def analyzeData(results2):
print('Accuracy')
print(
AnovaRM(data=results2,
depvar='Accuracy',
subject='Subject',
within=['Condition'],
aggregate_func='mean').fit())
MultiComp = MultiComparison(results2['Accuracy'], results2['Condition'])
comp = MultiComp.allpairtest(sci.ttest_rel, method='bonf')
print(comp[0])
print('Reaction Time')
print(
AnovaRM(data=results2,
depvar='Reaction Time',
subject='Subject',
within=['Condition'],
aggregate_func='mean').fit())
MultiComp = MultiComparison(results2['Reaction Time'],
results2['Condition'])
comp = MultiComp.allpairtest(sci.ttest_rel, method='bonf')
print(comp[0])
def get_multiplecomparisons(self, dataframe, test):
# If distributions are different then do multiple comparisons
dataframe = dataframe.dropna()
print(dataframe)
cleanbin = dataframe.melt(var_name='Bin', value_name='Value')
MultiComp = MultiComparison(cleanbin['Value'], cleanbin['Bin'])
if test == 'ttest':
comp = MultiComp.allpairtest(scipy.stats.ttest_rel, method='Bonf')
else:
comp = MultiComp.allpairtest(scipy.stats.wilcoxon, method='Bonf')
print(comp[0])
def calculate_test(self):
"""It applies Holm-Bonferroni test to the dataframe. Bonferroni is a multi-comparison method.
Discover more at https://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni_method .
Be sure you are working with a normal distribution"""
MultiComp = MultiComparison(self.data.values,self.data.index)
holm=MultiComp.allpairtest(stats.ttest_rel, method='Holm')
print("\nHolm-Bonferroni test for rows\n"+str(holm) +"\n")
self.results.write("\nHolm-Bonferroni test for rows\n"+str(holm) +"\n")
MultiComp = MultiComparison(self.data.T.values,self.data.columns)
holm2=MultiComp.allpairtest(stats.ttest_rel, method='Holm')
print("\nHolm-Bonferroni test for columns\n"+str(holm2) +"\n")
self.results.write("\nHolm-Bonferroni test for columns\n"+str(holm2) +"\n")
return (holm,holm2)
def get_significance_booleans(data):
'''
preform multiple comparisons (t-tests).
paramters
---------
data: Series
must have a single level index containing the group labels (id).
values are the results to be compared
returns
------
booleans: Series
boolean values indicating significance between the groups.
'''
id = data.index.values
value = data.values
# multiple comparison
multiple_comparisons = MultiComparison(value, id) # instanciate multiple comparisons object
pairwise_holm = multiple_comparisons.allpairtest(ttest_ind, method='holm') # preform pairwise t-test
significance_matrix = DataFrame(pairwise_holm[2]) # store results in dataframe
groups_as_index = significance_matrix.set_index(['group1', 'group2'])
significance_booleans = groups_as_index['reject']
return significance_booleans
def pairwise_ttest(val_vec, cnf):
df = pd.DataFrame()
cluster = []
score = []
for subc, dic_conf in val_vec.items():
cluster += [str(subc) for idx in range(len(dic_conf[cnf]))]
score.extend(dic_conf[cnf])
df['subcluster'] = cluster
df['score'] = score
# all_comb = list(combinations(df.subcluster, 2))
# p_vals = []
# for comb in all_comb:
# g1 = df[(df.subcluster == comb[0])]['score']
# g2 = df[(df.subcluster == comb[1])]['score']
# stat, pval = ttest_ind(g1, g2, equal_var=False)
# p_vals.append(pval)
# reject_list, corrected_p_vals = multipletests(p_vals, method='bonferroni')[:2]
# for comb, pv, cpv, r in zip(all_comb, p_vals, corrected_p_vals, reject_list):
# print("Comparison: {0} -- p={1}, corr_p={2}, rej={3}".format(
# comb, pv, cpv, r))
MultiComp = MultiComparison(df['score'], df['subcluster'])
comp = MultiComp.allpairtest(ttest_ind, method='bonf')
print(comp[0])
pd.options.display.float_format = '{:.3f}'.format
print(df.groupby(['subcluster']).describe())
def Holm_Bonferroni(multiComp:MultiComparison) -> float:
""" Instead of the Tukey's test, we can do pairwise t-test
Parameters
----------
multComp : Result of the 'MultiComparison'-test
Returns
-------
checkVal : the test paramter used for checking correct execution
"""
# First, with the "Holm" correction
rtp = multiComp.allpairtest(stats.ttest_rel, method='Holm')
print((rtp[0]))
# and then with the Bonferroni correction
print((multiComp.allpairtest(stats.ttest_rel, method='b')[0]))
# Any value, for testing the program for correct execution
checkVal = rtp[1][0][0,0]
return checkVal
def get_anova_multiplecomp(self, accuracy_dataframe):
# ANOVA and tukey test on the groups
f, p = scipy.stats.f_oneway(accuracy_dataframe['Task1'], accuracy_dataframe['Task2'],
accuracy_dataframe['Task2b'])
print('Anova %0.5f' % p)
# If distributions are different then do multiple comparisons
if p < 0.05:
df_melt = accuracy_dataframe.melt(var_name='Task', value_name='Error')
df_melt = df_melt[df_melt.Task != 'Task3']
# print(df_melt)
MultiComp = MultiComparison(df_melt['Error'],
df_melt['Task'])
comp = MultiComp.allpairtest(scipy.stats.ttest_rel, method='Holm')
print(comp[0])
def get_anova_multiplecomp_bytimebin(self, bin_df):
# ANOVA and tukey test on the groups
# bin_df = bin_df.drop()
f, p = scipy.stats.f_oneway(bin_df[bin_df.Bin == 'Bin0']['Value'], bin_df[bin_df.Bin == 'Bin1']['Value'],
bin_df[bin_df.Bin == 'Bin2']['Value'], bin_df[bin_df.Bin == 'Bin3']['Value'])
print('Anova %0.5f' % p)
# Remove uneven bins before comparison
clean_bin_df = bin_df[bin_df['Bin'].isin(['Bin0', 'Bin1', 'Bin2', 'Bin3'])]
# print(clean_bin_df)
# If distributions arse different then do multiple comparisons
if p < 0.05:
# print(df_melt)
MultiComp = MultiComparison(clean_bin_df['Value'],
clean_bin_df['Bin'])
comp = MultiComp.allpairtest(scipy.stats.kruskal, method='Holm')
print(comp[0])
def kruskal(*args):
localargs = locals()['args']
print(localargs)
statsk = stats.kruskal(*args)
print(statsk)
df = pd.DataFrame(localargs)
print(df)
stacked_data = df.stack().reset_index()
print(stacked_data)
stacked_data = stacked_data.rename(columns={
'level_0': 'genotype',
0: 'result'
})
print(stacked_data)
MultiComp = MultiComparison(stacked_data['result'],
stacked_data['genotype'])
print(MultiComp.allpairtest(stats.mannwhitneyu, method='Holm'))
def hypothesis_test_four(cleaned_data):
"""
This function takes in cleaned data, then uses create sample dists to grab
the required categories. From there the function performs fishers lSD
analysis and displays a chart of all the pairwise compairisons and the
p-values
:param alpha: the critical value of choice
:param cleaned_data: our cleaned dataset
:return:
"""
# Get data for tests
categories = ['NISS', 'FORD', 'HOND', 'TOY']
comparison_groups = create_sample_dists(cleaned_data,
y_var='ticket',
x_var='make',
categories=categories,
seed=4)
list_for_lsd = []
for i in range(len(categories)):
cat_list = [categories[i]] * 50
tk_lsd = zip(list(comparison_groups[i]), cat_list)
list_for_lsd += list(tk_lsd)
df_lsd = pd.DataFrame(list_for_lsd)
# perform fisher LSD for the groups
mult_comp = MultiComparison(df_lsd[0], df_lsd[1])
result = mult_comp.allpairtest(stats.ttest_ind, method='Holm')
# we need to convert the simpletable result object into a dataframe
result_summary = result[0].as_html()
lsd_df = pd.read_html(result_summary, header=0, index_col=0)[0]
lsd_df = lsd_df.drop(columns=['stat', 'pval_corr'])
lsd_df.reject = lsd_df.pval.apply(compare_pval_alpha_tf)
lsd_df.columns = ["Second Make", "P Value", "Signifigant Difference?"]
lsd_df.index.names = ['First Make']
return lsd_df
# ## Tukey's multi-comparison method # # See https://en.wikipedia.org/wiki/Tukey's_range_test # # This method tests at P<0.05 (correcting for the fact that multiple comparisons are being made which would normally increase the probability of a significant difference being identified). A results of 'reject = True' means that a significant difference has been observed. # In[26]: from statsmodels.stats.multicomp import (pairwise_tukeyhsd, MultiComparison) # Set up the data for comparison (creates a specialised object) MultiComp = MultiComparison(stacked_data['result'], stacked_data['treatment']) # Show all pair-wise comparisons: # Print the comparisons print(MultiComp.tukeyhsd().summary()) # ## Holm-Bonferroni Method # # See: https://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni_method # # The Holm-Bonferroni method is an alterantive method. # In[27]: comp = MultiComp.allpairtest(stats.ttest_rel, method='Holm') print(comp[0])
plt.xlim(*xlim)
pair_labels = mod.groupsunique[np.column_stack(res2[1][0])]
plt.xticks([0,1,2], pair_labels)
plt.title('Multiple Comparison of Means - Tukey HSD, FWER=0.05' +
'\n Pairwise Mean Differences')
# Save to outfile
outFile = 'MultComp.png'
plt.savefig('MultComp.png', dpi=200)
print 'Figure written to {0}'.format(outFile)
plt.show()
# Instead of the Tukey's test, we can do pairwise t-test
# First, with the "Holm" correction
rtp = mod.allpairtest(stats.ttest_rel, method='Holm')
print rtp[0]
# and then with the Bonferroni correction
print mod.allpairtest(stats.ttest_rel, method='b')[0]
# Done this way, the variance is calculated at each comparison.
# If you want the joint variance across all samples, you have to
# use a few tricks:(http://jpktd.blogspot.co.at/2013/03/multiple-comparison-and-tukey-hsd-or_25.html)
res2 = pairwise_tukeyhsd(dta2['StressReduction'], dta2['Treatment'])
studentized_mean = res2[1][2]
studentized_variance = res2[1][3]
t_stat = (studentized_mean / studentized_variance) / np.sqrt(2)
dof = len(dta2) - len(mod.groupsunique)
my_pvalues = stats.t.sf(np.abs(t_stat), dof) * 2 # two-sided
def position_stats(df, name_mapping=None):
# print '### position stats'
from statsmodels.stats.weightstats import ztest
from functools32 import partial, wraps
POS = df.position.unique()
POS.sort()
model = 'value ~ group'
allpvals = None
header = None
DF = None
ttest_log_wrap = wraps(
partial(ttest_ind_log, equal_var=False))(ttest_ind_log)
ttest_ind_nev = wraps(
partial(stats.ttest_ind, equal_var=False))(stats.ttest_ind)
mwu_test = wraps(partial(stats.mannwhitneyu, use_continuity=False))(
stats.mannwhitneyu)
bootstrap_sample_num = 1000
# print df
stats_test = ttest_ind_nev
GROUPS = df.group.unique()
# GROUPS = [0,3]
for pos in POS:
# print pos
data = df[df.position == pos]
data = data.groupby(['sid']).mean()
data = resample_data(data, num_sample_per_pos=BOOTSTRAP_NUM)
# print data
# print data.group.unique()
# data = df[(df.group == 0) | (df.group == 3)]
# print data
# sys.exit()
#cross = smf.ols(model, data=data).fit()
#anova = sm.stats.anova_lm(cross, type=1)
# print data.group
mcp = MultiComparison(data.value, data.group.astype(int))
rtp = mcp.allpairtest(stats_test, method='bonf')
mheader = []
for itest in rtp[2]:
name1 = itest[0]
name2 = itest[1]
if name_mapping is not None:
name1 = name_mapping[str(name1)]
name2 = name_mapping[str(name2)]
mheader.append("{} - {}".format(name1, name2))
if not header or len(mheader) > len(header):
header = mheader
# get the uncorrecte pvals
pvals = rtp[1][0][:, 1]
ndf = pd.DataFrame(data=[pvals], columns=mheader)
if allpvals is None:
allpvals = ndf
else:
allpvals = pd.concat([allpvals, ndf])
# return allpvals
# corr_pvals = allpvals
# print allpvals
# return allpvals
flatten = allpvals.values.ravel()
flatten = flatten * 2
mcpres = multipletests(flatten, alpha=0.05, method='bonf')
# print mcpres
corr_pvals = np.array(mcpres[1])
# print corr_pvals
corr_pvals = np.reshape(corr_pvals, (len(POS), -1))
# print corr_pvals,corr_pvals.shape,header
data = pd.DataFrame(data=corr_pvals, columns=header)
data = data[data.columns[:3]]
return data
#Check for heteroskedasticity sm.qqplot(anova_reg.resid, line='s') plt.show() ###### #Post Hoc Tests for One-way ANOVA #Tukey test - good when groups are the same size and have and homogeneous variance postHoc = pairwise_tukeyhsd(alldata['Fare_Per_Person'], alldata['Embarked'], alpha=0.05) print(postHoc) #Pairwise comparison using Bonferroni correction of p-values mc = MultiComparison(alldata['Fare_Per_Person'], alldata['Embarked']) #print(mc.allpairtest(stats.ttest_rel, method='Holm')[0]) #For paired t-test print(mc.allpairtest(stats.ttest_ind, method='b')[0]) #For independent t-test ###### #ANCOVA #Look for heteroskedasticity plt.plot(alldata[(alldata['Pclass']==2) & (alldata['Sex_male']==1)]['Fare_Per_Person'], alldata[(alldata['Pclass']==2) &(alldata['Sex_male']==1)]['Group_Size'], 'bo') plt.show() #Second class male passengers with a fare price > 0 seem OK #There are a couple group sizes with only 1 observation with these criteria though, so make sure to filter them out too #Test for heteroskedasticity print(levenes_test(alldata[(alldata['Pclass']==2) & (alldata['Sex_male']==1) & (alldata['Fare']>0) & (alldata['Group_Size'].isin([1,2,3,4,8,9,10,11]))]['Fare_Per_Person'], alldata[(alldata['Pclass']==2) & (alldata['Sex_male']==1) & (alldata['Fare']>0) & (alldata['Group_Size'].isin([1,2,3,4,8,9,10,11]))]['Group_Size'])) print(bartlett_test(alldata[(alldata['Pclass']==2) & (alldata['Sex_male']==1) & (alldata['Fare']>0) & (alldata['Group_Size'].isin([1,2,3,4,8,9,10,11]))]['Fare_Per_Person'], alldata[(alldata['Pclass']==2) & (alldata['Sex_male']==1) & (alldata['Fare']>0) & (alldata['Group_Size'].isin([1,2,3,4,8,9,10,11]))]['Group_Size'])) sub = alldata[(alldata['Pclass']==2) & (alldata['Sex_male']==1) & (alldata['Fare']>0) & (alldata['Group_Size'].isin([1,2,3,4,8,9,10,11]))]
def main():
# Note: the statsmodels module is required here.
from statsmodels.stats.multicomp import (pairwise_tukeyhsd,
MultiComparison)
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm
# Set up the data, as a structured array.
# The first and last field are 32-bit intergers; the second field is an
# 8-byte string. Note that here we can also give names to the individual
# fields!
dta2 = np.rec.array([
( 1, 'mental', 2 ),
( 2, 'mental', 2 ),
( 3, 'mental', 3 ),
( 4, 'mental', 4 ),
( 5, 'mental', 4 ),
( 6, 'mental', 5 ),
( 7, 'mental', 3 ),
( 8, 'mental', 4 ),
( 9, 'mental', 4 ),
( 10, 'mental', 4 ),
( 11, 'physical', 4 ),
( 12, 'physical', 4 ),
( 13, 'physical', 3 ),
( 14, 'physical', 5 ),
( 15, 'physical', 4 ),
( 16, 'physical', 1 ),
( 17, 'physical', 1 ),
( 18, 'physical', 2 ),
( 19, 'physical', 3 ),
( 20, 'physical', 3 ),
( 21, 'medical', 1 ),
( 22, 'medical', 2 ),
( 23, 'medical', 2 ),
( 24, 'medical', 2 ),
( 25, 'medical', 3 ),
( 26, 'medical', 2 ),
( 27, 'medical', 3 ),
( 28, 'medical', 1 ),
( 29, 'medical', 3 ),
( 30, 'medical', 1 )], dtype=[('idx', '<i4'),
('Treatment', '|S8'),
('StressReduction', '<i4')])
# First, do an one-way ANOVA
df = pd.DataFrame(dta2)
model = ols('StressReduction ~ C(Treatment)',df).fit()
anovaResults = anova_lm(model)
print(anovaResults)
if anovaResults['PR(>F)'][0] < 0.05:
print('One of the groups is different.')
#Then, do the multiple testing
mod = MultiComparison(dta2['StressReduction'], dta2['Treatment'])
print((mod.tukeyhsd().summary()))
# The following code produces the same printout
res2 = pairwise_tukeyhsd(dta2['StressReduction'], dta2['Treatment'])
#print res2[0]
# Show the group names
print((mod.groupsunique))
# Generate a print
import matplotlib.pyplot as plt
xvals = np.arange(3)
plt.plot(xvals, res2.meandiffs, 'o')
#plt.errorbar(xvals, res2.meandiffs, yerr=np.abs(res2[1][4].T-res2[1][2]), ls='o')
errors = np.ravel(np.diff(res2.confint)/2)
plt.errorbar(xvals, res2.meandiffs, yerr=errors, ls='o')
xlim = -0.5, 2.5
plt.hlines(0, *xlim)
plt.xlim(*xlim)
pair_labels = mod.groupsunique[np.column_stack(res2._multicomp.pairindices)]
plt.xticks(xvals, pair_labels)
plt.title('Multiple Comparison of Means - Tukey HSD, FWER=0.05' +
'\n Pairwise Mean Differences')
# Save to outfile
outFile = 'MultComp.png'
plt.savefig('MultComp.png', dpi=200)
print(('Figure written to {0}'.format(outFile)))
plt.show()
# Instead of the Tukey's test, we can do pairwise t-test
# First, with the "Holm" correction
rtp = mod.allpairtest(stats.ttest_rel, method='Holm')
print((rtp[0]))
# and then with the Bonferroni correction
print((mod.allpairtest(stats.ttest_rel, method='b')[0]))
# Done this way, the variance is calculated at each comparison.
# If you want the joint variance across all samples, you have to
# use a few tricks:(http://jpktd.blogspot.co.at/2013/03/multiple-comparison-and-tukey-hsd-or_25.html)
res2 = pairwise_tukeyhsd(dta2['StressReduction'], dta2['Treatment'])
studentized_mean = res2.meandiffs
studentized_variance = res2.variance
t_stat = (studentized_mean / studentized_variance) / np.sqrt(2)
dof = len(dta2) - len(mod.groupsunique)
my_pvalues = stats.t.sf(np.abs(t_stat), dof) * 2 # two-sided
# Now with the Bonferroni correction
from statsmodels.stats.multitest import multipletests
res_b = multipletests(my_pvalues, method='b')
return res2.variance
w, p_bf = stats.levene(edg['WPM'], graf['WPM'], uni['WPM'], center='median')
check_p('brown forsythe test',
assumption='homogeneity of variance',
p_val=p_bf)
# non-significance shows we don't have a violation
# now that we know our assumptions have not been violated, we can fit the ANOVA. This is the omnibus test
alpha_lm = ols('WPM ~ C(Alphabet)', data=alpha).fit()
logger.info(f'ANOVA summary: \n\n {alpha_lm.summary()}')
# Prob (F-statistic) shows that there is some difference between the different Alphabets but does not tell us where the
# difference is. For that we do the pairwise comparisons
# tukey comparison followed by holm adjustment (not sure how to combine the two
mc = MultiComparison(alpha['WPM'], alpha['Alphabet'])
logger.info(f'tukey comparison2: \n {mc.tukeyhsd()}')
comp = mc.allpairtest(stats.ttest_ind, method='Holm')
logger.info(f'holm corrected version: \n {comp[0]}')
# non parametric version of one-way ANOVA
chi, p = stats.kruskal(edg['WPM'], graf['WPM'], uni['WPM'])
check_p(descr='Kruskal chi squared test', assumption='', p_val=p)
# mann whitney
mw, p_eg = stats.mannwhitneyu(edg['WPM'], graf['WPM'], alternative='two-sided')
logger.info(f'mann-whitney stat edg vs. graf: {mw}, p value: {p_eg}')
mw, p_ug = stats.mannwhitneyu(uni['WPM'], graf['WPM'], alternative='two-sided')
logger.info(f'mann-whitney stat uni vs. graf: {mw}, p value: {p_ug}')
mw, p_ue = stats.mannwhitneyu(uni['WPM'], edg['WPM'], alternative='two-sided')
logger.info(f'mann-whitney stat EC vs. PC: {mw}, p value: {p_ue}')
rej, p_vals, _, _ = multitest.multipletests([p_eg, p_ug, p_ue], method='holm')
for num, pv in enumerate(p_vals):
def KMWU(pathname='',
pulsedurs=[5],
genders=["_male", "_female", "_matedFemale"],
neuronparts=["medial", "lateral"],
identifiers=[".mat", "10ms", "40Hz"],
key="pulsedff",
compareOn="genders",
multicompmethod='holm'):
'''performs a Kruskal-wallis test followed by multiple comparisons with mann-whitney-U-test'''
currentdir = os.getcwd()
if pathname:
if pathname[0] == '/':
fullpath = pathname
else:
fullpath = os.path.join(currentdir, pathname)
else:
fullpath = currentdir
dirlist = os.listdir(fullpath)
ps = []
for pulsedur in pulsedurs:
if pulsedur < 1:
pulsedurstring = str(int(1000 * pulsedur)) + 'ms'
else:
pulsedurstring = str(int(pulsedur)) + 's'
filelists = []
for n in neuronparts:
for g in genders:
gfiles = [
filename for filename in dirlist
if g in filename and pulsedurstring in filename and all(
[identifier in filename for identifier in identifiers])
]
nfiles = [filename for filename in gfiles if n in filename]
if nfiles:
filelists.append(nfiles)
pulsedffs = []
for filelist in filelists:
fullfile = os.path.join(fullpath, filelist[0])
data = scipy.io.loadmat(fullfile, matlab_compatible=True)
pulsedff = [dat[0] for dat in data[key]]
pulsedffs.append(pulsedff)
if compareOn == "genders":
for npart in range(len(neuronparts)):
neuronpart = neuronparts[npart]
print(neuronpart)
groupnum = npart * len(genders)
numgenders = len(genders)
data = tuple(pulsedffs[groupnum:(groupnum + numgenders)])
df = pd.DataFrame(pulsedffs[groupnum:(groupnum + numgenders)])
df.rename(index={0: "female", 1: 'matedFemale', 2: 'male'})
statsk = scipy.stats.kruskal(*data)
print(statsk)
stacked_data = df.stack().reset_index()
stacked_data.rename(index={
0: "female",
1: 'matedFemale',
2: 'male'
})
stacked_data = stacked_data.rename(columns={
'level_0': 'genotype',
0: 'result'
})
MultiComp = MultiComparison(stacked_data['result'],
stacked_data['genotype'])
print(
MultiComp.allpairtest(scipy.stats.ranksums, method='Holm'))
elif compareOn == "neuronparts":
for gend in range(len(genders)):
ind = [(numnpart * len(genders) + gend)
for numnpart in range(len(neuronparts))]
data = tuple([pulsedffs[index] for index in ind])
df = pd.DataFrame([pulsedffs[index] for index in ind])
statsk = scipy.stats.kruskal(*data)
print(statsk)
stacked_data = df.stack().reset_index()
stacked_data.rename(index={0: "medial", 1: 'lateral'})
stacked_data = stacked_data.rename(columns={
'level_0': 'neuronpart',
0: 'result'
})
MultiComp = MultiComparison(stacked_data['result'],
stacked_data['neuronpart'])
print(
MultiComp.allpairtest(scipy.stats.ranksums, method='Holm'))
else:
print(
"not a valid selection for compareOn - must be \"genders\" or \"neuronparts\""
)
parser.add_argument('--output',
required=True,
default='MultiComparison.csv',
help='out file name.')
args = parser.parse_args()
method = args.method
in_path = args.input
# output = args.output
col1 = args.col1
col2 = args.col2
df = pd.read_csv(in_path)
df[col2] = df[col2].astype("float64")
# print(df.head(3))
multiComp = MultiComparison(df[col2], df[col1])
if method == 'Tukey':
print(multiComp.tukeyhsd().summary())
result = multiComp.tukeyhsd().summary()
resultdf = pd.DataFrame(result)
resultdf.to_csv(args.output, header=None, index=False)
else:
print(multiComp.allpairtest(stats.ttest_rel, method=method)[-1])
result = multiComp.allpairtest(stats.ttest_rel, method=method)[-1]
resultdf = pd.DataFrame(result)
resultdf.to_csv(args.output, index=False)
#python MultiComparison.py --method "Tukey" --input ../../data/sample.csv --col1 Treatment --col2 "StressReduction" --output ../../out/MultiComparison.csv
def main():
# Note: the statsmodels module is required here.
from statsmodels.stats.multicomp import (pairwise_tukeyhsd,
MultiComparison)
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm
# Set up the data, as a structured array.
# The first and last field are 32-bit intergers; the second field is an
# 8-byte string. Note that here we can also give names to the individual
# fields!
dta2 = np.rec.array([(1, 'mental', 2), (2, 'mental', 2), (3, 'mental', 3),
(4, 'mental', 4), (5, 'mental', 4), (6, 'mental', 5),
(7, 'mental', 3), (8, 'mental', 4), (9, 'mental', 4),
(10, 'mental', 4), (11, 'physical', 4),
(12, 'physical', 4), (13, 'physical', 3),
(14, 'physical', 5), (15, 'physical', 4),
(16, 'physical', 1), (17, 'physical', 1),
(18, 'physical', 2), (19, 'physical', 3),
(20, 'physical', 3), (21, 'medical', 1),
(22, 'medical', 2), (23, 'medical', 2),
(24, 'medical', 2), (25, 'medical', 3),
(26, 'medical', 2), (27, 'medical', 3),
(28, 'medical', 1), (29, 'medical', 3),
(30, 'medical', 1)],
dtype=[('idx', '<i4'), ('Treatment', '|S8'),
('StressReduction', '<i4')])
# First, do an one-way ANOVA
df = pd.DataFrame(dta2)
model = ols('StressReduction ~ C(Treatment)', df).fit()
anovaResults = anova_lm(model)
print(anovaResults)
if anovaResults['PR(>F)'][0] < 0.05:
print('One of the groups is different.')
#Then, do the multiple testing
mod = MultiComparison(dta2['StressReduction'], dta2['Treatment'])
print((mod.tukeyhsd().summary()))
# The following code produces the same printout
res2 = pairwise_tukeyhsd(dta2['StressReduction'], dta2['Treatment'])
#print res2[0]
# Show the group names
print((mod.groupsunique))
# Generate a print
import matplotlib.pyplot as plt
xvals = np.arange(3)
plt.plot(xvals, res2.meandiffs, 'o')
#plt.errorbar(xvals, res2.meandiffs, yerr=np.abs(res2[1][4].T-res2[1][2]), ls='o')
errors = np.ravel(np.diff(res2.confint) / 2)
plt.errorbar(xvals, res2.meandiffs, yerr=errors, ls='o')
xlim = -0.5, 2.5
plt.hlines(0, *xlim)
plt.xlim(*xlim)
pair_labels = mod.groupsunique[np.column_stack(
res2._multicomp.pairindices)]
plt.xticks(xvals, pair_labels)
plt.title('Multiple Comparison of Means - Tukey HSD, FWER=0.05' +
'\n Pairwise Mean Differences')
# Save to outfile
outFile = 'MultComp.png'
plt.savefig('MultComp.png', dpi=200)
print(('Figure written to {0}'.format(outFile)))
plt.show()
# Instead of the Tukey's test, we can do pairwise t-test
# First, with the "Holm" correction
rtp = mod.allpairtest(stats.ttest_rel, method='Holm')
print((rtp[0]))
# and then with the Bonferroni correction
print((mod.allpairtest(stats.ttest_rel, method='b')[0]))
# Done this way, the variance is calculated at each comparison.
# If you want the joint variance across all samples, you have to
# use a few tricks:(http://jpktd.blogspot.co.at/2013/03/multiple-comparison-and-tukey-hsd-or_25.html)
res2 = pairwise_tukeyhsd(dta2['StressReduction'], dta2['Treatment'])
studentized_mean = res2.meandiffs
studentized_variance = res2.variance
t_stat = (studentized_mean / studentized_variance) / np.sqrt(2)
dof = len(dta2) - len(mod.groupsunique)
my_pvalues = stats.t.sf(np.abs(t_stat), dof) * 2 # two-sided
# Now with the Bonferroni correction
from statsmodels.stats.multitest import multipletests
res_b = multipletests(my_pvalues, method='b')
return res2.variance
