301	0.8603
f[ANOISE_STD]	0.2500	0.0400	0.2100	0.8399

Compare Variance f[X]

Population observed (fit) plots

indOBS_vs_TIME

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

-396.7510

which required 1.9 iterations and took 10.58 seconds

Fitted f[X] values (after fitting)

f[PNOISE_STD] = 0.0699
f[ANOISE_STD] = 0.0400

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