ANOVAΒΆ
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import Analysis.Anova as anova
%matplotlib inline
%load_ext autoreload
%autoreload 2
groups = []
for mean in [1, 1.5, 1.5, 1]:
groups.append(np.random.normal(mean, 0.5, 100))
groups = pd.DataFrame(groups).T
ax = sns.boxplot(groups)
anova.one_way(groups)
+-----------+-------------+--------------+-------------+-------------+------------+
| F-value | p-value | effect sss | effect df | error sss | error df |
+===========+=============+==============+=============+=============+============+
| 35.4803 | 1.11022e-16 | 24.6688 | 3 | 91.7773 | 396 |
+-----------+-------------+--------------+-------------+-------------+------------+
(27.174986167650943, 5.5511151231257827e-16, 3, 396)
#Conduct anova on one population which was exposed to different levels of two treatments
factorByfactor = np.array([[np.random.normal(1, 0.5, 100), np.random.normal(1.5, 0.5, 100), np.random.normal(1, 0.5, 100)],
[np.random.normal(1, 0.5, 100), np.random.normal(1.5, 0.5, 100), np.random.normal(1, 0.5, 100)]]
).swapaxes(0,2).swapaxes(1,2)
anova.two_way(factorByfactor, 'seed', 'fertilizer')
+-------------+---------------+------+------------+-------------+
| Source | Mean square | df | F-values | p-values |
+=============+===============+======+============+=============+
| seed | 0.750668 | 1 | 3.24798 | 0.0720186 |
+-------------+---------------+------+------------+-------------+
| fertilizer | 20.3989 | 2 | 88.2614 | 1.11022e-16 |
+-------------+---------------+------+------------+-------------+
| Interaction | 0.234242 | 2 | 1.01351 | 0.363568 |
+-------------+---------------+------+------------+-------------+