def test_chi2_contingency_g():
c = np.array([[15, 60], [15, 90]])
g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood', correction=False)
assert_allclose(g, 2*xlogy(c, c/e).sum())
g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood', correction=True)
c_corr = c + np.array([[-0.5, 0.5], [0.5, -0.5]])
assert_allclose(g, 2*xlogy(c_corr, c_corr/e).sum())
c = np.array([[10, 12, 10], [12, 10, 10]])
g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood')
assert_allclose(g, 2*xlogy(c, c/e).sum())
def test_chi2_contingency_g():
c = np.array([[15, 60], [15, 90]])
g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood', correction=False)
assert_allclose(g, 2*xlogy(c, c/e).sum())
g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood', correction=True)
c_corr = c + np.array([[-0.5, 0.5], [0.5, -0.5]])
assert_allclose(g, 2*xlogy(c_corr, c_corr/e).sum())
c = np.array([[10, 12, 10], [12, 10, 10]])
g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood')
assert_allclose(g, 2*xlogy(c, c/e).sum())
def test_chi2_contingency_trivial():
"""Some very simple tests for chi2_contingency."""
# A trivial case
obs = np.array([[1, 2], [1, 2]])
chi2, p, dof, expected = chi2_contingency(obs, correction=False)
assert_equal(chi2, 0.0)
assert_equal(p, 1.0)
assert_equal(dof, 1)
assert_array_equal(obs, expected)
# A *really* trivial case: 1-D data.
obs = np.array([1, 2, 3])
chi2, p, dof, expected = chi2_contingency(obs, correction=False)
assert_equal(chi2, 0.0)
assert_equal(p, 1.0)
assert_equal(dof, 0)
assert_array_equal(obs, expected)
def test_chi2_contingency_trivial():
"""Some very simple tests for chi2_contingency."""
# A trivial case
obs = np.array([[1, 2], [1, 2]])
chi2, p, dof, expected = chi2_contingency(obs, correction=False)
assert_equal(chi2, 0.0)
assert_equal(p, 1.0)
assert_equal(dof, 1)
assert_array_equal(obs, expected)
# A *really* trivial case: 1-D data.
obs = np.array([1, 2, 3])
chi2, p, dof, expected = chi2_contingency(obs, correction=False)
assert_equal(chi2, 0.0)
assert_equal(p, 1.0)
assert_equal(dof, 0)
assert_array_equal(obs, expected)
def independentChi(A, B, alpha=0.05):
contingency = getContingency(A, B)
res = chi2_contingency(contingency, correction=False)
print(res)
# A and B are independant if true
return res[1] < alpha
def independentG(A, B, alpha=0.05):
contingency = getContingency(A, B)
res = chi2_contingency(contingency,
correction=False,
lambda_="log-likelihood")
print(res)
# A and B are independant if true
return res[1] < alpha
def Analysis(rdata):
df = pd.DataFrame(rdata) # 데이터를 DataFrame으로 바꿔서 변수에 저장.
#df.dropna() # 노파심에 함.
#print(df)
df['genNum'] = df['gender'].apply(lambda g:1 if g == '남' else 2) # 칼럼 추가. genNum이라는 컬럼을 추가하고 남자일 때 1, 아닐 때 2.
df['coNum'] = df['co_survey'].apply(lambda c:1 if c == '스타벅스' else 2 # 칼럼 추가. coNum이라는 컬럼을 추가하고 스타벅스 일 때 1, 커피빈일 때 2, 이디아일 때 3, 탐앤탐스 일 때는 4.
if c == '커피빈' else 3
if c == '이디아' else 4)
#print(df)
crosstal = pd.crosstab(index = df['genNum'], columns = df['co_survey']) # 인덱스가 열
#print(crosstal)
#st, pv, _, _ = chi2_contingency(crosstal) # 이 방법이나 밑에 방법이나 똑같음.
st, pv, _, _ = chi2_contingency((df['genNum'], df['coNum']))
#print("통계값 : {}, p-value : {}".format(st, pv))
if pv > 0.05:
result = "<b>p 값이 {}</b>이므로 유의수준 0.05보다 커 <b>귀무가설을 채택</b><br>(성별에 따라 선호하는 커피 브랜드에는 차이가 없다)".format(pv)
else:
result = "<b>p 값이 {}</b>이므로 유의수준 0.05보다 작아 <b>귀무가설을 기각</b><br>(성별에 따라 선호하는 커피 브랜드에는 차이가 있다)".format(pv)
return crosstal, result
def test_chi2_contingency_R():
# Some test cases that were computed independently, using R.
# Rcode = \
# """
# # Data vector.
# data <- c(
# 12, 34, 23, 4, 47, 11,
# 35, 31, 11, 34, 10, 18,
# 12, 32, 9, 18, 13, 19,
# 12, 12, 14, 9, 33, 25
# )
#
# # Create factor tags:r=rows, c=columns, t=tiers
# r <- factor(gl(4, 2*3, 2*3*4, labels=c("r1", "r2", "r3", "r4")))
# c <- factor(gl(3, 1, 2*3*4, labels=c("c1", "c2", "c3")))
# t <- factor(gl(2, 3, 2*3*4, labels=c("t1", "t2")))
#
# # 3-way Chi squared test of independence
# s = summary(xtabs(data~r+c+t))
# print(s)
# """
# Routput = \
# """
# Call: xtabs(formula = data ~ r + c + t)
# Number of cases in table: 478
# Number of factors: 3
# Test for independence of all factors:
# Chisq = 102.17, df = 17, p-value = 3.514e-14
# """
obs = np.array(
[[[12, 34, 23],
[35, 31, 11],
[12, 32, 9],
[12, 12, 14]],
[[4, 47, 11],
[34, 10, 18],
[18, 13, 19],
[9, 33, 25]]])
chi2, p, dof, expected = chi2_contingency(obs)
assert_approx_equal(chi2, 102.17, significant=5)
assert_approx_equal(p, 3.514e-14, significant=4)
assert_equal(dof, 17)
# Rcode = \
# """
# # Data vector.
# data <- c(
# #
# 12, 17,
# 11, 16,
# #
# 11, 12,
# 15, 16,
# #
# 23, 15,
# 30, 22,
# #
# 14, 17,
# 15, 16
# )
#
# # Create factor tags:r=rows, c=columns, d=depths(?), t=tiers
# r <- factor(gl(2, 2, 2*2*2*2, labels=c("r1", "r2")))
# c <- factor(gl(2, 1, 2*2*2*2, labels=c("c1", "c2")))
# d <- factor(gl(2, 4, 2*2*2*2, labels=c("d1", "d2")))
# t <- factor(gl(2, 8, 2*2*2*2, labels=c("t1", "t2")))
#
# # 4-way Chi squared test of independence
# s = summary(xtabs(data~r+c+d+t))
# print(s)
# """
# Routput = \
# """
# Call: xtabs(formula = data ~ r + c + d + t)
# Number of cases in table: 262
# Number of factors: 4
# Test for independence of all factors:
# Chisq = 8.758, df = 11, p-value = 0.6442
# """
obs = np.array(
[[[[12, 17],
[11, 16]],
[[11, 12],
[15, 16]]],
[[[23, 15],
[30, 22]],
[[14, 17],
[15, 16]]]])
chi2, p, dof, expected = chi2_contingency(obs)
assert_approx_equal(chi2, 8.758, significant=4)
assert_approx_equal(p, 0.6442, significant=4)
assert_equal(dof, 11)
import pandas as pd
import numpy as np
import scipy
from scipy.stats.contingency import chi2_contingency
df1 = pd.read_csv("C:/Users/Aaron Korver/Desktop/AnalyseCijfers2.csv")
df1['trip_purpose'] = df1['trip_purpose'].replace(['betaaldwerk', 'dagelijkeboodschappen', 'diensten','niet-dagelijkeboodschappen','studie'], '1')
df1['trip_purpose'] = df1['trip_purpose'].replace(['recreatie', 'sociaal', 'vrijetijd','home'], '0')
contingency = pd.crosstab(df1['BG2010NameDest'], df1['trip_purpose'])
contingency.to_csv("C:/Users/Aaron Korver/Desktop/ChiSquareOrig3.csv")
chi2, p, dof, expected = chi2_contingency(contingency)
print chi2, p, dof
def test_independence(data, colX, colY):
X = data[colX].astype(str)
Y = data[colY].astype(str)
observed = pd.crosstab(Y, X)
chi2, p, dof, expected = cn.chi2_contingency(observed.values)
return p
"""
Created on Wed May 8 13:02:49 2019
@author: mfatemeh
"""
import pandas as pd
df = pd.read_csv('titanic_train.csv')
df.isnull().sum()
observed_contigency_table = pd.crosstab(df.Survived, df.Pclass)
from scipy.stats.contingency import chi2_contingency
chi_2, p_val, dof, expected_contigency_table = chi2_contingency(
observed_contigency_table)
#the frredom ddegree is 2 because 3-1 =2 * 1 =2
#p_value is vey low therefore the H_0 is regected meaning not(they are independent); therefore, they are dependent(correlated)
observed_contigency_table2 = pd.crosstab(df.Survived, df.Sex)
chi_2_G, p_val, dof_G, expected_contigency_table_G = chi2_contingency(
observed_contigency_table2)
# if p_value is less than alpha (ass)
alpha = 0.05
if p_val < alpha:
print('The varianles are correlated at significant level', alpha)
else:
print('The variables are independent at signifiacant level', alpha)
###--- dropping unnecessary feature
df = df.drop(['# Columns: time'], axis=1)
###--- seperation of dependent and independent
X = df.iloc[:, 2:7].values
y = df.iloc[:, -1].values
###---correlation nalysis
corr = df.corr()
sns.heatmap(abs(corr), annot=True)
from scipy.stats.contingency import chi2_contingency
for i in range(len(X[0]) + 1):
contigency_table = pd.crosstab(y, X[:, i - 1])
chi_2, p_val, dof, expected_val = chi2_contingency(contigency_table)
# if p_value is less than alpha (pass)
alpha = 0.05
if p_val < alpha:
print('The varianles are correlated at significant level', alpha)
else:
print('The variables are independent at signifiacant level', alpha)
###-----encoding and labling
df.detected_activity = df.detected_activity.map({
'bending_1': 1,
'bending_2': 2,
'cycling': 3,
'lying': 4,
'sitting': 5,
'standing': 6,
def test_chi2_contingency_R():
# Some test cases that were computed independently, using R.
# Rcode = \
# """
# # Data vector.
# data <- c(
# 12, 34, 23, 4, 47, 11,
# 35, 31, 11, 34, 10, 18,
# 12, 32, 9, 18, 13, 19,
# 12, 12, 14, 9, 33, 25
# )
#
# # Create factor tags:r=rows, c=columns, t=tiers
# r <- factor(gl(4, 2*3, 2*3*4, labels=c("r1", "r2", "r3", "r4")))
# c <- factor(gl(3, 1, 2*3*4, labels=c("c1", "c2", "c3")))
# t <- factor(gl(2, 3, 2*3*4, labels=c("t1", "t2")))
#
# # 3-way Chi squared test of independence
# s = summary(xtabs(data~r+c+t))
# print(s)
# """
# Routput = \
# """
# Call: xtabs(formula = data ~ r + c + t)
# Number of cases in table: 478
# Number of factors: 3
# Test for independence of all factors:
# Chisq = 102.17, df = 17, p-value = 3.514e-14
# """
obs = np.array([[[12, 34, 23], [35, 31, 11], [12, 32, 9], [12, 12, 14]],
[[4, 47, 11], [34, 10, 18], [18, 13, 19], [9, 33, 25]]])
chi2, p, dof, expected = chi2_contingency(obs)
assert_approx_equal(chi2, 102.17, significant=5)
assert_approx_equal(p, 3.514e-14, significant=4)
assert_equal(dof, 17)
# Rcode = \
# """
# # Data vector.
# data <- c(
# #
# 12, 17,
# 11, 16,
# #
# 11, 12,
# 15, 16,
# #
# 23, 15,
# 30, 22,
# #
# 14, 17,
# 15, 16
# )
#
# # Create factor tags:r=rows, c=columns, d=depths(?), t=tiers
# r <- factor(gl(2, 2, 2*2*2*2, labels=c("r1", "r2")))
# c <- factor(gl(2, 1, 2*2*2*2, labels=c("c1", "c2")))
# d <- factor(gl(2, 4, 2*2*2*2, labels=c("d1", "d2")))
# t <- factor(gl(2, 8, 2*2*2*2, labels=c("t1", "t2")))
#
# # 4-way Chi squared test of independence
# s = summary(xtabs(data~r+c+d+t))
# print(s)
# """
# Routput = \
# """
# Call: xtabs(formula = data ~ r + c + d + t)
# Number of cases in table: 262
# Number of factors: 4
# Test for independence of all factors:
# Chisq = 8.758, df = 11, p-value = 0.6442
# """
obs = np.array([[[[12, 17], [11, 16]], [[11, 12], [15, 16]]],
[[[23, 15], [30, 22]], [[14, 17], [15, 16]]]])
chi2, p, dof, expected = chi2_contingency(obs)
assert_approx_equal(chi2, 8.758, significant=4)
assert_approx_equal(p, 0.6442, significant=4)
assert_equal(dof, 11)
# test Yates' correction (unbalanced case)
# > X <- matrix(c(1000, 10, 1, 0), ncol=2, nrow=2)
# > X
# [,1] [,2]
# [1,] 1000 1
# [2,] 10 0
# > chisq.test(X)
# Pearson's Chi-squared test with Yates' continuity correction
# data: X
# X-squared = 5.163e-28, df = 1, p-value = 1
obs = np.array([[1000, 1], [10, 0]])
chi2, p, dof, expected = chi2_contingency(obs, correction=True)
assert_approx_equal(p, 1.0, significant=2)
assert_allclose(chi2, 0.0)
def test_result(correction):
obs = np.array([[1, 2], [1, 2]])
res = chi2_contingency(obs, correction=correction)
assert_equal((res.statistic, res.pvalue, res.dof, res.expected_freq), res)
Created on Wed May 8 13:04:22 2019
@author: gsaikia
"""
import pandas as pd
df = pd.read_csv('titanic_train.csv')
df.isnull().sum()
observed_contigency_table = pd.crosstab(df.Survived, df.Sex)
from scipy.stats.contingency import chi2_contingency
chi_2, p_val, dof, expected_contingency_table = chi2_contingency(
observed_contigency_table)
alpha = 0.05
if p_val < alpha:
print('The variables are correlated at signicance level', alpha)
else:
print(
'We fail to reject that the variables are independent at signicance level',
alpha)
'''
c_t = pd.DataFrame([[250,200],[50,1000]],columns=['Plays Chess','Doesnt Play Chess'],
index=['Likes Science Fiction','Doesnt Like Science Fiction'])
chi2_contingency(c_t)
'''
def test_chi2_contingency_R():
"""Some test cases that were computed independently, using R."""
Rcode = \
"""
# Data vector.
data <- c(
12, 34, 23, 4, 47, 11,
35, 31, 11, 34, 10, 18,
12, 32, 9, 18, 13, 19,
12, 12, 14, 9, 33, 25
)
# Create factor tags:r=rows, c=columns, t=tiers
r <- factor(gl(4, 2*3, 2*3*4, labels=c("r1", "r2", "r3", "r4")))
c <- factor(gl(3, 1, 2*3*4, labels=c("c1", "c2", "c3")))
t <- factor(gl(2, 3, 2*3*4, labels=c("t1", "t2")))
# 3-way Chi squared test of independence
s = summary(xtabs(data~r+c+t))
print(s)
"""
Routput = \
"""
Call: xtabs(formula = data ~ r + c + t)
Number of cases in table: 478
Number of factors: 3
Test for independence of all factors:
Chisq = 102.17, df = 17, p-value = 3.514e-14
"""
obs = np.array(
[[[12, 34, 23],
[35, 31, 11],
[12, 32, 9],
[12, 12, 14]],
[[4, 47, 11],
[34, 10, 18],
[18, 13, 19],
[9, 33, 25]]])
chi2, p, dof, expected = chi2_contingency(obs)
assert_approx_equal(chi2, 102.17, significant=5)
assert_approx_equal(p, 3.514e-14, significant=4)
assert_equal(dof, 17)
Rcode = \
"""
# Data vector.
data <- c(
#
12, 17,
11, 16,
#
11, 12,
15, 16,
#
23, 15,
30, 22,
#
14, 17,
15, 16
)
# Create factor tags:r=rows, c=columns, d=depths(?), t=tiers
r <- factor(gl(2, 2, 2*2*2*2, labels=c("r1", "r2")))
c <- factor(gl(2, 1, 2*2*2*2, labels=c("c1", "c2")))
d <- factor(gl(2, 4, 2*2*2*2, labels=c("d1", "d2")))
t <- factor(gl(2, 8, 2*2*2*2, labels=c("t1", "t2")))
# 4-way Chi squared test of independence
s = summary(xtabs(data~r+c+d+t))
print(s)
"""
Routput = \
"""
Call: xtabs(formula = data ~ r + c + d + t)
Number of cases in table: 262
Number of factors: 4
Test for independence of all factors:
Chisq = 8.758, df = 11, p-value = 0.6442
"""
obs = np.array(
[[[[12, 17],
[11, 16]],
[[11, 12],
[15, 16]]],
[[[23, 15],
[30, 22]],
[[14, 17],
[15, 16]]]])
chi2, p, dof, expected = chi2_contingency(obs)
assert_approx_equal(chi2, 8.758, significant=4)
assert_approx_equal(p, 0.6442, significant=4)
assert_equal(dof, 11)
def test_chi2_contingency_yates_gh13875():
# Magnitude of Yates' continuity correction should not exceed difference
# between expected and observed value of the statistic; see gh-13875
observed = np.array([[1573, 3], [4, 0]])
p = chi2_contingency(observed)[1]
assert_allclose(p, 1, rtol=1e-12)
