Language: en

Diagonal matrix generation diagonal matrix fit¶

[Generated automatically as a Tutorial summary]

Inputs¶

Description¶

Name:	gen_diag_fit_diag
Title:	Diagonal matrix generation diagonal matrix fit
Author:	Wright Dose Ltd
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:

True f[X] values¶

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¶

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 ],
]

Outputs¶

Generated data .csv file¶

Synthetic Data:	`synthetic_data.csv`

Generating and Fitting Summaries¶

Gen: Diagonal matrix generation diagonal matrix fit (gen)
Fit: Diagonal matrix generation diagonal matrix fit (fit)

Fitted f[X] values¶

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] = [
    [ 0.1977, 0.0000 ],
    [ 0.0000, 0.1167 ],
]

Plots¶

Dense comp plots¶

Alternatively see All dense_comp graph plots

Comparison¶

True objective value¶

-2214.8455

Final fitted objective value¶

-2215.9658

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	Prop. Error	Abs. Error
f[CL_isv]	0.01	0.198	0.2	1.14%	2.28e-03
f[CL_isv;V_isv]	0	0	0	inf	0.00e+00
f[V_isv;CL_isv]	0	0	0	inf	0.00e+00
f[V_isv]	0.01	0.117	0.1	16.70%	1.67e-02

Documentation