Compute fixed and random effects model matrices in Python
modelframe builds model matrices and response vectors from a given
dataset using lme4-style formulas in Python. For instance, consider the sleepstudy data from
lme4:
from modelframe import model_frame, load_data
sleepstudy = load_data()
sleepstudy## Reaction Days Subject
## 0 249.5600 0 308
## 1 258.7047 1 308
## 2 250.8006 2 308
## 3 321.4398 3 308
## 4 356.8519 4 308
## .. ... ... ...
## 175 329.6076 5 372
## 176 334.4818 6 372
## 177 343.2199 7 372
## 178 369.1417 8 372
## 179 364.1236 9 372
##
## [180 rows x 3 columns]
Computing the model matrices is then as simple as:
frame = model_frame("~ Days + (Days | Subject)", sleepstudy)The fixed effects model matrix:
frame.coef_model_matrix## Intercept Days
## 0 1.0 0
## 1 1.0 1
## 2 1.0 2
## 3 1.0 3
## 4 1.0 4
## .. ... ...
## 175 1.0 5
## 176 1.0 6
## 177 1.0 7
## 178 1.0 8
## 179 1.0 9
##
## [180 rows x 2 columns]
The random effects model matrix:
frame.ranef_model_matrix## Intercept Days Intercept Days ... Intercept Days Intercept Days
## group 308 308 309 309 ... 371 371 372 372
## 0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
## 1 1.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
## 2 1.0 2.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
## 3 1.0 3.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
## 4 1.0 4.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
## .. ... ... ... ... ... ... ... ... ...
## 175 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 5.0
## 176 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 6.0
## 177 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 7.0
## 178 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 8.0
## 179 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 9.0
##
## [180 rows x 36 columns]
You can install modelframe either from PyPI using:
pip install modelframeor using the latest GitHub release:
python -m pip install git+https://github.com/dirmeier/modelframe.gitSimon Dirmeier simon.dirmeier @ gmx.de