- Language: en
Simple Tut Example¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | tut_example1 |
|---|---|
| Title: | Simple Tut Example |
| Author: | PoPy for PK/PD |
| Abstract: |
One compartment model with elimination rate constant KE.
| Keywords: | one compartment model; iv_one_cmp_k |
|---|---|
| Input Script: | tut_example1_fit.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KE] | 0.0500 | 0.0941 | 0.0441 | 0.8815 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.0521 | 0.0479 | 0.4788 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KE_isv] | 0.1000 | 0.0128 | 0.0872 | 0.8719 |
Population simulated (sim) plots¶
|
allOBS(DV_CENTRAL)_vs_IPRED(CEN) |
|
allOBS(DV_CENTRAL)_vs_PRED(CEN) |
|
allOBS_vs_TIME |
|
CWRES(DV_CENTRAL)_vs_IPRED(CEN) |
|
IRES(DV_CENTRAL)_vs_IPRED(CEN) |
|
IWRES(DV_CENTRAL)_vs_IPRED(CEN) |
|
WRES(DV_CENTRAL)_vs_PPRED(CEN) |
Outputs¶
Fitted f[X] values (after fitting)¶
f[KE] = 0.0941
f[PNOISE] = 0.0521
f[KE_isv] = 0.0128
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[KE] = 0.0500
f[PNOISE] = 0.1000
f[KE_isv] = 0.1000