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Diagonal matrix generation diagonal matrix fit

[Generated automatically as a Tutorial summary]

Model Description

Name:gen_diag_fit_diag
Title:Diagonal matrix generation diagonal matrix fit
Author:PoPy for PK/PD
Abstract:
One compartment model with absorption compartment and CL/V parametrisation.
This script uses a diagonal covariance matrix to generate the data and a diagonal covariance matrix to fit.
Keywords:one compartment model; dep_one_cmp_cl; diagonal matrix
Input Script:gen_diag_fit_diag_tut.pyml
Diagram:

Comparison

True objective value

-2183.0504

Final fitted objective value

-2183.6185

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 0.209 0.2 9.23e-03 4.62%
f[CL_isv;V_isv] 0 0 0 0.00e+00 inf
f[V_isv;CL_isv] 0 0 0 0.00e+00 inf
f[V_isv] 0.01 0.0909 0.1 9.08e-03 9.08%

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] = [
    [ 0.2092, 0.0000 ],
    [ 0.0000, 0.0909 ],
]

Generated data .csv file

Synthetic Data:synthetic_data.csv

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] = [
    [ 0.2000, 0.0000 ],
    [ 0.0000, 0.1000 ],
]

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, 0.0000 ],
    [ 0.0000, 0.0100 ],
]
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