import itertools
import warnings
import numpy as np
import pandas as pd
import pandas_flavor as pf
from scipy.stats import t, norm
from scipy.linalg import pinvh, lstsq
from pingouin.config import options
from pingouin.utils import remove_na as rm_na
from pingouin.utils import _flatten_list as _fl
from pingouin.utils import _postprocess_dataframe
__all__ = ["linear_regression", "logistic_regression", "mediation_analysis"]
[docs]
def linear_regression(
X,
y,
add_intercept=True,
weights=None,
coef_only=False,
alpha=0.05,
as_dataframe=True,
remove_na=False,
relimp=False,
):
"""(Multiple) Linear regression.
Parameters
----------
X : array_like
Predictor(s), of shape *(n_samples, n_features)* or *(n_samples)*.
y : array_like
Dependent variable, of shape *(n_samples)*.
add_intercept : bool
If False, assume that the data are already centered. If True, add a
constant term to the model. In this case, the first value in the
output dict is the intercept of the model.
.. note:: It is generally recommended to include a constant term
(intercept) to the model to limit the bias and force the residual
mean to equal zero. The intercept coefficient and p-values
are however rarely meaningful.
weights : array_like
An optional vector of sample weights to be used in the fitting
process, of shape *(n_samples)*. Missing or negative weights are not
allowed. If not null, a weighted least squares is calculated.
.. versionadded:: 0.3.5
coef_only : bool
If True, return only the regression coefficients.
alpha : float
Alpha value used for the confidence intervals.
:math:`\\text{CI} = [\\alpha / 2 ; 1 - \\alpha / 2]`
as_dataframe : bool
If True, returns a pandas DataFrame. If False, returns a dictionnary.
remove_na : bool
If True, apply a listwise deletion of missing values (i.e. the entire
row is removed). Default is False, which will raise an error if missing
values are present in either the predictor(s) or dependent
variable.
relimp : bool
If True, returns the relative importance (= contribution) of
predictors. This is irrelevant when the predictors are uncorrelated:
the total :math:`R^2` of the model is simply the sum of each univariate
regression :math:`R^2`-values. However, this does not apply when
predictors are correlated. Instead, the total :math:`R^2` of the model
is partitioned by averaging over all combinations of predictors,
as done in the `relaimpo
<https://cran.r-project.org/web/packages/relaimpo/relaimpo.pdf>`_
R package (``calc.relimp(type="lmg")``).
.. warning:: The computation time roughly doubles for each
additional predictor and therefore this can be extremely slow for
models with more than 12-15 predictors.
.. versionadded:: 0.3.0
Returns
-------
stats : :py:class:`pandas.DataFrame` or dict
Linear regression summary:
* ``'names'``: name of variable(s) in the model (e.g. x1, x2...)
* ``'coef'``: regression coefficients
* ``'se'``: standard errors
* ``'T'``: T-values
* ``'pval'``: p-values
* ``'r2'``: coefficient of determination (:math:`R^2`)
* ``'adj_r2'``: adjusted :math:`R^2`
* ``'CI[2.5%]'``: lower confidence intervals
* ``'CI[97.5%]'``: upper confidence intervals
* ``'relimp'``: relative contribution of each predictor to the final\
:math:`R^2` (only if ``relimp=True``).
* ``'relimp_perc'``: percent relative contribution
In addition, the output dataframe comes with hidden attributes such as
the residuals, and degrees of freedom of the model and residuals, which
can be accessed as follow, respectively:
>>> lm = pg.linear_regression() # doctest: +SKIP
>>> lm.residuals_, lm.df_model_, lm.df_resid_ # doctest: +SKIP
Note that to follow scikit-learn convention, these hidden atributes end
with an "_". When ``as_dataframe=False`` however, these attributes
are no longer hidden and can be accessed as any other keys in the
output dictionary.
>>> lm = pg.linear_regression() # doctest: +SKIP
>>> lm['residuals'], lm['df_model'], lm['df_resid'] # doctest: +SKIP
When ``as_dataframe=False`` the dictionary also contains the
processed ``X`` and ``y`` arrays (i.e, with NaNs removed if
``remove_na=True``) and the model's predicted values ``pred``.
>>> lm['X'], lm['y'], lm['pred'] # doctest: +SKIP
For a weighted least squares fit, the weighted ``Xw`` and ``yw``
arrays are included in the dictionary.
>>> lm['Xw'], lm['yw'] # doctest: +SKIP
See also
--------
logistic_regression, mediation_analysis, corr
Notes
-----
The :math:`\\beta` coefficients are estimated using an ordinary least
squares (OLS) regression, as implemented in the
:py:func:`scipy.linalg.lstsq` function. The OLS method minimizes
the sum of squared residuals, and leads to a closed-form expression for
the estimated :math:`\\beta`:
.. math:: \\hat{\\beta} = (X^TX)^{-1} X^Ty
It is generally recommended to include a constant term (intercept) to the
model to limit the bias and force the residual mean to equal zero.
Note that intercept coefficient and p-values are however rarely meaningful.
The standard error of the estimates is a measure of the accuracy of the
prediction defined as:
.. math:: \\sigma = \\sqrt{\\text{MSE} \\cdot (X^TX)^{-1}}
where :math:`\\text{MSE}` is the mean squared error,
.. math::
\\text{MSE} = \\frac{SS_{\\text{resid}}}{n - p - 1}
= \\frac{\\sum{(\\text{true} - \\text{pred})^2}}{n - p - 1}
:math:`p` is the total number of predictor variables in the model
(excluding the intercept) and :math:`n` is the sample size.
Using the :math:`\\beta` coefficients and the standard errors,
the T-values can be obtained:
.. math:: T = \\frac{\\beta}{\\sigma}
and the p-values approximated using a T-distribution with
:math:`n - p - 1` degrees of freedom.
The coefficient of determination (:math:`R^2`) is defined as:
.. math:: R^2 = 1 - (\\frac{SS_{\\text{resid}}}{SS_{\\text{total}}})
The adjusted :math:`R^2` is defined as:
.. math:: \\overline{R}^2 = 1 - (1 - R^2) \\frac{n - 1}{n - p - 1}
The relative importance (``relimp``) column is a partitioning of the
total :math:`R^2` of the model into individual :math:`R^2` contribution.
This is calculated by taking the average over average contributions in
models of different sizes. For more details, please refer to
`Groemping et al. 2006 <http://dx.doi.org/10.18637/jss.v017.i01>`_
and the R package `relaimpo
<https://cran.r-project.org/web/packages/relaimpo/relaimpo.pdf>`_.
Note that Pingouin will automatically remove any duplicate columns
from :math:`X`, as well as any column with only one unique value
(constant), excluding the intercept.
Results have been compared against sklearn, R, statsmodels and JASP.
Examples
--------
1. Simple linear regression using columns of a pandas dataframe
In this first example, we'll use the tips dataset to see how well we
can predict the waiter's tip (in dollars) based on the total bill (also
in dollars).
>>> import numpy as np
>>> import pingouin as pg
>>> df = pg.read_dataset('tips')
>>> # Let's predict the tip ($) based on the total bill (also in $)
>>> lm = pg.linear_regression(df['total_bill'], df['tip'])
>>> lm.round(2)
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]
0 Intercept 0.92 0.16 5.76 0.0 0.46 0.45 0.61 1.23
1 total_bill 0.11 0.01 14.26 0.0 0.46 0.45 0.09 0.12
It comes as no surprise that total bill is indeed a significant predictor
of the waiter's tip (T=14.26, p<0.05). The :math:`R^2` of the model is 0.46
and the adjusted :math:`R^2` is 0.45, which means that our model roughly
explains ~45% of the total variance in the tip amount.
2. Multiple linear regression
We can also have more than one predictor and run a multiple linear
regression. Below, we add the party size as a second predictor of tip.
>>> # We'll add a second predictor: the party size
>>> lm = pg.linear_regression(df[['total_bill', 'size']], df['tip'])
>>> lm.round(2)
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]
0 Intercept 0.67 0.19 3.46 0.00 0.47 0.46 0.29 1.05
1 total_bill 0.09 0.01 10.17 0.00 0.47 0.46 0.07 0.11
2 size 0.19 0.09 2.26 0.02 0.47 0.46 0.02 0.36
The party size is also a significant predictor of tip (T=2.26, p=0.02).
Note that adding this new predictor however only improved the :math:`R^2`
of our model by ~1%.
This function also works with numpy arrays:
>>> X = df[['total_bill', 'size']].to_numpy()
>>> y = df['tip'].to_numpy()
>>> pg.linear_regression(X, y).round(2)
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]
0 Intercept 0.67 0.19 3.46 0.00 0.47 0.46 0.29 1.05
1 x1 0.09 0.01 10.17 0.00 0.47 0.46 0.07 0.11
2 x2 0.19 0.09 2.26 0.02 0.47 0.46 0.02 0.36
3. Get the residuals
>>> # For clarity, only display the first 9 values
>>> np.round(lm.residuals_, 2)[:9]
array([-1.62, -0.55, 0.31, 0.06, -0.11, 0.93, 0.13, -0.81, -0.49])
Using pandas, we can show a summary of the distribution of the residuals:
>>> import pandas as pd
>>> pd.Series(lm.residuals_).describe().round(2)
count 244.00
mean -0.00
std 1.01
min -2.93
25% -0.55
50% -0.09
75% 0.51
max 4.04
dtype: float64
5. No intercept and return only the regression coefficients
Sometimes it may be useful to remove the constant term from the regression,
or to only return the regression coefficients without calculating the
standard errors or p-values. This latter can potentially save you a lot of
time if you need to calculate hundreds of regression and only care about
the coefficients!
>>> pg.linear_regression(X, y, add_intercept=False, coef_only=True)
array([0.1007119 , 0.36209717])
6. Return a dictionnary instead of a dataframe
>>> lm_dict = pg.linear_regression(X, y, as_dataframe=False)
>>> lm_dict.keys()
dict_keys(['names', 'coef', 'se', 'T', 'pval', 'r2', 'adj_r2', 'CI[2.5%]',
'CI[97.5%]', 'df_model', 'df_resid', 'residuals', 'X', 'y',
'pred'])
7. Remove missing values
>>> X[4, 1] = np.nan
>>> y[7] = np.nan
>>> pg.linear_regression(X, y, remove_na=True, coef_only=True)
array([0.65749955, 0.09262059, 0.19927529])
8. Get the relative importance of predictors
>>> lm = pg.linear_regression(X, y, remove_na=True, relimp=True)
>>> lm[['names', 'relimp', 'relimp_perc']]
names relimp relimp_perc
0 Intercept NaN NaN
1 x1 0.342503 73.045583
2 x2 0.126386 26.954417
The ``relimp`` column is a partitioning of the total :math:`R^2` of the
model into individual contribution. Therefore, it sums to the :math:`R^2`
of the full model. The ``relimp_perc`` is normalized to sum to 100%. See
`Groemping 2006 <https://www.jstatsoft.org/article/view/v017i01>`_
for more details.
>>> lm[['relimp', 'relimp_perc']].sum()
relimp 0.468889
relimp_perc 100.000000
dtype: float64
9. Weighted linear regression
>>> X = [1, 2, 3, 4, 5, 6]
>>> y = [10, 22, 11, 13, 13, 16]
>>> w = [1, 0.1, 1, 1, 0.5, 1] # Array of weights. Must be >= 0.
>>> lm = pg.linear_regression(X, y, weights=w)
>>> lm.round(2)
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]
0 Intercept 9.00 2.03 4.42 0.01 0.51 0.39 3.35 14.64
1 x1 1.04 0.50 2.06 0.11 0.51 0.39 -0.36 2.44
"""
# Extract names if X is a Dataframe or Series
if isinstance(X, pd.DataFrame):
names = X.keys().tolist()
elif isinstance(X, pd.Series):
names = [X.name]
else:
names = []
# Convert input to numpy array
X = np.asarray(X)
y = np.asarray(y)
assert y.ndim == 1, "y must be one-dimensional."
assert 0 < alpha < 1
if X.ndim == 1:
# Convert to (n_samples, n_features) shape
X = X[..., np.newaxis]
# Check for NaN / Inf
if remove_na:
X, y = rm_na(X, y[..., np.newaxis], paired=True, axis="rows")
y = np.squeeze(y)
y_gd = np.isfinite(y).all()
X_gd = np.isfinite(X).all()
assert y_gd, (
"Target (y) contains NaN or Inf. Please remove them " "manually or use remove_na=True."
)
assert X_gd, (
"Predictors (X) contain NaN or Inf. Please remove them " "manually or use remove_na=True."
)
# Check that X and y have same length
assert y.shape[0] == X.shape[0], "X and y must have same number of samples"
if not names:
names = ["x" + str(i + 1) for i in range(X.shape[1])]
if add_intercept:
# Add intercept
X = np.column_stack((np.ones(X.shape[0]), X))
names.insert(0, "Intercept")
# FINAL CHECKS BEFORE RUNNING LEAST SQUARES REGRESSION
# 1. Let's remove column(s) with only zero, otherwise the regression fails
n_nonzero = np.count_nonzero(X, axis=0)
idx_zero = np.flatnonzero(n_nonzero == 0) # Find columns that are only 0
if len(idx_zero):
X = np.delete(X, idx_zero, 1)
names = np.delete(names, idx_zero)
# 2. We also want to make sure that there is no more than one constant
# column (= intercept), otherwise the regression fails
# This is equivalent, but much faster, to pd.DataFrame(X).nunique()
idx_unique = np.where(np.all(X == X[0, :], axis=0))[0]
if len(idx_unique) > 1:
# We remove all but the first "Intercept" column.
X = np.delete(X, idx_unique[1:], 1)
names = np.delete(names, idx_unique[1:])
# Is there a constant in our predictor matrix? Useful for dof and R^2.
constant = 1 if len(idx_unique) > 0 else 0
# 3. Finally, we want to remove duplicate columns
if X.shape[1] > 1:
idx_duplicate = []
for pair in itertools.combinations(range(X.shape[1]), 2):
if np.array_equal(X[:, pair[0]], X[:, pair[1]]):
idx_duplicate.append(pair[1])
if len(idx_duplicate):
X = np.delete(X, idx_duplicate, 1)
names = np.delete(names, idx_duplicate)
# 4. Check that we have enough samples / features
n, p = X.shape[0], X.shape[1]
assert n >= 3, "At least three valid samples are required in X."
assert p >= 1, "X must have at least one valid column."
# 5. Handle weights
if weights is not None:
if relimp:
raise ValueError("relimp = True is not supported when using " "weights.")
w = np.asarray(weights)
assert w.ndim == 1, "weights must be a 1D array."
assert w.size == n, "weights must be of shape n_samples."
assert not np.isnan(w).any(), "Missing weights are not accepted."
assert not (w < 0).any(), "Negative weights are not accepted."
# Do not count weights == 0 in dof
# This gives similar results as R lm() but different from statsmodels
n = np.count_nonzero(w)
# Rescale (whitening)
wts = np.diag(np.sqrt(w))
Xw = wts @ X
yw = wts @ y
else:
# Set all weights to one, [1, 1, 1, ...]
w = np.ones(n)
Xw = X
yw = y
# FIT (WEIGHTED) LEAST SQUARES REGRESSION
coef, ss_res, rank, _ = lstsq(Xw, yw, cond=None)
ss_res = ss_res[0] if ss_res.shape == (1,) else ss_res
if coef_only:
return coef
calc_ss_res = False
if rank < Xw.shape[1]:
# in this case, ss_res is of shape (0,), i.e., an empty array
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 - rank
# Calculate predicted values and (weighted) residuals
pred = Xw @ coef
resid = yw - pred
if calc_ss_res:
# In case we did not get ss_res from lstsq due to rank deficiency
ss_res = (resid**2).sum()
# Calculate total (weighted) sums of squares and R^2
ss_tot = yw @ yw
ss_wtot = np.sum(w * (y - np.average(y, weights=w)) ** 2)
if constant:
r2 = 1 - ss_res / ss_wtot
else:
r2 = 1 - ss_res / ss_tot
adj_r2 = 1 - (1 - r2) * (n - constant) / df_resid
# Compute mean squared error, variance and SE
mse = ss_res / df_resid
beta_var = mse * (np.linalg.pinv(Xw.T @ Xw).diagonal())
beta_se = np.sqrt(beta_var)
# Compute T and p-values
T = coef / beta_se
pval = 2 * t.sf(np.fabs(T), df_resid)
# Compute confidence intervals
crit = t.ppf(1 - alpha / 2, df_resid)
marg_error = crit * beta_se
ll = coef - marg_error
ul = coef + marg_error
# Rename CI
ll_name = "CI[%.1f%%]" % (100 * alpha / 2)
ul_name = "CI[%.1f%%]" % (100 * (1 - alpha / 2))
# Create dict
stats = {
"names": names,
"coef": coef,
"se": beta_se,
"T": T,
"pval": pval,
"r2": r2,
"adj_r2": adj_r2,
ll_name: ll,
ul_name: ul,
}
# Relative importance
if relimp:
data = pd.concat(
[pd.DataFrame(y, columns=["y"]), pd.DataFrame(X, columns=names)], sort=False, axis=1
)
if "Intercept" in names:
# Intercept is the first column
reli = _relimp(data.drop(columns=["Intercept"]).cov(numeric_only=True))
reli["names"] = ["Intercept"] + reli["names"]
reli["relimp"] = np.insert(reli["relimp"], 0, np.nan)
reli["relimp_perc"] = np.insert(reli["relimp_perc"], 0, np.nan)
else:
reli = _relimp(data.cov(numeric_only=True))
stats.update(reli)
if as_dataframe:
stats = _postprocess_dataframe(pd.DataFrame(stats))
stats.df_model_ = df_model
stats.df_resid_ = df_resid
stats.residuals_ = 0 # Trick to avoid Pandas warning
stats.residuals_ = resid # Residuals is a hidden attribute
else:
stats["df_model"] = df_model
stats["df_resid"] = df_resid
stats["residuals"] = resid
stats["X"] = X
stats["y"] = y
stats["pred"] = pred
if weights is not None:
stats["yw"] = yw
stats["Xw"] = Xw
return stats
def _relimp(S):
"""Relative importance of predictors in multiple regression.
This is an internal function. This function should only be used with a low
number of predictors. Indeed, the computation time roughly doubles for each
additional predictor.
Parameters
----------
S : pd.DataFrame
Covariance matrix. The target variable MUST be the FIRST column,
followed by the predictors (excluding the intercept).
"""
assert isinstance(S, pd.DataFrame)
cols = S.columns.tolist()
# Define indices of columns: .iloc is faster than .loc
predictors = cols[1:]
npred = len(predictors)
target_int = 0
predictors_int = np.arange(1, npred + 1)
# Calculate total sum of squares and beta coefficients
# Note that the R^2 that we calculate below is always the R^2 of the model
# INCLUDING the intercept!
ss_tot = S.iat[target_int, target_int]
betas = (
np.linalg.pinv(S.iloc[predictors_int, predictors_int]) @ S.iloc[predictors_int, target_int]
)
r2_full = betas @ S.iloc[target_int, predictors_int] / ss_tot
# Pre-computed SSreg dictionnary
ss_reg_precomp = {}
# Start looping over predictors
all_preds = []
for pred in predictors_int:
loo = np.setdiff1d(predictors_int, pred)
r2_seq_mean = []
# Loop over number of predictors
for k in np.arange(0, npred - 1):
r2_seq = []
# Loop over combinations of predictors
for p in itertools.combinations(loo, int(k)):
p = list(p)
p_with = p + [pred]
# To avoid calculating several times the same values
# we use a trick here: we save the first calculation
# to a dictionnary where the key is the sorted string
# (hence the order does not matter)
if str(sorted(p)) in ss_reg_precomp.keys():
ss_reg_without = ss_reg_precomp[str(sorted(p))]
else:
S_without = S.iloc[p, target_int]
ss_reg_without = np.linalg.pinv(S.iloc[p, p]) @ S_without @ S_without
ss_reg_precomp[str(sorted(p))] = ss_reg_without
S_with = S.iloc[p_with, target_int]
ss_reg_with = pinvh(S.iloc[p_with, p_with]) @ S_with @ S_with
ss_reg_precomp[str(sorted(p_with))] = ss_reg_with
# Calculate R^2
r2_diff = (ss_reg_with - ss_reg_without) / ss_tot
# Append the difference
r2_seq.append(r2_diff)
# First averaging
r2_seq_mean.append(np.mean(r2_seq))
# When Sk(r) = S
S_without = S.iloc[loo, target_int]
ss_reg = np.linalg.pinv(S.iloc[loo, loo]) @ S_without @ S_without
r2_without = ss_reg / ss_tot
r2_seq = r2_full - r2_without
r2_seq_mean.append(r2_seq)
all_preds.append(np.mean(r2_seq_mean))
stats_relimp = {
"names": predictors,
"relimp": all_preds,
"relimp_perc": np.array(all_preds) / sum(all_preds) * 100,
}
return stats_relimp
[docs]
def logistic_regression(
X, y, coef_only=False, alpha=0.05, as_dataframe=True, remove_na=False, **kwargs
):
"""(Multiple) Binary logistic regression.
Parameters
----------
X : array_like
Predictor(s), of shape *(n_samples, n_features)* or *(n_samples)*.
y : array_like
Dependent variable, of shape *(n_samples)*.
``y`` must be binary, i.e. only contains 0 or 1. Multinomial logistic
regression is not supported.
coef_only : bool
If True, return only the regression coefficients.
alpha : float
Alpha value used for the confidence intervals.
:math:`\\text{CI} = [\\alpha / 2 ; 1 - \\alpha / 2]`
as_dataframe : bool
If True, returns a pandas DataFrame. If False, returns a dictionnary.
remove_na : bool
If True, apply a listwise deletion of missing values (i.e. the entire
row is removed). Default is False, which will raise an error if missing
values are present in either the predictor(s) or dependent
variable.
**kwargs : optional
Optional arguments passed to
:py:class:`sklearn.linear_model.LogisticRegression` (see Notes).
Returns
-------
stats : :py:class:`pandas.DataFrame` or dict
Logistic regression summary:
* ``'names'``: name of variable(s) in the model (e.g. x1, x2...)
* ``'coef'``: regression coefficients (log-odds)
* ``'se'``: standard error
* ``'z'``: z-scores
* ``'pval'``: two-tailed p-values
* ``'CI[2.5%]'``: lower confidence interval
* ``'CI[97.5%]'``: upper confidence interval
See also
--------
linear_regression
Notes
-----
.. caution:: This function is a wrapper around the
:py:class:`sklearn.linear_model.LogisticRegression` class. However,
Pingouin internally disables the L2 regularization and changes the
default solver to 'newton-cg' to obtain results that are similar to R and
statsmodels.
Logistic regression assumes that the log-odds (the logarithm of the
odds) for the value labeled "1" in the response variable is a linear
combination of the predictor variables. The log-odds are given by the
`logit <https://en.wikipedia.org/wiki/Logit>`_ function,
which map a probability :math:`p` of the response variable being "1"
from :math:`[0, 1)` to :math:`(-\\infty, +\\infty)`.
.. math:: \\text{logit}(p) = \\ln \\frac{p}{1 - p} = \\beta_0 + \\beta X
The odds of the response variable being "1" can be obtained by
exponentiating the log-odds:
.. math:: \\frac{p}{1 - p} = e^{\\beta_0 + \\beta X}
and the probability of the response variable being "1" is given by the
`logistic function <https://en.wikipedia.org/wiki/Logistic_function>`_:
.. math:: p = \\frac{1}{1 + e^{-(\\beta_0 + \\beta X})}
The first coefficient is always the constant term (intercept) of
the model. Pingouin will automatically add the intercept
to your predictor(s) matrix, therefore, :math:`X` should not include a
constant term. Pingouin will remove any constant term (e.g column with only
one unique value), or duplicate columns from :math:`X`.
The calculation of the p-values and confidence interval is adapted from a
`code by Rob Speare
<https://gist.github.com/rspeare/77061e6e317896be29c6de9a85db301d>`_.
Results have been compared against statsmodels, R, and JASP.
Examples
--------
1. Simple binary logistic regression.
In this first example, we'll use the
`penguins dataset <https://github.com/allisonhorst/palmerpenguins>`_
to see how well we can predict the sex of penguins based on their
bodies mass.
>>> import numpy as np
>>> import pandas as pd
>>> import pingouin as pg
>>> df = pg.read_dataset('penguins')
>>> # Let's first convert the target variable from string to boolean:
>>> df['male'] = (df['sex'] == 'male').astype(int) # male: 1, female: 0
>>> # Since there are missing values in our outcome variable, we need to
>>> # set `remove_na=True` otherwise regression will fail.
>>> lom = pg.logistic_regression(df['body_mass_g'], df['male'],
... remove_na=True)
>>> lom.round(2)
names coef se z pval CI[2.5%] CI[97.5%]
0 Intercept -5.16 0.71 -7.24 0.0 -6.56 -3.77
1 body_mass_g 0.00 0.00 7.24 0.0 0.00 0.00
Body mass is a significant predictor of sex (p<0.001). Here, it
could be useful to rescale our predictor variable from *g* to *kg*
(e.g divide by 1000) in order to get more intuitive coefficients and
confidence intervals:
>>> df['body_mass_kg'] = df['body_mass_g'] / 1000
>>> lom = pg.logistic_regression(df['body_mass_kg'], df['male'],
... remove_na=True)
>>> lom.round(2)
names coef se z pval CI[2.5%] CI[97.5%]
0 Intercept -5.16 0.71 -7.24 0.0 -6.56 -3.77
1 body_mass_kg 1.23 0.17 7.24 0.0 0.89 1.56
2. Multiple binary logistic regression
We'll now add the species as a categorical predictor in our model. To do
so, we first need to dummy-code our categorical variable, dropping the
first level of our categorical variable (species = Adelie) which will be
used as the reference level:
>>> df = pd.get_dummies(df, columns=['species'], dtype=float, drop_first=True)
>>> X = df[['body_mass_kg', 'species_Chinstrap', 'species_Gentoo']]
>>> y = df['male']
>>> lom = pg.logistic_regression(X, y, remove_na=True)
>>> lom.round(2)
names coef se z pval CI[2.5%] CI[97.5%]
0 Intercept -26.24 2.84 -9.24 0.00 -31.81 -20.67
1 body_mass_kg 7.10 0.77 9.23 0.00 5.59 8.61
2 species_Chinstrap -0.13 0.42 -0.31 0.75 -0.96 0.69
3 species_Gentoo -9.72 1.12 -8.65 0.00 -11.92 -7.52
3. Using NumPy aray and returning only the coefficients
>>> pg.logistic_regression(X.to_numpy(), y.to_numpy(), coef_only=True,
... remove_na=True)
array([-26.23906892, 7.09826571, -0.13180626, -9.71718529])
4. Passing custom parameters to sklearn
>>> lom = pg.logistic_regression(X, y, solver='sag', max_iter=10000,
... random_state=42, remove_na=True)
>>> print(lom['coef'].to_numpy())
[-25.98248153 7.02881472 -0.13119779 -9.62247569]
**How to interpret the log-odds coefficients?**
We'll use the `Wikipedia example
<https://en.wikipedia.org/wiki/Logistic_regression#Probability_of_passing_an_exam_versus_hours_of_study>`_
of the probability of passing an exam
versus the hours of study:
*A group of 20 students spends between 0 and 6 hours studying for an
exam. How does the number of hours spent studying affect the
probability of the student passing the exam?*
>>> # First, let's create the dataframe
>>> Hours = [0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 1.75, 2.00, 2.25, 2.50,
... 2.75, 3.00, 3.25, 3.50, 4.00, 4.25, 4.50, 4.75, 5.00, 5.50]
>>> Pass = [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1]
>>> df = pd.DataFrame({'HoursStudy': Hours, 'PassExam': Pass})
>>> # And then run the logistic regression
>>> lr = pg.logistic_regression(df['HoursStudy'], df['PassExam']).round(3)
>>> lr
names coef se z pval CI[2.5%] CI[97.5%]
0 Intercept -4.078 1.761 -2.316 0.021 -7.529 -0.626
1 HoursStudy 1.505 0.629 2.393 0.017 0.272 2.737
The ``Intercept`` coefficient (-4.078) is the log-odds of ``PassExam=1``
when ``HoursStudy=0``. The odds ratio can be obtained by exponentiating
the log-odds:
>>> np.exp(-4.078)
0.016941314421496552
i.e. :math:`0.017:1`. Conversely the odds of failing the exam are
:math:`(1/0.017) \\approx 59:1`.
The probability can then be obtained with the following equation
.. math:: p = \\frac{1}{1 + e^{-(-4.078 + 0 * 1.505)}}
>>> 1 / (1 + np.exp(-(-4.078)))
0.016659087580814722
The ``HoursStudy`` coefficient (1.505) means that for each additional hour
of study, the log-odds of passing the exam increase by 1.505, and the odds
are multipled by :math:`e^{1.505} \\approx 4.50`.
For example, a student who studies 2 hours has a probability of passing
the exam of 25%:
>>> 1 / (1 + np.exp(-(-4.078 + 2 * 1.505)))
0.2557836148964987
The table below shows the probability of passing the exam for several
values of ``HoursStudy``:
+----------------+----------+----------------+------------------+
| Hours of Study | Log-odds | Odds | Probability |
+================+==========+================+==================+
| 0 | −4.08 | 0.017 ≈ 1:59 | 0.017 |
+----------------+----------+----------------+------------------+
| 1 | −2.57 | 0.076 ≈ 1:13 | 0.07 |
+----------------+----------+----------------+------------------+
| 2 | −1.07 | 0.34 ≈ 1:3 | 0.26 |
+----------------+----------+----------------+------------------+
| 3 | 0.44 | 1.55 | 0.61 |
+----------------+----------+----------------+------------------+
| 4 | 1.94 | 6.96 | 0.87 |
+----------------+----------+----------------+------------------+
| 5 | 3.45 | 31.4 | 0.97 |
+----------------+----------+----------------+------------------+
| 6 | 4.96 | 141.4 | 0.99 |
+----------------+----------+----------------+------------------+
"""
# Check that sklearn is installed
from pingouin.utils import _is_sklearn_installed
_is_sklearn_installed(raise_error=True)
from sklearn.linear_model import LogisticRegression
# Extract names if X is a Dataframe or Series
if isinstance(X, pd.DataFrame):
names = X.keys().tolist()
elif isinstance(X, pd.Series):
names = [X.name]
else:
names = []
# Convert to numpy array
X = np.asarray(X)
y = np.asarray(y)
assert y.ndim == 1, "y must be one-dimensional."
assert 0 < alpha < 1, "alpha must be between 0 and 1."
# Add axis if only one-dimensional array
if X.ndim == 1:
X = X[..., np.newaxis]
# Check for NaN / Inf
if remove_na:
X, y = rm_na(X, y[..., np.newaxis], paired=True, axis="rows")
y = np.squeeze(y)
y_gd = np.isfinite(y).all()
X_gd = np.isfinite(X).all()
assert y_gd, (
"Target (y) contains NaN or Inf. Please remove them " "manually or use remove_na=True."
)
assert X_gd, (
"Predictors (X) contain NaN or Inf. Please remove them " "manually or use remove_na=True."
)
# Check that X and y have same length
assert y.shape[0] == X.shape[0], "X and y must have same number of samples"
# Check that y is binary
if np.unique(y).size != 2:
raise ValueError("Dependent variable must be binary.")
if not names:
names = ["x" + str(i + 1) for i in range(X.shape[1])]
# We also want to make sure that there is no column
# with only one unique value, otherwise the regression fails
# This is equivalent, but much faster, to pd.DataFrame(X).nunique()
idx_unique = np.where(np.all(X == X[0, :], axis=0))[0]
if len(idx_unique):
X = np.delete(X, idx_unique, 1)
names = np.delete(names, idx_unique).tolist()
# Finally, we want to remove duplicate columns
if X.shape[1] > 1:
idx_duplicate = []
for pair in itertools.combinations(range(X.shape[1]), 2):
if np.array_equal(X[:, pair[0]], X[:, pair[1]]):
idx_duplicate.append(pair[1])
if len(idx_duplicate):
X = np.delete(X, idx_duplicate, 1)
names = np.delete(names, idx_duplicate).tolist()
# Initialize and fit
if "solver" not in kwargs:
# https://stats.stackexchange.com/a/204324/253579
# Updated in Pingouin > 0.3.6 to be consistent with R
kwargs["solver"] = "newton-cg"
if "penalty" not in kwargs:
kwargs["penalty"] = None
lom = LogisticRegression(**kwargs)
lom.fit(X, y)
if lom.get_params()["fit_intercept"]:
names.insert(0, "Intercept")
X_design = np.column_stack((np.ones(X.shape[0]), X))
coef = np.append(lom.intercept_, lom.coef_)
else:
coef = lom.coef_
X_design = X
if coef_only:
return coef
# Fisher Information Matrix
n, p = X_design.shape
denom = 2 * (1 + np.cosh(lom.decision_function(X)))
denom = np.tile(denom, (p, 1)).T
fim = (X_design / denom).T @ X_design
crao = np.linalg.pinv(fim)
# Standard error and Z-scores
se = np.sqrt(np.diag(crao))
z_scores = coef / se
# Two-tailed p-values
pval = 2 * norm.sf(np.fabs(z_scores))
# Wald Confidence intervals
# In R: this is equivalent to confint.default(model)
# Note that confint(model) will however return the profile CI
crit = norm.ppf(1 - alpha / 2)
ll = coef - crit * se
ul = coef + crit * se
# Rename CI
ll_name = "CI[%.1f%%]" % (100 * alpha / 2)
ul_name = "CI[%.1f%%]" % (100 * (1 - alpha / 2))
# Create dict
stats = {
"names": names,
"coef": coef,
"se": se,
"z": z_scores,
"pval": pval,
ll_name: ll,
ul_name: ul,
}
if as_dataframe:
return _postprocess_dataframe(pd.DataFrame(stats))
else:
return stats
def _point_estimate(X_val, XM_val, M_val, y_val, idx, n_mediator, mtype="linear", **logreg_kwargs):
"""Point estimate of indirect effect based on bootstrap sample."""
# Mediator(s) model (M(j) ~ X + covar)
beta_m = []
for j in range(n_mediator):
if mtype == "linear":
beta_m.append(linear_regression(X_val[idx], M_val[idx, j], coef_only=True)[1])
else:
beta_m.append(
logistic_regression(X_val[idx], M_val[idx, j], coef_only=True, **logreg_kwargs)[1]
)
# Full model (Y ~ X + M + covar)
beta_y = linear_regression(XM_val[idx], y_val[idx], coef_only=True)[2 : (2 + n_mediator)]
# Point estimate
return beta_m * beta_y
def _bias_corrected_ci(bootdist, sample_point, alpha=0.05):
"""Bias-corrected confidence intervals
Parameters
----------
bootdist : array-like
Array with bootstrap estimates for each sample.
sample_point : float
Point estimate based on full sample.
alpha : float
Alpha for confidence interval.
Returns
-------
CI : 1d array-like
Lower and upper bias-corrected confidence interval estimates.
Notes
-----
This is what's used in the "cper" method implemented in :py:func:`pingouin.compute_bootci`.
This differs from the bias-corrected and accelerated method (BCa, default in Matlab and
SciPy) because it does not correct for skewness. Indeed, the acceleration parameter, a,
is proportional to the skewness of the bootstrap distribution. The bias-correction parameter,
z0, is related to the proportion of bootstrap estimates that are less than the observed
statistic.
"""
# Bias of bootstrap estimates
# In Matlab bootci they also count when bootdist == sample_point
# >>> z0 = norminv(mean(bstat < stat,1) + mean(bstat == stat,1)/2);
z0 = norm.ppf(np.mean(bootdist < sample_point))
# Adjusted intervals
adjusted_ll = norm.cdf(2 * z0 + norm.ppf(alpha / 2)) * 100
adjusted_ul = norm.cdf(2 * z0 + norm.ppf(1 - alpha / 2)) * 100
# Adjusted percentiles
ci = np.percentile(bootdist, q=[adjusted_ll, adjusted_ul])
return ci
def _pval_from_bootci(boot, estimate):
"""Compute p-value from bootstrap distribution.
Similar to the pval function in the R package mediation.
Note that this is less accurate than a permutation test because the
bootstrap distribution is not conditioned on a true null hypothesis.
"""
if estimate == 0:
out = 1
else:
out = 2 * min(sum(boot > 0), sum(boot < 0)) / len(boot)
return min(out, 1)
