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 |
|---|
Gen and Fit Summaries¶
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