Model containing both proportional and additive error¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | pa_gen_pa_fit |
|---|---|
| Title: | Model containing both proportional and additive error |
| Author: | PoPy for PK/PD |
| Abstract: |
One compartment model with a depot leading to a central compartment.
This model contains both proportional and additive error.
| Keywords: | one compartment model; one_two_cmp_cl; proportional and additive error |
|---|---|
| Input Script: | pa_gen_pa_fit_fit.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE_STD] | 0.5000 | 0.0951 | 0.4049 | 0.8098 |
| f[ANOISE_STD] | 0.2500 | 0.0453 | 0.2047 | 0.8187 |
Compare Variance f[X]¶
Population simulated (sim) plots¶
|
allOBS_vs_TIME |
|
CWRES(DV_CENTRAL)_vs_IPRED(DV_CENTRAL) |
|
indOBS_vs_TIME |
|
IRES(DV_CENTRAL)_vs_IPRED(DV_CENTRAL) |
|
WRES(DV_CENTRAL)_vs_PPRED(DV_CENTRAL) |
Outputs¶
Fitted f[X] values (after fitting)¶
f[PNOISE_STD] = 0.0951
f[ANOISE_STD] = 0.0453
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[PNOISE_STD] = 0.5000
f[ANOISE_STD] = 0.2500