Model coding

Illustrates how to inspect coding of regression models.

# Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
from eelbrain import *

ds = Dataset()
ds['A'] = Factor(['a1', 'a0'], repeat=4)
ds['B'] = Factor(['b1', 'b0'], repeat=2, tile=2)

look at data

ds.head()

Out:

A    B
-------
a1   b1
a1   b1
a1   b0
a1   b0
a0   b1
a0   b1
a0   b0
a0   b0

create a fixed effects model

m = ds.eval('A * B')
print(repr(m))

Out:

A + B + A % B

show the model coding

print(m)

Out:

intercept   A   B   A x B
-------------------------
1           0   0   0
1           0   0   0
1           0   1   0
1           0   1   0
1           1   0   0
1           1   0   0
1           1   1   1
1           1   1   1

create random effects model

ds['subject'] = Factor(['s1', 's2'], tile=4, name='subject', random=True)
m = ds.eval('A * B * subject')
print(repr(m))

Out:

A + B + A % B + subject + A % subject + B % subject + A % B % subject

show the model coding

print(m)

Out:

intercept   A   B   A x B   subject   A x subject   B x subject   A x B x subject
---------------------------------------------------------------------------------
1           0   0   0       1         0             0             0
1           0   0   0       0         0             0             0
1           0   1   0       1         0             1             0
1           0   1   0       0         0             0             0
1           1   0   0       1         1             0             0
1           1   0   0       0         0             0             0
1           1   1   1       1         1             1             1
1           1   1   1       0         0             0             0