# -*- coding: utf-8 -*-
#
# This file is part of Sequana software
#
# Copyright (c) 2016 - Sequana Development Team
#
# File author(s):
# Thomas Cokelaer <thomas.cokelaer@pasteur.fr>
#
# Distributed under the terms of the 3-clause BSD license.
# The full license is in the LICENSE file, distributed with this software.
#
# website: https://github.com/sequana/sequana
# documentation: http://sequana.readthedocs.io
#
##############################################################################
from sequana.lazy import numpy as np
from sequana.lazy import pandas as pd
__all__ = ["ANOVA"]
[docs]class ANOVA(object):
"""DRAFT
Testing if 3(+) population means are all equal.
Looks like the group are different, visually, and naively.
.. plot::
:include-source:
from pylab import *
from sequana.viz import ANOVA
import pandas as pd
A = normal(0.5,size=10000)
B = normal(0.25, size=10000)
C = normal(0, 0.5,size=10000)
df = pd.DataFrame({"A":A, "B":B, "C":C})
a = ANOVA(df)
print(a.anova())
a.imshow_anova_pairs()
"""
def __init__(self, df):
self.df = df.copy()
[docs] def anova(self):
"""Perform one-way ANOVA.
The one-way ANOVA tests the null hypothesis that two or more groups have
the same population mean. The test is applied to samples from two or
more groups. Since we ar using a dataframe, vector length are identical.
return: the F value (test itself), and its p-value
"""
import scipy.stats
F, P = scipy.stats.f_oneway(*[self.df[x] for x in self.df.columns])
return F, P
[docs] def imshow_anova_pairs(self, log=True, **kargs):
# avoids circular imports
import scipy.stats
from sequana.viz import Imshow
N = len(self.df.columns)
# could use a dataframe straight way ?
res = np.ones((N, N))
for i, col1 in enumerate(self.df.columns):
for j, col2 in enumerate(self.df.columns):
d1 = self.df[col1]
d2 = self.df[col2]
F, P = scipy.stats.f_oneway(*[d1, d2])
res[i][j] = P
df = pd.DataFrame(res, index=self.df.columns, columns=self.df.columns)
# FIXME: may have na, which are set to 1
df = df.fillna(1)
if log is True:
Imshow(-np.log10(df)).plot(**kargs)
else:
Imshow(df).plot(**kargs)
return df