- 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 |
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Input Script: | gen_diag_fit_diag_tut.pyml |
Diagram: |
Failed to create compartment diagram
True f[X] values¶
f[KA] = 0.3
f[CL] = 3
f[V] = 20
f[PNOISE_STD] = 0.1
f[ANOISE_STD] = 0.05
f[CL_isv,V_isv] = [
[ 0.2, 0 ],
[ 0, 0.1 ]
]
Starting f[X] values¶
f[KA] = 0.3
f[CL] = 3
f[V] = 20
f[PNOISE_STD] = 0.1
f[ANOISE_STD] = 0.05
f[CL_isv,V_isv] = [
[ 0.01, 0 ],
[ 0, 0.01 ]
]
Outputs¶
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] = 0.3
f[CL] = 3
f[V] = 20
f[PNOISE_STD] = 0.1
f[ANOISE_STD] = 0.05
f[CL_isv,V_isv] = [
[ 0.175, 0 ],
[ 0, 0.087425 ]
]
Plots¶
Comparison¶
True objective value¶
-2170.87181473
Final fitted objective value¶
-2172.76457339
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 | 0.175 | 0.2 | 12.50% | 2.50e-02 |
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 | 0.0874 | 0.1 | 12.57% | 1.26e-02 |