- Language: en
Full matrix generation full matrix fit¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | gen_full_fit_full |
|---|---|
| Title: | Full matrix generation full matrix fit |
| Author: | PoPy for PK/PD |
| Abstract: |
One compartment model with absorption compartment and CL/V parametrisation.
This script uses a full covariance matrix to generate the data and a full covariance matrix to fit.
| Keywords: | dep_one_cmp_cl; one compartment model; full matrix |
|---|---|
| Input Script: | gen_full_fit_full_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 | 0.1309 | 0.1209 | 12.0913 |
| f[CL_isv;V_isv] | 0.0010 | 0.0525 | 0.0515 | 51.4634 |
| f[V_isv;CL_isv] | 0.0010 | 0.0525 | 0.0515 | 51.4634 |
| f[V_isv] | 0.0100 | 0.1657 | 0.1557 | 15.5741 |
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,V_isv] = [
[ 0.1309, 0.0525 ],
[ 0.0525, 0.1657 ],
]
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,V_isv] = [
[ 0.0100, 0.0010 ],
[ 0.0010, 0.0100 ],
]