Statistical package in Python based on Pandas

I have a large dataset of 500k rows and 74 columns. Whenever I try to use a pairwise partial correlation I get the following error:

----> 1 data2.pairwise_corr(covar = 'SEX')

~/.local/lib/python3.7/site-packages/pandas_flavor/register.py in __call__(self, *args, **kwargs)
     27             @wraps(method)
     28             def __call__(self, *args, **kwargs):
---> 29                 return method(self._obj, *args, **kwargs)
     30 
     31         register_dataframe_accessor(method.__name__)(AccessorMethod)

~/.local/lib/python3.7/site-packages/pingouin/pairwise.py in pairwise_corr(data, columns, covar, tail, method, padjust, nan_policy)
   1229         else:
   1230             cor_st = partial_corr(data=data, x=col1, y=col2, covar=covar,
-> 1231                                   tail=tail, method=method)
   1232         cor_st_keys = cor_st.columns.tolist()
   1233 

~/.local/lib/python3.7/site-packages/pingouin/correlation.py in partial_corr(data, x, y, covar, x_covar, y_covar, tail, method, **kwargs)
    783         # PARTIAL CORRELATION
    784         cvar = np.atleast_2d(C[covar].to_numpy())
--> 785         beta_x = np.linalg.lstsq(cvar, C[x].to_numpy(), rcond=None)[0]
    786         beta_y = np.linalg.lstsq(cvar, C[y].to_numpy(), rcond=None)[0]
    787         res_x = C[x].to_numpy() - cvar @ beta_x

<__array_function__ internals> in lstsq(*args, **kwargs)

/curc/sw/anaconda3/2019.07/envs/jupyterlab2/lib/python3.7/site-packages/numpy/linalg/linalg.py in lstsq(a, b, rcond)
   2257         # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
   2258         b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
-> 2259     x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
   2260     if m == 0:
   2261         x[...] = 0

ValueError: On entry to DLASCL parameter number 4 had an illegal value

This is even with 12 cores running, is there any way to resolve this or is the pairwise correlation with a covariate just not compatible with a large dataset? pcorr() and pairwise_corr() work as methods on the dataframe.

As a test, I generated correlated variables x and y. I then set x_test to x and ran a semi-partial correlation of xtest against y, controlling for the effect x on y. As one would expect, the pearson semi-partial correlation of this is zero. However, when applying Spearman and Kendall, the result is non-zero.

The Spearman and Kendall result was 0.14 and 0.11 respectively. Although it is low using the following dummy dataset, the data I am working with produces a very high semi-partial correlation around 0.8 which does not make sense.

df=pd.DataFrame({"y":[1,2,3,4,5,6,7,8,9,10],"x":[1,3,5,8,7,10,13,14,18,21]})
print(df.corr())
df['x_test']=df['x']
partial_corr(data=df,x='x_test',y='y',y_covar=['x'], 
             method='spearman')['r'].values

Out:

          y         x
y  1.000000  0.985318
x  0.985318  1.000000

array([0.13939394])

Currently pingouin.normality() silently discards the W statistic returned by scipy.stats.shapiro(). I would love to have an option to retrieve these stats, as I need to present them together with the p values when drafting a scientific publication. I wonder if the function could not be adjusted to return a DataFrame with numerous information like most of the other pingouin functions?

The entire library is absolutely amazing; thank you for developing it. I came up with a minor improvement for the "Shift plot". No matter what data array we provide, this graphic shows X and Y. There may be disagreement about its use in academic papers. I believed that by defining X and Y, this conflict would be addressed. I created an edit for this where the user can configure the X and Y variables as well as the plot title. The labels being on the Y axis creates problems with the size of the figure, thus I located them as a legend in the plot.

I have ideas for other modules and would like to add them when I have the time. My initial contribution would be adding the odds ratio and CI-range values to the chi-sqr module.

Here my test code; plotting_test.zip

closes #130

For now I opted to only return the coefficients in case a rank deficient design matrix is detected.

The basic error of pingouin is its inability to handle the residues return parameter of lstsq whenever the design matrix is rank deficient. In that case, residues is an empty array array([], dtype=float64) instead of a float.

One option apart from warning and simply returning the coefficients only could be to "revive" the currently commented line from line 413(old) / 422(new): # ss_res = (resid ** 2).sum()

Something like:

    # FIT (WEIGHTED) LEAST SQUARES REGRESSION USING SCIPY.LINALG.LSTST
    coef, ss_res, rank, _ = lstsq(Xw, yw)
    if coef_only:
        return coef
    calc_ss_res = False
    if rank < Xw.shape[1]:
        warnings.warn('Design matrix supplied with `X` parameter is rank '
                      f'deficient (rank {rank} with {Xw.shape[1]} columns. '
                      'That means that one or more of the columns in `X` '
                      'are a linear combination of one of more of the '
                      'other columns.'')
        calc_ss_res = True

    # Degrees of freedom
    df_model = rank - constant
    df_resid = n - p
    # Calculate predicted values and (weighted) residuals
    pred = Xw @ coef
    resid = yw - pred
    if calc_ss_res:
        ss_res = (resid ** 2).sum()

I played around with that, and it seems to be producing the same results as statsmodels. Try for yourself if you'd like:

import numpy as np 
import pingouin 
import statsmodels.api as sm 

# make up some random data and a design matrix for multiple linear regression 
n = 100  
y = np.random.randn(n) 
X = np.vstack([np.random.permutation([0, 1, 0, 0, 0]) for i in range(n)]) 

# try with pingouin (use patch from code example above)
results_pingouin = pingouin.linear_regression(X, y, add_intercept=True) 
print(results_pingouin)

# try with statsmodels
X_with_intercept = sm.add_constant(X) 
model = sm.OLS(endog=y, exog=X_with_intercept) 
results_sm = model.fit() 
print(results_sm.summary())

# compare residuals
np.allclose(results_pingouin.residuals_, results_sm.resid)  # True
np.allclose(results_pingouin.coef.to_numpy(), results_sm.params)  # True

Let me know what you think :-)

I updated doc of pairwise_corr to reflect more clearly that a partial corr is only calculated, if covariates are specified.

Furthermore I implemented that combinations of columns, which are also present in covars, will not be dropped. Instead the x and y columns will be removed from covar only for this specific combination. This allows for automatically controlling for all other columns in the dataframe while calculating the pairwise partial correlation for all combinations.

If this means that there are not covars for a combination, an empty list will be returned for stats['covar'] and via partial_corr line 744 a standard non-partial corr will be returned. Is this clear enough for the user or should "empty" covar rows be dropped instead?

Closes #123

Some basic examples of the funcitonality (tests are included in the respective test file)

import numpy as np
import pandas as pd

import pingouin as pg

df = pd.DataFrame(
    data=np.random.rand(200, 4), columns=['a', 'b', 'c', 'd'])
df['e'] = df['a']**2
df['f'] = df['a'] * df['b']
df['g'] = df['c'] + np.random.randint(0, 5, 200)
df['h'] = np.sqrt(df['d'] + 5) - df['b']**3

# Get pairwise corr for all available covars per combination, also try nan policy
pwc = pg.pairwise_corr(data=df, covar=df.columns)
pwc_lwnan = pg.pairwise_corr(data=df, covar=df.columns, nan_policy='listwise')
# assert that both are equal
assert (
    pwc.drop(columns=['CI95%']) == pwc_lwnan.drop(columns=['CI95%'])
).all().all()

# Now the same with a MultiIndex:
mi = pd.MultiIndex.from_product((
    ('a', 'b', 'c', 'd'), ('u', 'v'),
))
df_mi = df.copy()
df_mi.columns = mi

pwc_mi = pg.pairwise_corr(data=df_mi, covar=df_mi.columns)
# Assert that the results are equal to non-multiindex tests
assert (
    pwc_mi.drop(
        columns=['X', 'Y', 'covar', 'CI95%']) == pwc.drop(
            columns=['X', 'Y', 'covar', 'CI95%'])
).all().all()
# and compare to nan policy listwise
pwc_mi_lwnan = pg.pairwise_corr(
    data=df_mi, covar=df_mi.columns, nan_policy='listwise'
)
assert (
    pwc_mi.drop(
        columns=['X', 'Y', 'covar', 'CI95%']) == pwc.drop(
            columns=['X', 'Y', 'covar', 'CI95%'])
).all().all()

And to show what I mean with "empty covars" (taken from test_pairwise.py):

from pingouin import pairwise_corr, read_dataset

data = read_dataset('pairwise_corr').iloc[:, 1:]
n = data.shape[0]
data['Age'] = np.random.randint(18, 65, n)
data['IQ'] = np.random.normal(105, 1, n)
data['One'] = 1
data['Gender'] = np.repeat(['M', 'F'], int(n / 2))

# see row 4 for empty covar
pwc_empty = pairwise_corr(data, columns='Neuroticism', covar='Age')
print(pwc_empty.covar)
# or fully empty covar
pwc_fully_empty = pairwise_corr(data, columns=['Neuroticism', 'Age'], covar='Age')

Hi there, Thanks for providing this awesome library. I picked it up to run an rm_corr test on some data but I'm having issues.

I have 1000 individuals for each of whom I have 8 paired measurements. I have uploaded a pickled dataframe that causes the issue to GDrive (https://drive.google.com/open?id=1gkvGKBth2J8EhmhblsWRdK-eCDlk8zJk).

Here's a snapshot of what the dataframe looks like:

You can reproduce the issue by running:

df = pd.read_pickle('bad_df.pkl')
rm_corr(data=df, x='m2', y='m1', subject='id')

which yields the error

AssertionError                            Traceback (most recent call last)
<ipython-input-156-86b16d57ef4b> in <module>
----> 1 rm_corr(data=df, x='m2', y='m1', subject='id')

.../lib/python3.7/site-packages/pingouin/correlation.py in rm_corr(data, x, y, subject, tail)
    981     pval = pval * 0.5 if tail == 'one-sided' else pval
    982     ci = compute_esci(stat=rm, nx=n, eftype='pearson').tolist()
--> 983     pwr = power_corr(r=rm, n=n, tail=tail)
    984     # Convert to Dataframe
    985     stats = pd.DataFrame({"r": round(rm, 3), "dof": int(dof),

.../lib/python3.7/site-packages/pingouin/power.py in power_corr(r, n, power, alpha, tail)
    902     # Safety checks
    903     if r is not None:
--> 904         assert -1 <= r <= 1
    905         r = abs(r)
    906     if alpha is not None:

If I drop into a debugger I can see that r = 1.0000184067367273, perhaps this is due to numerical stability issues?

The following reproducer:

from pandas import DataFrame
from random import seed, sample

import pingouin as pg

seed(0, 1)
sample_size = 100
random_columns = 60

n = [0.0000001 for _ in range(sample_size)]
df = DataFrame(dict(
    n1b=n, n2b=n, n3b=n,
    **{
        f'x{i}': sample(range(2000), sample_size)
        for i in range(random_columns)
    }
))

pg.pcorr(df).max().max()

gives a value of 6.754645467173336 with pingouin 0.4.0. I encountered this behaviour (giving out of range coefficients) in the wild when I accidentally forgot to remove variables with no variance.

To ensure that incorrect results like that don't get published, I would:

• add a check on the data verifying that var() != 0 prior to running computations; not sure if this should raise or warn
• add a check on the results; if any of the coefficients are out of range it would warn and provide name of the column those come from and a helpful message like "your data may have close-to-zero variance variables or be otherwise ill-conditioned".

It might be also worth investigating if there is a way to workaround these numerical issues, but the two points above should address the most urgent worries.

I think this line in correlation.py:

dis[i, :] = np.linalg.norm(B * B2[i, :] / bot[i], axis=1)

is functionally different compared to the Matlab implementation and the equation in Wilcox, R. (2004)

This PR is a work in progress.

I'm not sure whether there is a general guideline about returning statistics as formatted strings, rather than as float, but I find it very useful to use the raw float values, rather than get strings. This PR adds this functionality, at least to the bayesian module (not sure what the situation is in other modules).

This PR leaves the default behaviour of returning strings untouched. It could be argued that returning the raw float values should be default, and formatting left to the user in general (or some utility function perhaps). I'm interested to hear thoughts on this as well.

If you think in principle this PR is a good idea, I'll update and add proper documentation etc.

I’m helping to update the python-pingouin package in Fedora Linux. One test, TestParametric::test_pandas, is failing on 32-bit platforms (i686 and armv7hl). I can’t easily tell if the actual problem here is in pingouin, pandas, or somewhere else (scipy, pandas-flavor, …).

=================================== FAILURES ===================================
__________________________ TestParametric.test_pandas __________________________
self = <pingouin.tests.test_pandas.TestParametric testMethod=test_pandas>
    def test_pandas(self):
        """Test pandas method.
        """
        # Test the ANOVA (Pandas)
        aov = df.anova(dv='Scores', between='Group', detailed=True)
        assert aov.equals(pg.anova(dv='Scores', between='Group', detailed=True,
                                   data=df))
        aov3_ss1 = df_aov3.anova(dv='Cholesterol', between=['Sex', 'Drug'],
                                 ss_type=1)
        aov3_ss2 = df_aov3.anova(dv='Cholesterol', between=['Sex', 'Drug'],
                                 ss_type=2)
        aov3_ss2_pg = pg.anova(dv='Cholesterol', between=['Sex', 'Drug'],
                               data=df_aov3, ss_type=2)
        assert not aov3_ss1.equals(aov3_ss2)
        assert aov3_ss2.round(3).equals(aov3_ss2_pg.round(3))
    
        # Test the Welch ANOVA (Pandas)
        aov = df.welch_anova(dv='Scores', between='Group')
        assert aov.equals(pg.welch_anova(dv='Scores', between='Group',
                                         data=df))
    
        # Test the ANCOVA
        aov = df_anc.ancova(dv='Scores', covar='Income',
                            between='Method').round(3)
        assert aov.equals(pg.ancova(data=df_anc, dv='Scores', covar='Income',
                          between='Method').round(3))
    
        # Test the repeated measures ANOVA (Pandas)
        aov = df.rm_anova(dv='Scores', within='Time', subject='Subject',
                          detailed=True)
        assert aov.equals(pg.rm_anova(dv='Scores', within='Time',
                                      subject='Subject',
                                      detailed=True, data=df))
    
        # FDR-corrected post hocs with Hedges'g effect size
        ttests = df.pairwise_ttests(dv='Scores', within='Time',
                                    subject='Subject', padjust='fdr_bh',
                                    effsize='hedges')
        assert ttests.equals(pg.pairwise_ttests(dv='Scores', within='Time',
                                                subject='Subject',
                                                padjust='fdr_bh',
                                                effsize='hedges', data=df))
    
        # Pairwise Tukey
        tukey = df.pairwise_tukey(dv='Scores', between='Group')
        assert tukey.equals(pg.pairwise_tukey(data=df, dv='Scores',
                                              between='Group'))
    
        # Test two-way mixed ANOVA
        aov = df.mixed_anova(dv='Scores', between='Group', within='Time',
                             subject='Subject', correction=False)
        assert aov.equals(pg.mixed_anova(dv='Scores', between='Group',
                                         within='Time',
                                         subject='Subject', correction=False,
                                         data=df))
    
        # Test parwise correlations
        corrs = data.pairwise_corr(columns=['X', 'M', 'Y'], method='spearman')
        corrs2 = pg.pairwise_corr(data=data, columns=['X', 'M', 'Y'],
                                  method='spearman')
        assert corrs['r'].equals(corrs2['r'])
    
        # Test partial correlation
        corrs = data.partial_corr(x='X', y='Y', covar='M', method='spearman')
        corrs2 = pg.partial_corr(x='X', y='Y', covar='M', method='spearman',
                                 data=data)
        assert corrs['r'].equals(corrs2['r'])
    
        # Test partial correlation matrix (compare with the ppcor package)
        corrs = data.iloc[:, :5].pcorr().round(3)
        np.testing.assert_array_equal(corrs.iloc[0, :].to_numpy(),
                                      [1, 0.392, 0.06, -0.014, -0.149])
        # Now compare against Pingouin's own partial_corr function
        corrs = data[['X', 'Y', 'M']].pcorr()
        corrs2 = data.partial_corr(x='X', y='Y', covar='M')
        assert np.isclose(corrs.at['X', 'Y'], corrs2.at['pearson', 'r'])
    
        # Test rcorr (correlation matrix with p-values)
        # We compare against Pingouin pairwise_corr function
        corrs = df_corr.rcorr(padjust='holm', decimals=4)
        corrs2 = df_corr.pairwise_corr(padjust='holm').round(4)
        assert corrs.at['Neuroticism', 'Agreeableness'] == '*'
        assert (corrs.at['Agreeableness', 'Neuroticism'] ==
                str(corrs2.at[2, 'r']))
        corrs = df_corr.rcorr(padjust='holm', stars=False, decimals=4)
        assert (corrs.at['Neuroticism', 'Agreeableness'] ==
                str(corrs2.at[2, 'p-corr'].round(4)))
        corrs = df_corr.rcorr(upper='n', decimals=5)
        corrs2 = df_corr.pairwise_corr().round(5)
        assert corrs.at['Extraversion', 'Openness'] == corrs2.at[4, 'n']
        assert corrs.at['Openness', 'Extraversion'] == str(corrs2.at[4, 'r'])
        # Method = spearman does not work with Python 3.5 on Travis?
        # Instead it seems to return the Pearson correlation!
>       df_corr.rcorr(method='spearman')
aov        =         Source        SS  DF1  DF2  ...         F     p-unc       np2       eps
0        Group  5.459963    1   58  ......064929  0.998751
2  Interaction  5.167192    2  116  ...  2.727996  0.069545  0.044922       NaN
[3 rows x 9 columns]
aov3_ss1   =        Source         SS    DF        MS         F     p-unc       np2
0         Sex   3.362769   1.0  3.362769  3.461...62   2.0  0.561181  0.577636  0.564168  0.018007
3    Residual  61.205378  63.0  0.971514       NaN       NaN       NaN
aov3_ss2   =        Source         SS    DF        MS         F     p-unc       np2
0         Sex   3.419789   1.0  3.419789  3.520...62   2.0  0.561181  0.577636  0.564168  0.018007
3    Residual  61.205378  63.0  0.971514       NaN       NaN       NaN
aov3_ss2_pg =        Source         SS    DF        MS         F     p-unc       np2
0         Sex   3.419789   1.0  3.419789  3.520...62   2.0  0.561181  0.577636  0.564168  0.018007
3    Residual  61.205378  63.0  0.971514       NaN       NaN       NaN
corrs      =                   Neuroticism Extraversion  ... Agreeableness Conscientiousness
Neuroticism                 -         ...              500
Conscientiousness    -0.36801      0.06459  ...       0.15867                 -
[5 rows x 5 columns]
corrs2     =                X                  Y   method  ...    p-unc       BF10    power
0    Neuroticism       Extraversion  pe...  0.059  0.06035
9  Agreeableness  Conscientiousness  pearson  ...  0.00037     31.243  0.94638
[10 rows x 10 columns]
self       = <pingouin.tests.test_pandas.TestParametric testMethod=test_pandas>
ttests     =   Contrast        A        B  Paired  ...    p-corr  p-adjust   BF10    hedges
0     Time   August  January    True  ....  4.232 -0.482547
2     Time  January     June    True  ...  0.310194    fdr_bh  0.232 -0.169520
[3 rows x 13 columns]
tukey      =          A           B   mean(A)  ...         T   p-tukey    hedges
0  Control  Meditation  5.567851  ... -2.289903  0.023202 -0.339918
[1 rows x 9 columns]
pingouin/tests/test_pandas.py:114: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.10/site-packages/pandas_flavor/register.py:29: in __call__
    return method(self._obj, *args, **kwargs)
        args       = ()
        kwargs     = {'method': 'spearman'}
        method     = <function rcorr at 0xbb3880b8>
        self       = <pandas_flavor.register.register_dataframe_method.<locals>.inner.<locals>.AccessorMethod object at 0xa8881f10>
pingouin/correlation.py:1050: in rcorr
    mat = self.corr(method=method).round(decimals)
        decimals   = 3
        ffp        = <function format_float_positional at 0xc3ab3028>
        method     = 'spearman'
        padjust    = None
        pearsonr   = <function pearsonr at 0xbbb59e38>
        pval_stars = {0.001: '***', 0.01: '**', 0.05: '*'}
        self       =      Neuroticism  Extraversion  Openness  Agreeableness  Conscientiousness
0        2.47917       4.20833   3.93750   ...3            3.39583
499      2.54167       3.56250   3.14583        3.45833            2.89583
[500 rows x 5 columns]
        spearmanr  = <function spearmanr at 0xbbb5f388>
        stars      = True
        tif        = <function triu_indices_from at 0xc39efcd0>
        upper      = 'pval'
/usr/lib/python3.10/site-packages/pandas/core/frame.py:9376: in corr
    correl = libalgos.nancorr_spearman(mat, minp=min_periods)
        cols       = Index(['Neuroticism', 'Extraversion', 'Openness', 'Agreeableness',
       'Conscientiousness'],
      dtype='object')
        idx        = Index(['Neuroticism', 'Extraversion', 'Openness', 'Agreeableness',
       'Conscientiousness'],
      dtype='object')
        mat        = array([[2.47917, 4.20833, 3.9375 , 3.95833, 3.45833],
       [2.60417, 3.1875 , 3.95833, 3.39583, 3.22917],
       [2.... 3.     ],
       [2.08333, 3.66667, 3.58333, 3.45833, 3.39583],
       [2.54167, 3.5625 , 3.14583, 3.45833, 2.89583]])
        method     = 'spearman'
        min_periods = 1
        numeric_df =      Neuroticism  Extraversion  Openness  Agreeableness  Conscientiousness
0        2.47917       4.20833   3.93750   ...3            3.39583
499      2.54167       3.56250   3.14583        3.45833            2.89583
[500 rows x 5 columns]
        self       =      Neuroticism  Extraversion  Openness  Agreeableness  Conscientiousness
0        2.47917       4.20833   3.93750   ...3            3.39583
499      2.54167       3.56250   3.14583        3.45833            2.89583
[500 rows x 5 columns]
pandas/_libs/algos.pyx:415: in pandas._libs.algos.nancorr_spearman
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: Buffer dtype mismatch, expected 'const intp_t' but got 'long long'
pandas/_libs/algos.pyx:938: ValueError

On 32-bit Linux platforms, long long is normally 64-bit but intp_t is 32-bit, which explains what is happening but not why it is happening.

I can easily do any tests that are needed on these platforms to try to figure out where the root issue is, but some ideas regarding what to look for would be very helpful.

The pairwise_tests module has a return_desc option that is suitable for parametric data. However, if non-parametric data is being used, medians and IQRs can be returned by setting the parametric parameter to False. I am willing to implement this feature in the relevant file at the provided GitHub link if it is desired. code line:

I believe it would be a great addition to the package! Generally, when reporting GG corrected p-values, the corrected F value and ddofs are also reported. It is also a relatively easy addition. (...) if correction: (...)
corr_fval = f(corr_ddof1, corr_ddof2).ppf(1-p_corr)

then during the formation of dataframe: if not detailed: (....) aov["F"] = corr_fval aov["DF"] = [corr_ddof1,corr_ddof2] else: (...) aov["DF"] = [corr_ddof1,corr_ddof2] aov["F"] = [corr_fval,np.nan] (...)

Hi,

1. Intro

As mentioned in discussion #296, it would be convenient to have support for both proportion and mean z tests within pingouin.

statsmodels already provides several methods around those tests but in a confused and dispersed way, so you often end up writing some wrapper. Moreover, those methods sometimes have strange signatures, making it not really straightforward to use. A single and richer pandas output would be far more helpful. We are talking about basics here: in my opinion it would strengthen pingouin's position as a user-friendly but powerful and complete statistical package.

2. Ressources

• statsmodels

  • Proportions
    • z_stat and p_value: https://www.statsmodels.org/devel/generated/statsmodels.stats.proportion.proportions_ztest.html
    • diff CI: https://www.statsmodels.org/devel/generated/statsmodels.stats.proportion.confint_proportions_2indep.html
    • power: https://www.statsmodels.org/devel/generated/statsmodels.stats.proportion.power_proportions_2indep.html
    • effect size: https://www.statsmodels.org/dev/generated/statsmodels.stats.proportion.proportion_effectsize.html
  • Means
    • z_stat and p_value: https://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.ztest.html?highlight=ztest#statsmodels.stats.weightstats.ztest OR https://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.CompareMeans.ztest_ind.html#statsmodels.stats.weightstats.CompareMeans.ztest_ind
    • diff CI: https://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.zconfint.html?highlight=ztest OR https://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.CompareMeans.zconfint_diff.html#statsmodels.stats.weightstats.CompareMeans.zconfint_diff

• R

  • Means
    • Paired Z test: https://rpubs.com/nguyenminhsang/paired_z-test

3. Feature

• proportions_ztest()

  • parameters

    • x1: 2-column array_like, 1st column: number of trials, 2nd column: number of successes
    • x2: same or proportion value
    • alternative
    • paired
    • r
    • confidence
    • method: method for computing confidence interval, ‘newcomb’ (default), ‘wald’, ‘agresti-caffo’, ‘score’

  • returns

    • Z
    • alternative
    • p_val
    • CI95%: diff in proportion
    • cohen-d
    • BF10
    • power

• means_ztest()

  • parameters
    • x1: array_like
    • x2: same or mean value
    • alternative
    • paired
    • r
    • confidence
  • returns
    • Z
    • alternative
    • p_val
    • CI95%: diff in means
    • cohen-d
    • BF10
    • power

Thanks! Aurélien

The following issues should be addressed:

• [x] #308 ❗
• [ ] #153
• [ ] #253
• [ ] #208
• [ ] #225
• [ ] #118

The following PR should be merged:

• [ ] #226
• [x] #291

The following dependencies should be updated:

• [x] Scikit-learn <1.1.2 (revert temporary fix in #278)

The following deprecation should be made:

Hello, is there an implementation for Bayesian credible intervals as there is for frequentist confidence intervals?

https://en.wikipedia.org/wiki/Credible_interval

Helpful Literature:

1. https://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/