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:
- Diagram:
Comparison
True objective value
-2170.8804
Final fitted objective value
-2172.8028
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 |
0.178 |
0.2 |
2.16e-02 |
10.79% |
f[V_isv] |
0.01 |
0.0881 |
0.1 |
1.19e-02 |
11.91% |
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] = 0.1784
f[V_isv] = 0.0881
Generated data .csv file
- Synthetic Data:
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