- Language: en
Body Weight Covariate¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | weight_covariate |
|---|---|
| Title: | Body Weight Covariate |
| Author: | PoPy for PK/PD |
| Abstract: |
One compartment model with absorption compartment and CL/V parametrisation.
There are no random effects here. Each individual just has a different weight.
The weight is a covariate for the m[CL] clearance parameter for each individual.
Only the f[WT_EFFECT] and f[V] fixed effect parameters are estimated, other f[X] are fixed.
| Keywords: | one compartment model; dep_one_cmp_cl; weight; covariate effect |
|---|---|
| Input Script: | weight_covariate_fit.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[V] | 15.0000 | 20.2610 | 5.2610 | 0.3507 |
| f[WT_EFFECT] | 1.0000 | 0.6657 | 0.3343 | 0.3343 |
Compare Noise f[X]¶
Compare Variance f[X]¶
Population simulated (sim) plots¶
|
allOBS_vs_TIME |
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.2610
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 0.6657
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] = 15.0000
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 1.0000