- Language: en
Diagonal matrix generation full matrix fit¶
[Generated automatically as a Tutorial summary]
Inputs¶
Description¶
Name: | gen_diag_fit_full |
---|---|
Title: | Diagonal matrix generation full 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 full covariance matrix to fit.
Keywords: | dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix |
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Input Script: | gen_diag_fit_full_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, 0.0001 ],
[ 0.0001, 0.0100 ],
]
Outputs¶
Generated data .csv file¶
Synthetic Data: | synthetic_data.csv |
---|
Generating and Fitting Summaries¶
- Gen: Diagonal matrix generation full matrix fit (gen)
- Fit: Diagonal matrix generation full 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] = [
[ 0.1928, -0.0166 ],
[ -0.0166, 0.1155 ],
]
Plots¶
Comparison¶
True objective value¶
-2214.8455
Final fitted objective value¶
-2217.2438
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 | 0.193 | 0.2 | 3.58% | 7.17e-03 |
f[CL_isv;V_isv] | 0.0001 | -0.0166 | 0 | inf | 1.66e-02 |
f[V_isv;CL_isv] | 0.0001 | -0.0166 | 0 | inf | 1.66e-02 |
f[V_isv] | 0.01 | 0.116 | 0.1 | 15.50% | 1.55e-02 |