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Diagonal matrix generation diagonal matrix fit using separate univariate normals

[Generated automatically as a Tutorial summary]

Model Description

Name:gen_indep_fit_indep
Title:Diagonal matrix generation diagonal matrix fit using separate univariate normals
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.
Note here the ‘diagonal matrix’ is implemented as two separate univariate normal distributions, which is equivalent.
Keywords:dep_one_cmp_cl; one compartment model; diagonal matrix
Input Script:gen_indep_fit_indep_tut.pyml
Diagram:

Comparison

True objective value

-2214.8455

Final fitted objective value

-2215.9681

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 0.197 0.2 2.82e-03 1.41%
f[V_isv] 0.01 0.117 0.1 1.74e-02 17.38%

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] = 0.1972
f[V_isv] = 0.1174

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] = 0.2000
f[V_isv] = 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] = 0.0100
f[V_isv] = 0.0100
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