Full matrix generation full matrix fit

[Generated automatically as a Tutorial summary]

Model Description

Name:: gen_full_fit_full
Title:: Full matrix generation full matrix fit
Author:: PoPy for PK/PD
Abstract:

One compartment model with absorption compartment and CL/V parametrisation.

This script uses a full covariance matrix to generate the data and a full covariance matrix to fit.

Keywords:: dep_one_cmp_cl; one compartment model; full matrix
Input Script:: gen_full_fit_full_tut.pyml
Diagram:

Comparison

True objective value

-2106.7004

Final fitted objective value

-2108.2792

Compare Main f[X]

No Main f[X] values to compare.

Compare Noise f[X]

No Noise f[X] values to compare.

Compare Variance f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[CL_isv]	0.01	0.136	0.15	1.39e-02	9.27%
f[CL_isv;V_isv]	0.001	0.05…	0.05	3.63e-06	0.01%
f[V_isv;CL_isv]	0.001	0.05…	0.05	3.63e-06	0.01%
f[V_isv]	0.01	0.166	0.15	1.60e-02	10.66%

Outputs

Fitted f[X] values (after fitting)

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
    [ 0.1361, 0.0500 ],
    [ 0.0500, 0.1660 ],
]

Generated data .csv file

Synthetic Data:: synthetic_data.csv

Gen and Fit Summaries

Gen: Full matrix generation full matrix fit (gen)
Fit: Full matrix generation full matrix fit (fit)

Inputs

True f[X] values (for simulation)

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
    [ 0.1500, 0.0500 ],
    [ 0.0500, 0.1500 ],
]

Starting f[X] values (before fitting)

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
    [ 0.0100, 0.0010 ],
    [ 0.0010, 0.0100 ],
]