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
0.21153	a	0
0.50351	a	0
2.3472	b	0
-0.74684	b	0
0.43753	c	0
0.14149	c	0

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

ds.head()

y	a	z
0.21153	a	0
0.50351	a	0
2.3472	b	0
-0.74684	b	0
0.43753	c	0
0.14149	c	0

… or a summary of variables:

ds.summary()

Key	Type	Values
y	Var	-0.746844, 0.141487, 0.211534, 0.437533, 0.503505, 2.34721
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	-0.12166	a
S1	1.8879	b
S2	-0.25106	c
S3	0.18723	a
S4	0.5883	b
S5	-1.1111	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	0.71708
a	-0.20205
a	0.67982
a	-0.047205
a	0.75833
a	0.13167
a	-0.05084
b	0.26379
b	0.32878
b	0.74503
b	0.16783
b	1.3955
b	1.8905
b	-1.1074
c	2.7768
c	1.3085
c	1.5854
c	2.6146
c	1.6215
c	3.2714
c	3.7174

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	19.03	2	9.52	14.56***	< .001
Residuals	11.76	18	0.65
Total	30.80	20

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

Pairwise T-Tests (independent samples)

	b	c
a	t12 = -0.61 p = .553 pc = .553	t12 = -5.55*** p < .001 pc < .001
b		t12 = -3.74** p = .003 pc = .009

(* 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 & $t_{12} = -0.61^{   \ \ \ }$\\
$p = .553$\\
$p_{c} = .553$ & $t_{12} = -5.55^{***}$\\
$p < .001$\\
$p_{c} < .001$ \\
b &  & $t_{12} = -3.74^{** \ }$\\
$p = .003$\\
$p_{c} = .009$ \\
\bottomrule
\end{tabular}
\end{center}

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