- Language: en
Diagonal matrix generation diagonal matrix fit using separate univariate normals¶
[Generated automatically as a Fitting 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_fit.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
Compare Noise f[X]¶
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[CL_isv] | 0.0100 | 0.2090 | 0.1990 | 19.8977 |
| f[V_isv] | 0.0100 | 0.0915 | 0.0815 | 8.1530 |
Population simulated (sim) plots¶
|
allOBS_vs_TIME |
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] = 0.2090
f[V_isv] = 0.0915
Fitted parameter .csv files¶
| Fixed Effects: | fx_params.csv (fit) |
|---|---|
| Random Effects: | rx_params.csv (fit) |
| Model params: | mx_params.csv (fit) |
| State values: | sx_params.csv (fit) |
| Predictions: | px_params.csv (fit) |
| Likelihoods: | lx_params.csv (fit) |
Inputs¶
| Input Data: | cx_obs_params.csv |
|---|
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