def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
"""
Consider using one of month_plot or quarter_plot unless you need
irregular plotting.
Parameters
----------
grouped_x : iterable of DataFrames
Should be a GroupBy object (or similar pair of group_names and groups
as DataFrames) with a DatetimeIndex or PeriodIndex
"""
fig, ax = utils.create_mpl_ax(ax)
start = 0
ticks = []
for season, df in grouped_x:
df = df.copy() # or sort balks for series. may be better way
sort_values(df, inplace=True)
nobs = len(df)
x_plot = np.arange(start, start + nobs)
ticks.append(x_plot.mean())
ax.plot(x_plot, df.values, 'k')
ax.hlines(df.values.mean(), x_plot[0], x_plot[-1], colors='k')
start += nobs
ax.set_xticks(ticks)
ax.set_xticklabels(xticklabels)
ax.set_ylabel(ylabel)
ax.margins(.1, .05)
return fig
def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
"""
Consider using one of month_plot or quarter_plot unless you need
irregular plotting.
Parameters
----------
grouped_x : iterable of DataFrames
Should be a GroupBy object (or similar pair of group_names and groups
as DataFrames) with a DatetimeIndex or PeriodIndex
"""
fig, ax = utils.create_mpl_ax(ax)
start = 0
ticks = []
for season, df in grouped_x:
df = df.copy() # or sort balks for series. may be better way
sort_values(df, inplace=True)
nobs = len(df)
x_plot = np.arange(start, start + nobs)
ticks.append(x_plot.mean())
ax.plot(x_plot, df.values, 'k')
ax.hlines(df.values.mean(), x_plot[0], x_plot[-1], colors='k')
start += nobs
ax.set_xticks(ticks)
ax.set_xticklabels(xticklabels)
ax.set_ylabel(ylabel)
ax.margins(.1, .05)
return fig
def test_mosaic(close_figures):
# make the same analysis on a known dataset
# load the data and clean it a bit
affairs = datasets.fair.load_pandas()
datas = affairs.exog
# any time greater than 0 is cheating
datas['cheated'] = affairs.endog > 0
# sort by the marriage quality and give meaningful name
# [rate_marriage, age, yrs_married, children,
# religious, educ, occupation, occupation_husb]
datas = sort_values(datas, ['rate_marriage', 'religious'])
num_to_desc = {1: 'awful', 2: 'bad', 3: 'intermediate',
4: 'good', 5: 'wonderful'}
datas['rate_marriage'] = datas['rate_marriage'].map(num_to_desc)
num_to_faith = {1: 'non religious', 2: 'poorly religious', 3: 'religious',
4: 'very religious'}
datas['religious'] = datas['religious'].map(num_to_faith)
num_to_cheat = {False: 'faithful', True: 'cheated'}
datas['cheated'] = datas['cheated'].map(num_to_cheat)
# finished cleaning
_, ax = plt.subplots(2, 2)
mosaic(datas, ['rate_marriage', 'cheated'], ax=ax[0, 0],
title='by marriage happiness')
mosaic(datas, ['religious', 'cheated'], ax=ax[0, 1],
title='by religiosity')
mosaic(datas, ['rate_marriage', 'religious', 'cheated'], ax=ax[1, 0],
title='by both', labelizer=lambda k:'')
ax[1, 0].set_xlabel('marriage rating')
ax[1, 0].set_ylabel('religion status')
mosaic(datas, ['religious', 'rate_marriage'], ax=ax[1, 1],
title='inter-dependence', axes_label=False)
plt.suptitle("extramarital affairs (plot 3 of 4)")
def test_mosaic():
# make the same analysis on a known dataset
# load the data and clean it a bit
affairs = datasets.fair.load_pandas()
datas = affairs.exog
# any time greater than 0 is cheating
datas['cheated'] = affairs.endog > 0
# sort by the marriage quality and give meaningful name
# [rate_marriage, age, yrs_married, children,
# religious, educ, occupation, occupation_husb]
datas = sort_values(datas, ['rate_marriage', 'religious'])
num_to_desc = {1: 'awful', 2: 'bad', 3: 'intermediate',
4: 'good', 5: 'wonderful'}
datas['rate_marriage'] = datas['rate_marriage'].map(num_to_desc)
num_to_faith = {1: 'non religious', 2: 'poorly religious', 3: 'religious',
4: 'very religious'}
datas['religious'] = datas['religious'].map(num_to_faith)
num_to_cheat = {False: 'faithful', True: 'cheated'}
datas['cheated'] = datas['cheated'].map(num_to_cheat)
# finished cleaning
fig, ax = pylab.subplots(2, 2)
mosaic(datas, ['rate_marriage', 'cheated'], ax=ax[0, 0],
title='by marriage happiness')
mosaic(datas, ['religious', 'cheated'], ax=ax[0, 1],
title='by religiosity')
mosaic(datas, ['rate_marriage', 'religious', 'cheated'], ax=ax[1, 0],
title='by both', labelizer=lambda k:'')
ax[1, 0].set_xlabel('marriage rating')
ax[1, 0].set_ylabel('religion status')
mosaic(datas, ['religious', 'rate_marriage'], ax=ax[1, 1],
title='inter-dependence', axes_label=False)
pylab.suptitle("extramarital affairs (plot 3 of 4)")
def _ros_sort(df, observations, censorship, warn=False):
"""
This function prepares a dataframe for ROS.
It sorts ascending with
left-censored observations first. Censored observations larger than
the maximum uncensored observations are removed from the dataframe.
Parameters
----------
df : pandas.DataFrame
observations : str
Name of the column in the dataframe that contains observed
values. Censored values should be set to the detection (upper)
limit.
censorship : str
Name of the column in the dataframe that indicates that a
observation is left-censored. (i.e., True -> censored,
False -> uncensored)
Returns
------
sorted_df : pandas.DataFrame
The sorted dataframe with all columns dropped except the
observation and censorship columns.
"""
# separate uncensored data from censored data
censored = sort_values(df[df[censorship]], observations, axis=0)
uncensored = sort_values(df[~df[censorship]], observations, axis=0)
if censored[observations].max() > uncensored[observations].max():
censored = censored[
censored[observations] <= uncensored[observations].max()]
if warn:
msg = ("Dropping censored observations greater than "
"the max uncensored observation.")
warnings.warn(msg)
return censored.append(uncensored)[[observations,
censorship]].reset_index(drop=True)
def _ros_sort(df, observations, censorship, warn=False):
"""
This function prepares a dataframe for ROS.
It sorts ascending with
left-censored observations first. Censored observations larger than
the maximum uncensored observations are removed from the dataframe.
Parameters
----------
df : pandas.DataFrame
observations : str
Name of the column in the dataframe that contains observed
values. Censored values should be set to the detection (upper)
limit.
censorship : str
Name of the column in the dataframe that indicates that a
observation is left-censored. (i.e., True -> censored,
False -> uncensored)
Returns
------
sorted_df : pandas.DataFrame
The sorted dataframe with all columns dropped except the
observation and censorship columns.
"""
# separate uncensored data from censored data
censored = sort_values(df[df[censorship]], observations, axis=0)
uncensored = sort_values(df[~df[censorship]], observations, axis=0)
if censored[observations].max() > uncensored[observations].max():
censored = censored[censored[observations] <= uncensored[observations].max()]
if warn:
msg = ("Dropping censored observations greater than "
"the max uncensored observation.")
warnings.warn(msg)
return censored.append(uncensored)[[observations, censorship]].reset_index(drop=True)
print(ols_model.summary())
infl = ols_model.get_influence()
student = infl.summary_frame()['student_resid']
print(student)
print(student.ix[np.abs(student) > 2])
print(infl.summary_frame().ix['minister'])
sidak = ols_model.outlier_test('sidak')
sort_values(sidak, 'unadj_p', inplace=True)
print(sidak)
fdr = ols_model.outlier_test('fdr_bh')
sort_values(fdr, 'unadj_p', inplace=True)
print(fdr)
rlm_model = rlm('prestige ~ income + education', prestige).fit()
print(rlm_model.summary())
print(rlm_model.weights)
ax2 = fig.add_subplot(212, xlabel='Education', ylabel='Prestige')
ax2.scatter(prestige.education, prestige.prestige)
ols_model = ols('prestige ~ income + education', prestige).fit()
print(ols_model.summary())
infl = ols_model.get_influence()
student = infl.summary_frame()['student_resid']
print(student)
print(student.ix[np.abs(student) > 2])
print(infl.summary_frame().ix['minister'])
sidak = ols_model.outlier_test('sidak')
sort_values(sidak, 'unadj_p', inplace=True)
print(sidak)
fdr = ols_model.outlier_test('fdr_bh')
sort_values(fdr, 'unadj_p', inplace=True)
print(fdr)
rlm_model = rlm('prestige ~ income + education', prestige).fit()
print(rlm_model.summary())
print(rlm_model.weights)
#### Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points
# * Data is on the luminosity and temperature of 47 stars in the direction of Cygnus.
def multiOLS(model,
dataframe,
column_list=None,
method='fdr_bh',
alpha=0.05,
subset=None,
model_type=OLS,
**kwargs):
"""apply a linear model to several endogenous variables on a dataframe
Take a linear model definition via formula and a dataframe that will be
the environment of the model, and apply the linear model to a subset
(or all) of the columns of the dataframe. It will return a dataframe
with part of the information from the linear model summary.
Parameters
----------
model : string
formula description of the model
dataframe : pandas.dataframe
dataframe where the model will be evaluated
column_list : list of strings, optional
Names of the columns to analyze with the model.
If None (Default) it will perform the function on all the
eligible columns (numerical type and not in the model definition)
model_type : model class, optional
The type of model to be used. The default is the linear model.
Can be any linear model (OLS, WLS, GLS, etc..)
method: string, optional
the method used to perform the pvalue correction for multiple testing.
default is the Benjamini/Hochberg, other available methods are:
`bonferroni` : one-step correction
`sidak` : on-step correction
`holm-sidak` :
`holm` :
`simes-hochberg` :
`hommel` :
`fdr_bh` : Benjamini/Hochberg
`fdr_by` : Benjamini/Yekutieli
alpha: float, optional
the significance level used for the pvalue correction (default 0.05)
subset: boolean array
the selected rows to be used in the regression
all the other parameters will be directed to the model creation.
Returns
-------
summary : pandas.DataFrame
a dataframe containing an extract from the summary of the model
obtained for each columns. It will give the model complexive f test
result and p-value, and the regression value and standard deviarion
for each of the regressors. The Dataframe has a hierachical column
structure, divided as:
- params: contains the parameters resulting from the models. Has
an additional column named _f_test containing the result of the
F test.
- pval: the pvalue results of the models. Has the _f_test column
for the significativity of the whole test.
- adj_pval: the corrected pvalues via the multitest function.
- std: uncertainties of the model parameters
- statistics: contains the r squared statistics and the adjusted
r squared.
Notes
-----
The main application of this function is on system biology to perform
a linear model testing of a lot of different parameters, like the
different genetic expression of several genes.
See Also
--------
statsmodels.stats.multitest
contains several functions to perform the multiple p-value correction
Examples
--------
Using the longley data as dataframe example
>>> import statsmodels.api as sm
>>> data = sm.datasets.longley.load_pandas()
>>> df = data.exog
>>> df['TOTEMP'] = data.endog
This will perform the specified linear model on all the
other columns of the dataframe
>>> multiOLS('GNP + 1', df)
This select only a certain subset of the columns
>>> multiOLS('GNP + 0', df, ['GNPDEFL', 'TOTEMP', 'POP'])
It is possible to specify a trasformation also on the target column,
conforming to the patsy formula specification
>>> multiOLS('GNP + 0', df, ['I(GNPDEFL**2)', 'center(TOTEMP)'])
It is possible to specify the subset of the dataframe
on which perform the analysis
>> multiOLS('GNP + 1', df, subset=df.GNPDEFL > 90)
Even a single column name can be given without enclosing it in a list
>>> multiOLS('GNP + 0', df, 'GNPDEFL')
"""
# data normalization
# if None take all the numerical columns that aren't present in the model
# it's not waterproof but is a good enough criterion for everyday use
if column_list is None:
column_list = [
name for name in dataframe.columns
if dataframe[name].dtype != object and name not in model
]
# if it's a single string transform it in a single element list
if isinstance(column_list, string_types):
column_list = [column_list]
if subset is not None:
dataframe = dataframe.ix[subset]
# perform each model and retrieve the statistics
col_results = {}
# as the model will use always the same endogenous variables
# we can create them once and reuse
model_exog = dmatrix(model, data=dataframe, return_type="dataframe")
for col_name in column_list:
# it will try to interpret the column name as a valid dataframe
# index as it can be several times faster. If it fails it
# interpret it as a patsy formula (for example for centering)
try:
model_endog = dataframe[col_name]
except KeyError:
model_endog = dmatrix(col_name + ' + 0', data=dataframe)
# retrieve the result and store them
res = _model2dataframe(model_endog, model_exog, model_type, **kwargs)
col_results[col_name] = res
# mangle them togheter and sort by complexive p-value
summary = pd.DataFrame(col_results)
# order by the p-value: the most useful model first!
summary = sort_values(summary.T, [('pvals', '_f_test')])
summary.index.name = 'endogenous vars'
# implementing the pvalue correction method
smt = stats.multipletests
for (key1, key2) in summary:
if key1 != 'pvals':
continue
p_values = summary[key1, key2]
corrected = smt(p_values, method=method, alpha=alpha)[1]
# extend the dataframe of results with the column
# of the corrected p_values
summary['adj_' + key1, key2] = corrected
return summary
def multigroup(pvals, groups, exact=True, keep_all=True, alpha=0.05):
"""Test if the given groups are different from the total partition.
Given a boolean array test if each group has a proportion of positives
different than the complexive proportion.
The test can be done as an exact Fisher test or approximated as a
Chi squared test for more speed.
Parameters
----------
pvals: pandas series of boolean
the significativity of the variables under analysis
groups: dict of list
the name of each category of variables under exam.
each one is a list of the variables included
exact: boolean, optional
If True (default) use the fisher exact test, otherwise
use the chi squared test for contingencies tables.
For high number of elements in the array the fisher test can
be significantly slower than the chi squared.
keep_all: boolean, optional
if False it will drop those groups where the fraction
of positive is below the expected result. If True (default)
it will keep all the significant results.
alpha: float, optional
the significativity level for the pvalue correction
on the whole set of groups (not inside the groups themselves).
Returns
-------
result_df: pandas dataframe
for each group returns:
pvals - the fisher p value of the test
adj_pvals - the adjusted pvals
increase - the log of the odd ratio between the
internal significant ratio versus the external one
_in_sign - significative elements inside the group
_in_non - non significative elements inside the group
_out_sign - significative elements outside the group
_out_non - non significative elements outside the group
Notes
-----
This test allow to see if a category of variables is generally better
suited to be described for the model. For example to see if a predictor
gives more information on demographic or economical parameters,
by creating two groups containing the endogenous variables of each
category.
This function is conceived for medical dataset with a lot of variables
that can be easily grouped into functional groups. This is because
The significativity of a group require a rather large number of
composing elements.
Examples
--------
A toy example on a real dataset, the Guerry dataset from R
>>> url = "http://vincentarelbundock.github.com/"
>>> url = url + "Rdatasets/csv/HistData/Guerry.csv"
>>> df = pd.read_csv(url, index_col='dept')
evaluate the relationship between the variuos paramenters whith the Wealth
>>> pvals = multiOLS('Wealth', df)['adj_pvals', '_f_test']
define the groups
>>> groups = {}
>>> groups['crime'] = ['Crime_prop', 'Infanticide',
... 'Crime_parents', 'Desertion', 'Crime_pers']
>>> groups['religion'] = ['Donation_clergy', 'Clergy', 'Donations']
>>> groups['wealth'] = ['Commerce', 'Lottery', 'Instruction', 'Literacy']
do the analysis of the significativity
>>> multigroup(pvals < 0.05, groups)
"""
pvals = pd.Series(pvals)
if not (set(pvals.unique()) <= set([False, True])):
raise ValueError("the series should be binary")
if hasattr(pvals.index, 'is_unique') and not pvals.index.is_unique:
raise ValueError("series with duplicated index is not accepted")
results = {
'pvals': {},
'increase': {},
'_in_sign': {},
'_in_non': {},
'_out_sign': {},
'_out_non': {}
}
for group_name, group_list in iteritems(groups):
res = _test_group(pvals, group_name, group_list, exact)
results['pvals'][group_name] = res[0]
results['increase'][group_name] = res[1]
results['_in_sign'][group_name] = res[2][0]
results['_in_non'][group_name] = res[2][1]
results['_out_sign'][group_name] = res[2][2]
results['_out_non'][group_name] = res[2][3]
result_df = sort_values(pd.DataFrame(results), 'pvals')
if not keep_all:
result_df = result_df[result_df.increase]
smt = stats.multipletests
corrected = smt(result_df['pvals'], method='fdr_bh', alpha=alpha)[1]
result_df['adj_pvals'] = corrected
return result_df
def multiOLS(model, dataframe, column_list=None, method='fdr_bh',
alpha=0.05, subset=None, model_type=OLS, **kwargs):
"""apply a linear model to several endogenous variables on a dataframe
Take a linear model definition via formula and a dataframe that will be
the environment of the model, and apply the linear model to a subset
(or all) of the columns of the dataframe. It will return a dataframe
with part of the information from the linear model summary.
Parameters
----------
model : string
formula description of the model
dataframe : pandas.dataframe
dataframe where the model will be evaluated
column_list : list of strings, optional
Names of the columns to analyze with the model.
If None (Default) it will perform the function on all the
eligible columns (numerical type and not in the model definition)
model_type : model class, optional
The type of model to be used. The default is the linear model.
Can be any linear model (OLS, WLS, GLS, etc..)
method: string, optional
the method used to perform the pvalue correction for multiple testing.
default is the Benjamini/Hochberg, other available methods are:
`bonferroni` : one-step correction
`sidak` : on-step correction
`holm-sidak` :
`holm` :
`simes-hochberg` :
`hommel` :
`fdr_bh` : Benjamini/Hochberg
`fdr_by` : Benjamini/Yekutieli
alpha: float, optional
the significance level used for the pvalue correction (default 0.05)
subset: boolean array
the selected rows to be used in the regression
all the other parameters will be directed to the model creation.
Returns
-------
summary : pandas.DataFrame
a dataframe containing an extract from the summary of the model
obtained for each columns. It will give the model complexive f test
result and p-value, and the regression value and standard deviarion
for each of the regressors. The Dataframe has a hierachical column
structure, divided as:
- params: contains the parameters resulting from the models. Has
an additional column named _f_test containing the result of the
F test.
- pval: the pvalue results of the models. Has the _f_test column
for the significativity of the whole test.
- adj_pval: the corrected pvalues via the multitest function.
- std: uncertainties of the model parameters
- statistics: contains the r squared statistics and the adjusted
r squared.
Notes
-----
The main application of this function is on system biology to perform
a linear model testing of a lot of different parameters, like the
different genetic expression of several genes.
See Also
--------
statsmodels.stats.multitest
contains several functions to perform the multiple p-value correction
Examples
--------
Using the longley data as dataframe example
>>> import statsmodels.api as sm
>>> data = sm.datasets.longley.load_pandas()
>>> df = data.exog
>>> df['TOTEMP'] = data.endog
This will perform the specified linear model on all the
other columns of the dataframe
>>> multiOLS('GNP + 1', df)
This select only a certain subset of the columns
>>> multiOLS('GNP + 0', df, ['GNPDEFL', 'TOTEMP', 'POP'])
It is possible to specify a trasformation also on the target column,
conforming to the patsy formula specification
>>> multiOLS('GNP + 0', df, ['I(GNPDEFL**2)', 'center(TOTEMP)'])
It is possible to specify the subset of the dataframe
on which perform the analysis
>> multiOLS('GNP + 1', df, subset=df.GNPDEFL > 90)
Even a single column name can be given without enclosing it in a list
>>> multiOLS('GNP + 0', df, 'GNPDEFL')
"""
# data normalization
# if None take all the numerical columns that aren't present in the model
# it's not waterproof but is a good enough criterion for everyday use
if column_list is None:
column_list = [name for name in dataframe.columns
if dataframe[name].dtype != object and name not in model]
# if it's a single string transform it in a single element list
if isinstance(column_list, string_types):
column_list = [column_list]
if subset is not None:
dataframe = dataframe.loc[subset]
# perform each model and retrieve the statistics
col_results = {}
# as the model will use always the same endogenous variables
# we can create them once and reuse
model_exog = dmatrix(model, data=dataframe, return_type="dataframe")
for col_name in column_list:
# it will try to interpret the column name as a valid dataframe
# index as it can be several times faster. If it fails it
# interpret it as a patsy formula (for example for centering)
try:
model_endog = dataframe[col_name]
except KeyError:
model_endog = dmatrix(col_name + ' + 0', data=dataframe)
# retrieve the result and store them
res = _model2dataframe(model_endog, model_exog, model_type, **kwargs)
col_results[col_name] = res
# mangle them togheter and sort by complexive p-value
summary = pd.DataFrame(col_results)
# order by the p-value: the most useful model first!
summary = sort_values(summary.T, [('pvals', '_f_test')])
summary.index.name = 'endogenous vars'
# implementing the pvalue correction method
smt = stats.multipletests
for (key1, key2) in summary:
if key1 != 'pvals':
continue
p_values = summary[key1, key2]
corrected = smt(p_values, method=method, alpha=alpha)[1]
# extend the dataframe of results with the column
# of the corrected p_values
summary['adj_' + key1, key2] = corrected
return summary
def multigroup(pvals, groups, exact=True, keep_all=True, alpha=0.05):
"""Test if the given groups are different from the total partition.
Given a boolean array test if each group has a proportion of positives
different than the complexive proportion.
The test can be done as an exact Fisher test or approximated as a
Chi squared test for more speed.
Parameters
----------
pvals: pandas series of boolean
the significativity of the variables under analysis
groups: dict of list
the name of each category of variables under exam.
each one is a list of the variables included
exact: boolean, optional
If True (default) use the fisher exact test, otherwise
use the chi squared test for contingencies tables.
For high number of elements in the array the fisher test can
be significantly slower than the chi squared.
keep_all: boolean, optional
if False it will drop those groups where the fraction
of positive is below the expected result. If True (default)
it will keep all the significant results.
alpha: float, optional
the significativity level for the pvalue correction
on the whole set of groups (not inside the groups themselves).
Returns
-------
result_df: pandas dataframe
for each group returns:
pvals - the fisher p value of the test
adj_pvals - the adjusted pvals
increase - the log of the odd ratio between the
internal significant ratio versus the external one
_in_sign - significative elements inside the group
_in_non - non significative elements inside the group
_out_sign - significative elements outside the group
_out_non - non significative elements outside the group
Notes
-----
This test allow to see if a category of variables is generally better
suited to be described for the model. For example to see if a predictor
gives more information on demographic or economical parameters,
by creating two groups containing the endogenous variables of each
category.
This function is conceived for medical dataset with a lot of variables
that can be easily grouped into functional groups. This is because
The significativity of a group require a rather large number of
composing elements.
Examples
--------
A toy example on a real dataset, the Guerry dataset from R
>>> url = "https://raw.githubusercontent.com/vincentarelbundock/"
>>> url = url + "Rdatasets/csv/HistData/Guerry.csv"
>>> df = pd.read_csv(url, index_col='dept')
evaluate the relationship between the variuos paramenters whith the Wealth
>>> pvals = multiOLS('Wealth', df)['adj_pvals', '_f_test']
define the groups
>>> groups = {}
>>> groups['crime'] = ['Crime_prop', 'Infanticide',
... 'Crime_parents', 'Desertion', 'Crime_pers']
>>> groups['religion'] = ['Donation_clergy', 'Clergy', 'Donations']
>>> groups['wealth'] = ['Commerce', 'Lottery', 'Instruction', 'Literacy']
do the analysis of the significativity
>>> multigroup(pvals < 0.05, groups)
"""
pvals = pd.Series(pvals)
if not (set(pvals.unique()) <= set([False, True])):
raise ValueError("the series should be binary")
if hasattr(pvals.index, 'is_unique') and not pvals.index.is_unique:
raise ValueError("series with duplicated index is not accepted")
results = {'pvals': {},
'increase': {},
'_in_sign': {},
'_in_non': {},
'_out_sign': {},
'_out_non': {}}
for group_name, group_list in iteritems(groups):
res = _test_group(pvals, group_name, group_list, exact)
results['pvals'][group_name] = res[0]
results['increase'][group_name] = res[1]
results['_in_sign'][group_name] = res[2][0]
results['_in_non'][group_name] = res[2][1]
results['_out_sign'][group_name] = res[2][2]
results['_out_non'][group_name] = res[2][3]
result_df = sort_values(pd.DataFrame(results), 'pvals')
if not keep_all:
result_df = result_df[result_df.increase]
smt = stats.multipletests
corrected = smt(result_df['pvals'], method='fdr_bh', alpha=alpha)[1]
result_df['adj_pvals'] = corrected
return result_df
