- Language: en
Diagonal matrix generation diagonal matrix fit using separate univariate normals¶
[Generated automatically as a Tutorial summary]
Inputs¶
Description¶
| Name: | gen_indep_fit_indep |
|---|---|
| Title: | Diagonal matrix generation diagonal matrix fit using separate univariate normals |
| 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.
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: |
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] = 0.2000
f[V_isv] = 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] = 0.0100
f[V_isv] = 0.0100
Outputs¶
Generating and Fitting Summaries¶
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] = 0.1750
f[V_isv] = 0.0873
Plots¶
Comparison¶
True objective value¶
-2170.8718
Final fitted objective value¶
-2172.7667
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 | 12.49% | 2.50e-02 |
| f[V_isv] | 0.01 | 0.0873 | 0.1 | 12.71% | 1.27e-02 |