def test2(self):
R = """Anova: Single Factor on SUPPRESSION
SUMMARY
Groups Count Sum Average Variance
==============================================
AA 128 2048 16 148.792
AB 128 2510.600 19.614 250.326
LAB 128 2945.000 23.008 264.699
O'BRIEN TEST FOR HOMOGENEITY OF VARIANCE
Source of Variation SS df MS F P-value eta^2 Obs. power
============================================================================================
Treatments 1021873.960 2 510936.980 5.229 0.006 0.027 0.823
Error 37227154.824 381 97709.068
============================================================================================
Total 38249028.783 383
ANOVA
Source of Variation SS df MS F P-value eta^2 Obs. power
=========================================================================================
Treatments 3144.039 2 1572.020 7.104 9.348e-04 0.036 0.922
Error 84304.687 381 221.272
=========================================================================================
Total 87448.726 383
POSTHOC MULTIPLE COMPARISONS
Tukey HSD: Table of q-statistics
AA AB LAB
==============================
AA 0 2.749 ns 5.330 **
AB 0 2.581 ns
LAB 0
==============================
+ p < .10 (q-critical[3, 381] = 2.91125483514)
* p < .05 (q-critical[3, 381] = 3.32766157576)
** p < .01 (q-critical[3, 381] = 4.14515568451)"""
df = DataFrame()
df.read_tbl('data/suppression~subjectXgroupXageXcycleXphase.csv')
D = df.anova1way('SUPPRESSION', 'GROUP')
self.assertEqual(str(D), R)
# instantiate DataFrame object to hold data
df = DataFrame() # inherents a dict
# put data into a DataFrame object
df['data'] = data1 + data2
# build dummy code column
df['conditions'] = ['A'] * len(data1) + ['B'] * len(data2)
# visually verify data in DataFrame
print(df)
# run 1 way analysis of variance
# returns another dict-like object
aov = df.anova1way('data', 'conditions')
# print anova results
print(aov)
# this is just to show the data in the aov object
print(aov.keys())
# calculate omega-squared
aov['omega-sq'] = (aov['ssbn'] - aov['dfbn']*aov['mswn']) / \
(aov['ssbn'] + aov['sswn'] + aov['mswn'])
# you can access the results this way
print(aov['omega-sq'])
print(aov['f'])
print(aov['p'])
