Dataset basics

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

A dataset can be constructed column by column, by adding one variable after another:

# initialize an empty Dataset:
ds = Dataset()
# numeric values are added as Var object:
ds['y'] = Var(numpy.random.normal(0, 1, 6))
# categorical data as represented in Factors:
ds['a'] = Factor(['a', 'b', 'c'], repeat=2)
# A variable that's equal in all cases can be assigned quickly:
ds[:, 'z'] = 0.
# check the result:
ds

y	a	z
-0.79831	a	0
1.3289	a	0
-1.1348	b	0
-0.61174	b	0
0.87851	c	0
-0.78987	c	0

For larger datasets it can be more convenient to print only the first few cases…

ds.head()

y	a	z
-0.79831	a	0
1.3289	a	0
-1.1348	b	0
-0.61174	b	0
0.87851	c	0
-0.78987	c	0

… or a summary of variables:

ds.summary()

Key	Type	Values
y	Var	-1.13478, -0.798306, -0.789865, -0.611745, 0.878511, 1.32892
a	Factor	a:2, b:2, c:2
z	Var	0:6

Dataset: 6 cases

An alternative way of constructing a dataset is case by case (i.e., row by row):

rows = []
for i in range(6):
    subject = f'S{i}'
    y = numpy.random.normal(0, 1)
    a = 'abc'[i % 3]
    rows.append([subject, y, a])
ds = Dataset.from_caselist(['subject', 'y', 'a'], rows, random='subject')
ds

subject	y	a
S0	-0.073693	a
S1	0.057158	b
S2	0.42812	c
S3	0.88345	a
S4	-0.69126	b
S5	-0.76437	c

Example

Below is a simple example using data objects (for more, see the Examples):

y = numpy.empty(21)
y[:14] = numpy.random.normal(0, 1, 14)
y[14:] = numpy.random.normal(2, 1, 7)
ds = Dataset({
    'a': Factor('abc', 'A', repeat=7),
    'y': Var(y, 'Y'),
})
ds

a	y
a	-0.91354
a	0.13757
a	-0.80733
a	-1.7571
a	-0.27913
a	-2.2453
a	-1.1887
b	0.37502
b	-1.0636
b	-1.2591
b	0.19883
b	1.1008
b	-0.42098
b	-1.2403
c	2.359
c	1.718
c	2.7439
c	1.513
c	3.6197
c	2.0336
c	3.3641

table.frequencies('a', data=ds)

a	n
a	7
b	7
c	7

test.ANOVA('y', 'a', data=ds)

	SS	df	MS	F	p
a	47.84	2	23.92	33.22***	< .001
Residuals	12.96	18	0.72
Total	60.80	20

test.pairwise('y', 'a', data=ds, corr='Hochberg')

Pairwise T-Tests (independent samples)

	b	c
a	t12 = -1.46 p = .171 pc = .171	t12 = -8.04*** p < .001 pc < .001
b		t12 = -6.09*** p < .001 pc < .001

(* Corrected after Hochberg, 1988)

t = test.pairwise('y', 'a', data=ds, corr='Hochberg')
print(t.get_tex())

\begin{center}
\begin{tabular}{lll}
\toprule
 & b & c \\
\midrule
a & $t_{12} = -1.46^{   \ \ \ }$\\
$p = .171$\\
$p_{c} = .171$ & $t_{12} = -8.04^{***}$\\
$p < .001$\\
$p_{c} < .001$ \\
b &  & $t_{12} = -6.09^{***}$\\
$p < .001$\\
$p_{c} < .001$ \\
\bottomrule
\end{tabular}
\end{center}

p = plot.Boxplot('y', 'a', data=ds, title="My Boxplot", ylabel="value", corr='Hochberg')