- Language: en
Body Weight Covariate¶
[Generated automatically as a Fitting summary]
Inputs¶
Description¶
| Name: | weight_covariate |
|---|---|
| Title: | Body Weight Covariate |
| Author: | Wright Dose Ltd |
| 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 |
| Input Data: | synthetic_data.csv |
| Diagram: |
Initial fixed effect estimates¶
f[KA] = 0.3
f[CL] = 3
f[V] = 15
f[PNOISE] = 0.1
f[ANOISE] = 0.05
f[WT_EFFECT] = 1
Outputs¶
Final fitted fixed effects¶
f[KA] = 0.3
f[CL] = 3
f[V] = 20.261
f[PNOISE] = 0.1
f[ANOISE] = 0.05
f[WT_EFFECT] = 0.66554
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) |
