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|� � � |� |� � � � }|||fS )a-
Bayesian confidence intervals for the mean, var, and std.
Parameters
----------
data : array_like
Input data, if multi-dimensional it is flattened to 1-D by `bayes_mvs`.
Requires 2 or more data points.
alpha : float, optional
Probability that the returned confidence interval contains
the true parameter.
Returns
-------
mean_cntr, var_cntr, std_cntr : tuple
The three results are for the mean, variance and standard deviation,
respectively. Each result is a tuple of the form::
(center, (lower, upper))
with `center` the mean of the conditional pdf of the value given the
data, and `(lower, upper)` a confidence interval, centered on the
median, containing the estimate to a probability ``alpha``.
See Also
--------
mvsdist
Notes
-----
Each tuple of mean, variance, and standard deviation estimates represent
the (center, (lower, upper)) with center the mean of the conditional pdf
of the value given the data and (lower, upper) is a confidence interval
centered on the median, containing the estimate to a probability
``alpha``.
Converts data to 1-D and assumes all data has the same mean and variance.
Uses Jeffrey's prior for variance and std.
Equivalent to ``tuple((x.mean(), x.interval(alpha)) for x in mvsdist(dat))``
References
----------
T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and
standard-deviation from data", https://scholarsarchive.byu.edu/facpub/278,
2006.
Examples
--------
First a basic example to demonstrate the outputs:
>>> from scipy import stats
>>> data = [6, 9, 12, 7, 8, 8, 13]
>>> mean, var, std = stats.bayes_mvs(data)
>>> mean
Mean(statistic=9.0, minmax=(7.103650222612533, 10.896349777387467))
>>> var
Variance(statistic=10.0, minmax=(3.176724206..., 24.45910382...))
>>> std
Std_dev(statistic=2.9724954732045084, minmax=(1.7823367265645143, 4.945614605014631))
Now we generate some normally distributed random data, and get estimates of
mean and standard deviation with 95% confidence intervals for those
estimates:
>>> n_samples = 100000
>>> data = stats.norm.rvs(size=n_samples)
>>> res_mean, res_var, res_std = stats.bayes_mvs(data, alpha=0.95)
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.hist(data, bins=100, density=True, label='Histogram of data')
>>> ax.vlines(res_mean.statistic, 0, 0.5, colors='r', label='Estimated mean')
>>> ax.axvspan(res_mean.minmax[0],res_mean.minmax[1], facecolor='r',
... alpha=0.2, label=r'Estimated mean (95% limits)')
>>> ax.vlines(res_std.statistic, 0, 0.5, colors='g', label='Estimated scale')
>>> ax.axvspan(res_std.minmax[0],res_std.minmax[1], facecolor='g', alpha=0.2,
... label=r'Estimated scale (95% limits)')
>>> ax.legend(fontsize=10)
>>> ax.set_xlim([-4, 4])
>>> ax.set_ylim([0, 0.5])
>>> plt.show()
r! r z20 < alpha < 1 is required, but alpha=%s was given.)r/ �
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'Frozen' distributions for mean, variance, and standard deviation of data.
Parameters
----------
data : array_like
Input array. Converted to 1-D using ravel.
Requires 2 or more data-points.
Returns
-------
mdist : "frozen" distribution object
Distribution object representing the mean of the data.
vdist : "frozen" distribution object
Distribution object representing the variance of the data.
sdist : "frozen" distribution object
Distribution object representing the standard deviation of the data.
See Also
--------
bayes_mvs
Notes
-----
The return values from ``bayes_mvs(data)`` is equivalent to
``tuple((x.mean(), x.interval(0.90)) for x in mvsdist(data))``.
In other words, calling ``<dist>.mean()`` and ``<dist>.interval(0.90)``
on the three distribution objects returned from this function will give
the same results that are returned from `bayes_mvs`.
References
----------
T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and
standard-deviation from data", https://scholarsarchive.byu.edu/facpub/278,
2006.
Examples
--------
>>> from scipy import stats
>>> data = [6, 9, 12, 7, 8, 8, 13]
>>> mean, var, std = stats.mvsdist(data)
We now have frozen distribution objects "mean", "var" and "std" that we can
examine:
>>> mean.mean()
9.0
>>> mean.interval(0.95)
(6.6120585482655692, 11.387941451734431)
>>> mean.std()
1.1952286093343936
r- z Need at least 2 data-points.i� )�loc�scale� @r! ���)rc ) r �lenrT rU �varr) �norm�mathr �t�gengamma�invgamma) rW �x�n�xbar�C�mdist�sdist�vdist�nm1�fac�vals r_ r/ r/ � sV � �n �d� � �A� �A���A��1�u�u��7�8�8�8� �6�6�8�8�D� �����A��4�x�x� �"�t�4�9�Q��U�3C�3C�D�D�D�� �"�t�y��|�|�4�9�Q�"�q�&�\�;R�;R�S�S�S�� �"�q�� �#��'�0B�0B�Q�0F�G�G�G����!�e���!�e�b�j���B�h�� ����T�Y�q�3�w�5G�5G�H�H�H�� �&�s�B�d�i��n�n�E�E�E�� �&�s�#�6�6�6�� �%�� � r` c � � | S �N� �rm s r_ �<lambda>r{ � � � �a� r` c � � | fS rx ry rz s r_ r{ r{ � � � �A�4� r` )�result_to_tuple� n_outputs� default_axisc � � |dk s|dk rt d� � �t |� � }t j |dz t j � � }t | � � } | j }|dk rt d� � �t j t j | � � � � r t j S t d|dz � � D ] }t j | |z d�� � ||< � |dk r|d dz |z S |dk r ||d z |d d z z
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Return the nth k-statistic (1<=n<=4 so far).
The nth k-statistic k_n is the unique symmetric unbiased estimator of the
nth cumulant kappa_n.
Parameters
----------
data : array_like
Input array. Note that n-D input gets flattened.
n : int, {1, 2, 3, 4}, optional
Default is equal to 2.
Returns
-------
kstat : float
The nth k-statistic.
See Also
--------
kstatvar: Returns an unbiased estimator of the variance of the k-statistic.
moment: Returns the n-th central moment about the mean for a sample.
Notes
-----
For a sample size n, the first few k-statistics are given by:
.. math::
k_{1} = \mu
k_{2} = \frac{n}{n-1} m_{2}
k_{3} = \frac{ n^{2} } {(n-1) (n-2)} m_{3}
k_{4} = \frac{ n^{2} [(n + 1)m_{4} - 3(n - 1) m^2_{2}]} {(n-1) (n-2) (n-3)}
where :math:`\mu` is the sample mean, :math:`m_2` is the sample
variance, and :math:`m_i` is the i-th sample central moment.
References
----------
http://mathworld.wolfram.com/k-Statistic.html
http://mathworld.wolfram.com/Cumulant.html
Examples
--------
>>> from scipy import stats
>>> from numpy.random import default_rng
>>> rng = default_rng()
As sample size increases, n-th moment and n-th k-statistic converge to the
same number (although they aren't identical). In the case of the normal
distribution, they converge to zero.
>>> for n in [2, 3, 4, 5, 6, 7]:
... x = rng.normal(size=10**n)
... m, k = stats.moment(x, 3), stats.kstat(x, 3)
... print("%.3g %.3g %.3g" % (m, k, m-k))
-0.631 -0.651 0.0194 # random
0.0282 0.0283 -8.49e-05
-0.0454 -0.0454 1.36e-05
7.53e-05 7.53e-05 -2.26e-09
0.00166 0.00166 -4.99e-09
-2.88e-06 -2.88e-06 8.63e-13
� r! z'k-statistics only supported for 1<=n<=4r z Data input must not be empty��axis� �?r- rd � i����� � @zShould not be here.) rT �int�npr �float64r �size�isnan�sum�nan�range)rW rn �S�N�ks r_ r1 r1 � sC � �H �1�u�u��A����B�C�C�C� �A���A�
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|z z ||dz z z S t d� � �)a# Return an unbiased estimator of the variance of the k-statistic.
See `kstat` for more details of the k-statistic.
Parameters
----------
data : array_like
Input array. Note that n-D input gets flattened.
n : int, {1, 2}, optional
Default is equal to 2.
Returns
-------
kstatvar : float
The nth k-statistic variance.
See Also
--------
kstat: Returns the n-th k-statistic.
moment: Returns the n-th central moment about the mean for a sample.
Notes
-----
The variances of the first few k-statistics are given by:
.. math::
var(k_{1}) = \frac{\kappa^2}{n}
var(k_{2}) = \frac{\kappa^4}{n} + \frac{2\kappa^2_{2}}{n - 1}
var(k_{3}) = \frac{\kappa^6}{n} + \frac{9 \kappa_2 \kappa_4}{n - 1} +
\frac{9 \kappa^2_{3}}{n - 1} +
\frac{6 n \kappa^3_{2}}{(n-1) (n-2)}
var(k_{4}) = \frac{\kappa^8}{n} + \frac{16 \kappa_2 \kappa_6}{n - 1} +
\frac{48 \kappa_{3} \kappa_5}{n - 1} +
\frac{34 \kappa^2_{4}}{n-1} + \frac{72 n \kappa^2_{2} \kappa_4}{(n - 1) (n - 2)} +
\frac{144 n \kappa_{2} \kappa^2_{3}}{(n - 1) (n - 2)} +
\frac{24 (n + 1) n \kappa^4_{2}}{(n - 1) (n - 2) (n - 3)}
r! r- )rn r� r� zOnly n=1 or n=2 supported.)r rf r1 rT )rW rn r� �k2�k4s r_ r2 r2 > s� � �T ��;�;�D� �D� � �A��A�v�v��T�Q� � � �#�%�a�'�'�
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���!��B��E� �Q�q�S�"�H�$��A�a�C��1�1��5�6�6�6r` c �� � t j | t j �� � }dd| z z |d< d|d z
|d< t j d| � � }|dz
| d z z |dd�< |S )
aI Approximations of uniform order statistic medians.
Parameters
----------
n : int
Sample size.
Returns
-------
v : 1d float array
Approximations of the order statistic medians.
References
----------
.. [1] James J. Filliben, "The Probability Plot Correlation Coefficient
Test for Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
Examples
--------
Order statistics of the uniform distribution on the unit interval
are marginally distributed according to beta distributions.
The expectations of these order statistic are evenly spaced across
the interval, but the distributions are skewed in a way that
pushes the medians slightly towards the endpoints of the unit interval:
>>> import numpy as np
>>> n = 4
>>> k = np.arange(1, n+1)
>>> from scipy.stats import beta
>>> a = k
>>> b = n-k+1
>>> beta.mean(a, b)
array([0.2, 0.4, 0.6, 0.8])
>>> beta.median(a, b)
array([0.15910358, 0.38572757, 0.61427243, 0.84089642])
The Filliben approximation uses the exact medians of the smallest
and greatest order statistics, and the remaining medians are approximated
by points spread evenly across a sub-interval of the unit interval:
>>> from scipy.stats._morestats import _calc_uniform_order_statistic_medians
>>> _calc_uniform_order_statistic_medians(n)
array([0.15910358, 0.38545246, 0.61454754, 0.84089642])
This plot shows the skewed distributions of the order statistics
of a sample of size four from a uniform distribution on the unit interval:
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(0.0, 1.0, num=50, endpoint=True)
>>> pdfs = [beta.pdf(x, a[i], b[i]) for i in range(n)]
>>> plt.figure()
>>> plt.plot(x, pdfs[0], x, pdfs[1], x, pdfs[2], x, pdfs[3])
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|� � �| S )a Parse `dist` keyword.
Parameters
----------
dist : str or stats.distributions instance.
Several functions take `dist` as a keyword, hence this utility
function.
enforce_subclass : bool, optional
If True (default), `dist` needs to be a
`_distn_infrastructure.rv_generic` instance.
It can sometimes be useful to set this keyword to False, if a function
wants to accept objects that just look somewhat like such an instance
(for example, they have a ``ppf`` method).
z#%s is not a valid distribution nameNza`dist` should be a stats.distributions instance or a string with the name of such a distribution.)�
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| ffS ||fS )a�
Calculate quantiles for a probability plot, and optionally show the plot.
Generates a probability plot of sample data against the quantiles of a
specified theoretical distribution (the normal distribution by default).
`probplot` optionally calculates a best-fit line for the data and plots the
results using Matplotlib or a given plot function.
Parameters
----------
x : array_like
Sample/response data from which `probplot` creates the plot.
sparams : tuple, optional
Distribution-specific shape parameters (shape parameters plus location
and scale).
dist : str or stats.distributions instance, optional
Distribution or distribution function name. The default is 'norm' for a
normal probability plot. Objects that look enough like a
stats.distributions instance (i.e. they have a ``ppf`` method) are also
accepted.
fit : bool, optional
Fit a least-squares regression (best-fit) line to the sample data if
True (default).
plot : object, optional
If given, plots the quantiles.
If given and `fit` is True, also plots the least squares fit.
`plot` is an object that has to have methods "plot" and "text".
The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
or a custom object with the same methods.
Default is None, which means that no plot is created.
rvalue : bool, optional
If `plot` is provided and `fit` is True, setting `rvalue` to True
includes the coefficient of determination on the plot.
Default is False.
Returns
-------
(osm, osr) : tuple of ndarrays
Tuple of theoretical quantiles (osm, or order statistic medians) and
ordered responses (osr). `osr` is simply sorted input `x`.
For details on how `osm` is calculated see the Notes section.
(slope, intercept, r) : tuple of floats, optional
Tuple containing the result of the least-squares fit, if that is
performed by `probplot`. `r` is the square root of the coefficient of
determination. If ``fit=False`` and ``plot=None``, this tuple is not
returned.
Notes
-----
Even if `plot` is given, the figure is not shown or saved by `probplot`;
``plt.show()`` or ``plt.savefig('figname.png')`` should be used after
calling `probplot`.
`probplot` generates a probability plot, which should not be confused with
a Q-Q or a P-P plot. Statsmodels has more extensive functionality of this
type, see ``statsmodels.api.ProbPlot``.
The formula used for the theoretical quantiles (horizontal axis of the
probability plot) is Filliben's estimate::
quantiles = dist.ppf(val), for
0.5**(1/n), for i = n
val = (i - 0.3175) / (n + 0.365), for i = 2, ..., n-1
1 - 0.5**(1/n), for i = 1
where ``i`` indicates the i-th ordered value and ``n`` is the total number
of values.
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
>>> nsample = 100
>>> rng = np.random.default_rng()
A t distribution with small degrees of freedom:
>>> ax1 = plt.subplot(221)
>>> x = stats.t.rvs(3, size=nsample, random_state=rng)
>>> res = stats.probplot(x, plot=plt)
A t distribution with larger degrees of freedom:
>>> ax2 = plt.subplot(222)
>>> x = stats.t.rvs(25, size=nsample, random_state=rng)
>>> res = stats.probplot(x, plot=plt)
A mixture of two normal distributions with broadcasting:
>>> ax3 = plt.subplot(223)
>>> x = stats.norm.rvs(loc=[0,5], scale=[1,1.5],
... size=(nsample//2,2), random_state=rng).ravel()
>>> res = stats.probplot(x, plot=plt)
A standard normal distribution:
>>> ax4 = plt.subplot(224)
>>> x = stats.norm.rvs(loc=0, scale=1, size=nsample, random_state=rng)
>>> res = stats.probplot(x, plot=plt)
Produce a new figure with a loggamma distribution, using the ``dist`` and
``sparams`` keywords:
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> x = stats.loggamma.rvs(c=2.5, size=500, random_state=rng)
>>> res = stats.probplot(x, dist=stats.loggamma, sparams=(2.5,), plot=ax)
>>> ax.set_title("Probplot for loggamma dist with shape parameter 2.5")
Show the results with Matplotlib:
>>> plt.show()
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Calculate the shape parameter that maximizes the PPCC.
The probability plot correlation coefficient (PPCC) plot can be used
to determine the optimal shape parameter for a one-parameter family
of distributions. ``ppcc_max`` returns the shape parameter that would
maximize the probability plot correlation coefficient for the given
data to a one-parameter family of distributions.
Parameters
----------
x : array_like
Input array.
brack : tuple, optional
Triple (a,b,c) where (a<b<c). If bracket consists of two numbers (a, c)
then they are assumed to be a starting interval for a downhill bracket
search (see `scipy.optimize.brent`).
dist : str or stats.distributions instance, optional
Distribution or distribution function name. Objects that look enough
like a stats.distributions instance (i.e. they have a ``ppf`` method)
are also accepted. The default is ``'tukeylambda'``.
Returns
-------
shape_value : float
The shape parameter at which the probability plot correlation
coefficient reaches its max value.
See Also
--------
ppcc_plot, probplot, boxcox
Notes
-----
The brack keyword serves as a starting point which is useful in corner
cases. One can use a plot to obtain a rough visual estimate of the location
for the maximum to start the search near it.
References
----------
.. [1] J.J. Filliben, "The Probability Plot Correlation Coefficient Test
for Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
.. [2] Engineering Statistics Handbook, NIST/SEMATEC,
https://www.itl.nist.gov/div898/handbook/eda/section3/ppccplot.htm
Examples
--------
First we generate some random data from a Weibull distribution
with shape parameter 2.5:
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
>>> rng = np.random.default_rng()
>>> c = 2.5
>>> x = stats.weibull_min.rvs(c, scale=4, size=2000, random_state=rng)
Generate the PPCC plot for this data with the Weibull distribution.
>>> fig, ax = plt.subplots(figsize=(8, 6))
>>> res = stats.ppcc_plot(x, c/2, 2*c, dist='weibull_min', plot=ax)
We calculate the value where the shape should reach its maximum and a
red line is drawn there. The line should coincide with the highest
point in the PPCC graph.
>>> cmax = stats.ppcc_max(x, brack=(c/2, 2*c), dist='weibull_min')
>>> ax.axvline(cmax, color='r')
>>> plt.show()
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The probability plot correlation coefficient (PPCC) plot can be used to
determine the optimal shape parameter for a one-parameter family of
distributions. It cannot be used for distributions without shape
parameters
(like the normal distribution) or with multiple shape parameters.
By default a Tukey-Lambda distribution (`stats.tukeylambda`) is used. A
Tukey-Lambda PPCC plot interpolates from long-tailed to short-tailed
distributions via an approximately normal one, and is therefore
particularly useful in practice.
Parameters
----------
x : array_like
Input array.
a, b : scalar
Lower and upper bounds of the shape parameter to use.
dist : str or stats.distributions instance, optional
Distribution or distribution function name. Objects that look enough
like a stats.distributions instance (i.e. they have a ``ppf`` method)
are also accepted. The default is ``'tukeylambda'``.
plot : object, optional
If given, plots PPCC against the shape parameter.
`plot` is an object that has to have methods "plot" and "text".
The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
or a custom object with the same methods.
Default is None, which means that no plot is created.
N : int, optional
Number of points on the horizontal axis (equally distributed from
`a` to `b`).
Returns
-------
svals : ndarray
The shape values for which `ppcc` was calculated.
ppcc : ndarray
The calculated probability plot correlation coefficient values.
See Also
--------
ppcc_max, probplot, boxcox_normplot, tukeylambda
References
----------
J.J. Filliben, "The Probability Plot Correlation Coefficient Test for
Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
Examples
--------
First we generate some random data from a Weibull distribution
with shape parameter 2.5, and plot the histogram of the data:
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
>>> rng = np.random.default_rng()
>>> c = 2.5
>>> x = stats.weibull_min.rvs(c, scale=4, size=2000, random_state=rng)
Take a look at the histogram of the data.
>>> fig1, ax = plt.subplots(figsize=(9, 4))
>>> ax.hist(x, bins=50)
>>> ax.set_title('Histogram of x')
>>> plt.show()
Now we explore this data with a PPCC plot as well as the related
probability plot and Box-Cox normplot. A red line is drawn where we
expect the PPCC value to be maximal (at the shape parameter ``c``
used above):
>>> fig2 = plt.figure(figsize=(12, 4))
>>> ax1 = fig2.add_subplot(1, 3, 1)
>>> ax2 = fig2.add_subplot(1, 3, 2)
>>> ax3 = fig2.add_subplot(1, 3, 3)
>>> res = stats.probplot(x, plot=ax1)
>>> res = stats.boxcox_normplot(x, -4, 4, plot=ax2)
>>> res = stats.ppcc_plot(x, c/2, 2*c, dist='weibull_min', plot=ax3)
>>> ax3.axvline(c, color='r')
>>> plt.show()
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The boxcox log-likelihood function.
Parameters
----------
lmb : scalar
Parameter for Box-Cox transformation. See `boxcox` for details.
data : array_like
Data to calculate Box-Cox log-likelihood for. If `data` is
multi-dimensional, the log-likelihood is calculated along the first
axis.
Returns
-------
llf : float or ndarray
Box-Cox log-likelihood of `data` given `lmb`. A float for 1-D `data`,
an array otherwise.
See Also
--------
boxcox, probplot, boxcox_normplot, boxcox_normmax
Notes
-----
The Box-Cox log-likelihood function is defined here as
.. math::
llf = (\lambda - 1) \sum_i(\log(x_i)) -
N/2 \log(\sum_i (y_i - \bar{y})^2 / N),
where ``y`` is the Box-Cox transformed input data ``x``.
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.axes_grid1.inset_locator import inset_axes
Generate some random variates and calculate Box-Cox log-likelihood values
for them for a range of ``lmbda`` values:
>>> rng = np.random.default_rng()
>>> x = stats.loggamma.rvs(5, loc=10, size=1000, random_state=rng)
>>> lmbdas = np.linspace(-2, 10)
>>> llf = np.zeros(lmbdas.shape, dtype=float)
>>> for ii, lmbda in enumerate(lmbdas):
... llf[ii] = stats.boxcox_llf(lmbda, x)
Also find the optimal lmbda value with `boxcox`:
>>> x_most_normal, lmbda_optimal = stats.boxcox(x)
Plot the log-likelihood as function of lmbda. Add the optimal lmbda as a
horizontal line to check that that's really the optimum:
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.plot(lmbdas, llf, 'b.-')
>>> ax.axhline(stats.boxcox_llf(lmbda_optimal, x), color='r')
>>> ax.set_xlabel('lmbda parameter')
>>> ax.set_ylabel('Box-Cox log-likelihood')
Now add some probability plots to show that where the log-likelihood is
maximized the data transformed with `boxcox` looks closest to normal:
>>> locs = [3, 10, 4] # 'lower left', 'center', 'lower right'
>>> for lmbda, loc in zip([-1, lmbda_optimal, 9], locs):
... xt = stats.boxcox(x, lmbda=lmbda)
... (osm, osr), (slope, intercept, r_sq) = stats.probplot(xt)
... ax_inset = inset_axes(ax, width="20%", height="20%", loc=loc)
... ax_inset.plot(osm, osr, 'c.', osm, slope*osm + intercept, 'k-')
... ax_inset.set_xticklabels([])
... ax_inset.set_yticklabels([])
... ax_inset.set_title(r'$\lambda=%1.2f$' % lmbda)
>>> plt.show()
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Parameters
----------
x : ndarray
Input array to be transformed.
If `lmbda` is not None, this is an alias of
`scipy.special.boxcox`.
Returns nan if ``x < 0``; returns -inf if ``x == 0 and lmbda < 0``.
If `lmbda` is None, array must be positive, 1-dimensional, and
non-constant.
lmbda : scalar, optional
If `lmbda` is None (default), find the value of `lmbda` that maximizes
the log-likelihood function and return it as the second output
argument.
If `lmbda` is not None, do the transformation for that value.
alpha : float, optional
If `lmbda` is None and `alpha` is not None (default), return the
``100 * (1-alpha)%`` confidence interval for `lmbda` as the third
output argument. Must be between 0.0 and 1.0.
If `lmbda` is not None, `alpha` is ignored.
optimizer : callable, optional
If `lmbda` is None, `optimizer` is the scalar optimizer used to find
the value of `lmbda` that minimizes the negative log-likelihood
function. `optimizer` is a callable that accepts one argument:
fun : callable
The objective function, which evaluates the negative
log-likelihood function at a provided value of `lmbda`
and returns an object, such as an instance of
`scipy.optimize.OptimizeResult`, which holds the optimal value of
`lmbda` in an attribute `x`.
See the example in `boxcox_normmax` or the documentation of
`scipy.optimize.minimize_scalar` for more information.
If `lmbda` is not None, `optimizer` is ignored.
Returns
-------
boxcox : ndarray
Box-Cox power transformed array.
maxlog : float, optional
If the `lmbda` parameter is None, the second returned argument is
the `lmbda` that maximizes the log-likelihood function.
(min_ci, max_ci) : tuple of float, optional
If `lmbda` parameter is None and `alpha` is not None, this returned
tuple of floats represents the minimum and maximum confidence limits
given `alpha`.
See Also
--------
probplot, boxcox_normplot, boxcox_normmax, boxcox_llf
Notes
-----
The Box-Cox transform is given by::
y = (x**lmbda - 1) / lmbda, for lmbda != 0
log(x), for lmbda = 0
`boxcox` requires the input data to be positive. Sometimes a Box-Cox
transformation provides a shift parameter to achieve this; `boxcox` does
not. Such a shift parameter is equivalent to adding a positive constant to
`x` before calling `boxcox`.
The confidence limits returned when `alpha` is provided give the interval
where:
.. math::
llf(\hat{\lambda}) - llf(\lambda) < \frac{1}{2}\chi^2(1 - \alpha, 1),
with ``llf`` the log-likelihood function and :math:`\chi^2` the chi-squared
function.
References
----------
G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal of the
Royal Statistical Society B, 26, 211-252 (1964).
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
We generate some random variates from a non-normal distribution and make a
probability plot for it, to show it is non-normal in the tails:
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(211)
>>> x = stats.loggamma.rvs(5, size=500) + 5
>>> prob = stats.probplot(x, dist=stats.norm, plot=ax1)
>>> ax1.set_xlabel('')
>>> ax1.set_title('Probplot against normal distribution')
We now use `boxcox` to transform the data so it's closest to normal:
>>> ax2 = fig.add_subplot(212)
>>> xt, _ = stats.boxcox(x)
>>> prob = stats.probplot(xt, dist=stats.norm, plot=ax2)
>>> ax2.set_title('Probplot after Box-Cox transformation')
>>> plt.show()
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Parameters
----------
x : array_like
Input array.
brack : 2-tuple, optional, default (-2.0, 2.0)
The starting interval for a downhill bracket search for the default
`optimize.brent` solver. Note that this is in most cases not
critical; the final result is allowed to be outside this bracket.
If `optimizer` is passed, `brack` must be None.
method : str, optional
The method to determine the optimal transform parameter (`boxcox`
``lmbda`` parameter). Options are:
'pearsonr' (default)
Maximizes the Pearson correlation coefficient between
``y = boxcox(x)`` and the expected values for ``y`` if `x` would be
normally-distributed.
'mle'
Minimizes the log-likelihood `boxcox_llf`. This is the method used
in `boxcox`.
'all'
Use all optimization methods available, and return all results.
Useful to compare different methods.
optimizer : callable, optional
`optimizer` is a callable that accepts one argument:
fun : callable
The objective function to be optimized. `fun` accepts one argument,
the Box-Cox transform parameter `lmbda`, and returns the negative
log-likelihood function at the provided value. The job of `optimizer`
is to find the value of `lmbda` that minimizes `fun`.
and returns an object, such as an instance of
`scipy.optimize.OptimizeResult`, which holds the optimal value of
`lmbda` in an attribute `x`.
See the example below or the documentation of
`scipy.optimize.minimize_scalar` for more information.
Returns
-------
maxlog : float or ndarray
The optimal transform parameter found. An array instead of a scalar
for ``method='all'``.
See Also
--------
boxcox, boxcox_llf, boxcox_normplot, scipy.optimize.minimize_scalar
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
We can generate some data and determine the optimal ``lmbda`` in various
ways:
>>> rng = np.random.default_rng()
>>> x = stats.loggamma.rvs(5, size=30, random_state=rng) + 5
>>> y, lmax_mle = stats.boxcox(x)
>>> lmax_pearsonr = stats.boxcox_normmax(x)
>>> lmax_mle
2.217563431465757
>>> lmax_pearsonr
2.238318660200961
>>> stats.boxcox_normmax(x, method='all')
array([2.23831866, 2.21756343])
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.boxcox_normplot(x, -10, 10, plot=ax)
>>> ax.axvline(lmax_mle, color='r')
>>> ax.axvline(lmax_pearsonr, color='g', ls='--')
>>> plt.show()
Alternatively, we can define our own `optimizer` function. Suppose we
are only interested in values of `lmbda` on the interval [6, 7], we
want to use `scipy.optimize.minimize_scalar` with ``method='bounded'``,
and we want to use tighter tolerances when optimizing the log-likelihood
function. To do this, we define a function that accepts positional argument
`fun` and uses `scipy.optimize.minimize_scalar` to minimize `fun` subject
to the provided bounds and tolerances:
>>> from scipy import optimize
>>> options = {'xatol': 1e-12} # absolute tolerance on `x`
>>> def optimizer(fun):
... return optimize.minimize_scalar(fun, bounds=(6, 7),
... method="bounded", options=options)
>>> stats.boxcox_normmax(x, optimizer=optimizer)
6.000...
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|| d� � t |dd|�� � || fS )z�Compute parameters for a Box-Cox or Yeo-Johnson normality plot,
optionally show it.
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A Box-Cox normality plot shows graphically what the best transformation
parameter is to use in `boxcox` to obtain a distribution that is close
to normal.
Parameters
----------
x : array_like
Input array.
la, lb : scalar
The lower and upper bounds for the ``lmbda`` values to pass to `boxcox`
for Box-Cox transformations. These are also the limits of the
horizontal axis of the plot if that is generated.
plot : object, optional
If given, plots the quantiles and least squares fit.
`plot` is an object that has to have methods "plot" and "text".
The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
or a custom object with the same methods.
Default is None, which means that no plot is created.
N : int, optional
Number of points on the horizontal axis (equally distributed from
`la` to `lb`).
Returns
-------
lmbdas : ndarray
The ``lmbda`` values for which a Box-Cox transform was done.
ppcc : ndarray
Probability Plot Correlelation Coefficient, as obtained from `probplot`
when fitting the Box-Cox transformed input `x` against a normal
distribution.
See Also
--------
probplot, boxcox, boxcox_normmax, boxcox_llf, ppcc_max
Notes
-----
Even if `plot` is given, the figure is not shown or saved by
`boxcox_normplot`; ``plt.show()`` or ``plt.savefig('figname.png')``
should be used after calling `probplot`.
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
Generate some non-normally distributed data, and create a Box-Cox plot:
>>> x = stats.loggamma.rvs(5, size=500) + 5
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.boxcox_normplot(x, -20, 20, plot=ax)
Determine and plot the optimal ``lmbda`` to transform ``x`` and plot it in
the same plot:
>>> _, maxlog = stats.boxcox(x)
>>> ax.axvline(maxlog, color='r')
>>> plt.show()
r7 �r, �rm r' r( r� r� s r_ r9 r9 . s � �B �X�q�"�b�$�� 2� 2�2r` c � � t j | � � } | j dk r| S t j | j t j � � rt
d� � �t j | j t j � � r!| � t j d�� � } |�t | |� � S t | � � }t | |� � }||fS )a� Return a dataset transformed by a Yeo-Johnson power transformation.
Parameters
----------
x : ndarray
Input array. Should be 1-dimensional.
lmbda : float, optional
If ``lmbda`` is ``None``, find the lambda that maximizes the
log-likelihood function and return it as the second output argument.
Otherwise the transformation is done for the given value.
Returns
-------
yeojohnson: ndarray
Yeo-Johnson power transformed array.
maxlog : float, optional
If the `lmbda` parameter is None, the second returned argument is
the lambda that maximizes the log-likelihood function.
See Also
--------
probplot, yeojohnson_normplot, yeojohnson_normmax, yeojohnson_llf, boxcox
Notes
-----
The Yeo-Johnson transform is given by::
y = ((x + 1)**lmbda - 1) / lmbda, for x >= 0, lmbda != 0
log(x + 1), for x >= 0, lmbda = 0
-((-x + 1)**(2 - lmbda) - 1) / (2 - lmbda), for x < 0, lmbda != 2
-log(-x + 1), for x < 0, lmbda = 2
Unlike `boxcox`, `yeojohnson` does not require the input data to be
positive.
.. versionadded:: 1.2.0
References
----------
I. Yeo and R.A. Johnson, "A New Family of Power Transformations to
Improve Normality or Symmetry", Biometrika 87.4 (2000):
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
We generate some random variates from a non-normal distribution and make a
probability plot for it, to show it is non-normal in the tails:
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(211)
>>> x = stats.loggamma.rvs(5, size=500) + 5
>>> prob = stats.probplot(x, dist=stats.norm, plot=ax1)
>>> ax1.set_xlabel('')
>>> ax1.set_title('Probplot against normal distribution')
We now use `yeojohnson` to transform the data so it's closest to normal:
>>> ax2 = fig.add_subplot(212)
>>> xt, lmbda = stats.yeojohnson(x)
>>> prob = stats.probplot(xt, dist=stats.norm, plot=ax2)
>>> ax2.set_title('Probplot after Yeo-Johnson transformation')
>>> plt.show()
r z>Yeo-Johnson transformation is not defined for complex numbers.F)�copy) r� r
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parameter `lmbda`.
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The yeojohnson log-likelihood function.
Parameters
----------
lmb : scalar
Parameter for Yeo-Johnson transformation. See `yeojohnson` for
details.
data : array_like
Data to calculate Yeo-Johnson log-likelihood for. If `data` is
multi-dimensional, the log-likelihood is calculated along the first
axis.
Returns
-------
llf : float
Yeo-Johnson log-likelihood of `data` given `lmb`.
See Also
--------
yeojohnson, probplot, yeojohnson_normplot, yeojohnson_normmax
Notes
-----
The Yeo-Johnson log-likelihood function is defined here as
.. math::
llf = -N/2 \log(\hat{\sigma}^2) + (\lambda - 1)
\sum_i \text{ sign }(x_i)\log(|x_i| + 1)
where :math:`\hat{\sigma}^2` is estimated variance of the Yeo-Johnson
transformed input data ``x``.
.. versionadded:: 1.2.0
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.axes_grid1.inset_locator import inset_axes
Generate some random variates and calculate Yeo-Johnson log-likelihood
values for them for a range of ``lmbda`` values:
>>> x = stats.loggamma.rvs(5, loc=10, size=1000)
>>> lmbdas = np.linspace(-2, 10)
>>> llf = np.zeros(lmbdas.shape, dtype=float)
>>> for ii, lmbda in enumerate(lmbdas):
... llf[ii] = stats.yeojohnson_llf(lmbda, x)
Also find the optimal lmbda value with `yeojohnson`:
>>> x_most_normal, lmbda_optimal = stats.yeojohnson(x)
Plot the log-likelihood as function of lmbda. Add the optimal lmbda as a
horizontal line to check that that's really the optimum:
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.plot(lmbdas, llf, 'b.-')
>>> ax.axhline(stats.yeojohnson_llf(lmbda_optimal, x), color='r')
>>> ax.set_xlabel('lmbda parameter')
>>> ax.set_ylabel('Yeo-Johnson log-likelihood')
Now add some probability plots to show that where the log-likelihood is
maximized the data transformed with `yeojohnson` looks closest to normal:
>>> locs = [3, 10, 4] # 'lower left', 'center', 'lower right'
>>> for lmbda, loc in zip([-1, lmbda_optimal, 9], locs):
... xt = stats.yeojohnson(x, lmbda=lmbda)
... (osm, osr), (slope, intercept, r_sq) = stats.probplot(xt)
... ax_inset = inset_axes(ax, width="20%", height="20%", loc=loc)
... ax_inset.plot(osm, osr, 'c.', osm, slope*osm + intercept, 'k-')
... ax_inset.set_xticklabels([])
... ax_inset.set_yticklabels([])
... ax_inset.set_title(r'$\lambda=%1.2f$' % lmbda)
>>> plt.show()
r r� r- r! )r� r
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�Q���� �=�.� 9�:�:�:� �]�N�� �]�N���� �q��R�W�T�]�]�R�V�B�F�4�L�L�1�,<�%=�%=�=�B�B��B�J�J�J�L���� �Nr` �re r- c � � d� }t j d�� � 5 t j ||| f�� � cddd� � S # 1 swxY w Y dS )a� Compute optimal Yeo-Johnson transform parameter.
Compute optimal Yeo-Johnson transform parameter for input data, using
maximum likelihood estimation.
Parameters
----------
x : array_like
Input array.
brack : 2-tuple, optional
The starting interval for a downhill bracket search with
`optimize.brent`. Note that this is in most cases not critical; the
final result is allowed to be outside this bracket.
Returns
-------
maxlog : float
The optimal transform parameter found.
See Also
--------
yeojohnson, yeojohnson_llf, yeojohnson_normplot
Notes
-----
.. versionadded:: 1.2.0
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
Generate some data and determine optimal ``lmbda``
>>> rng = np.random.default_rng()
>>> x = stats.loggamma.rvs(5, size=30, random_state=rng) + 5
>>> lmax = stats.yeojohnson_normmax(x)
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.yeojohnson_normplot(x, -10, 10, plot=ax)
>>> ax.axvline(lmax, color='r')
>>> plt.show()
c �l � t | |� � }t j |t j |� � < | S rx )rH r� rB �isinf)r� rW �llfs r_ �_neg_llfz$yeojohnson_normmax.<locals>._neg_llfz s0 � � �U�D�)�)�� !�f�W��B�H�S�M�M���t� r` �ignore��invalidr� N)r� �errstater r� )rm r� rN s r_ rJ rJ J s� � �`� � �
��X� &� &� &� @� @��~�h�e�1�$�?�?�?�@� @� @� @� @� @� @� @� @� @� @� @���� @� @� @� @� @� @s �>�A�Ac �* � t d| ||||� � S )a� Compute parameters for a Yeo-Johnson normality plot, optionally show it.
A Yeo-Johnson normality plot shows graphically what the best
transformation parameter is to use in `yeojohnson` to obtain a
distribution that is close to normal.
Parameters
----------
x : array_like
Input array.
la, lb : scalar
The lower and upper bounds for the ``lmbda`` values to pass to
`yeojohnson` for Yeo-Johnson transformations. These are also the
limits of the horizontal axis of the plot if that is generated.
plot : object, optional
If given, plots the quantiles and least squares fit.
`plot` is an object that has to have methods "plot" and "text".
The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
or a custom object with the same methods.
Default is None, which means that no plot is created.
N : int, optional
Number of points on the horizontal axis (equally distributed from
`la` to `lb`).
Returns
-------
lmbdas : ndarray
The ``lmbda`` values for which a Yeo-Johnson transform was done.
ppcc : ndarray
Probability Plot Correlelation Coefficient, as obtained from `probplot`
when fitting the Box-Cox transformed input `x` against a normal
distribution.
See Also
--------
probplot, yeojohnson, yeojohnson_normmax, yeojohnson_llf, ppcc_max
Notes
-----
Even if `plot` is given, the figure is not shown or saved by
`boxcox_normplot`; ``plt.show()`` or ``plt.savefig('figname.png')``
should be used after calling `probplot`.
.. versionadded:: 1.2.0
Examples
--------
>>> from scipy import stats
>>> import matplotlib.pyplot as plt
Generate some non-normally distributed data, and create a Yeo-Johnson plot:
>>> x = stats.loggamma.rvs(5, size=500) + 5
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.yeojohnson_normplot(x, -20, 20, plot=ax)
Determine and plot the optimal ``lmbda`` to transform ``x`` and plot it in
the same plot:
>>> _, maxlog = stats.yeojohnson(x)
>>> ax.axvline(maxlog, color='r')
>>> plt.show()
rI r. r/ s r_ rK rK � s � �F �\�1�b�"�d�A� 6� 6�6r` �
ShapiroResult)rN �pvaluec � � t j | � � } t | � � }|dk rt d� � �| t j | � � z
} t |d� � }d}t
| � � }t j ||d|dz � |� � \ }}}}|dvrt j
d� � |d k rt j
d
� � t ||� � S ) a( Perform the Shapiro-Wilk test for normality.
The Shapiro-Wilk test tests the null hypothesis that the
data was drawn from a normal distribution.
Parameters
----------
x : array_like
Array of sample data.
Returns
-------
statistic : float
The test statistic.
p-value : float
The p-value for the hypothesis test.
See Also
--------
anderson : The Anderson-Darling test for normality
kstest : The Kolmogorov-Smirnov test for goodness of fit.
Notes
-----
The algorithm used is described in [4]_ but censoring parameters as
described are not implemented. For N > 5000 the W test statistic is accurate
but the p-value may not be.
The chance of rejecting the null hypothesis when it is true is close to 5%
regardless of sample size.
References
----------
.. [1] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
.. [2] Shapiro, S. S. & Wilk, M.B (1965). An analysis of variance test for
normality (complete samples), Biometrika, Vol. 52, pp. 591-611.
.. [3] Razali, N. M. & Wah, Y. B. (2011) Power comparisons of Shapiro-Wilk,
Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests, Journal of
Statistical Modeling and Analytics, Vol. 2, pp. 21-33.
.. [4] ALGORITHM AS R94 APPL. STATIST. (1995) VOL. 44, NO. 4.
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> x = stats.norm.rvs(loc=5, scale=3, size=100, random_state=rng)
>>> shapiro_test = stats.shapiro(x)
>>> shapiro_test
ShapiroResult(statistic=0.9813305735588074, pvalue=0.16855233907699585)
>>> shapiro_test.statistic
0.9813305735588074
>>> shapiro_test.pvalue
0.16855233907699585
r� z Data must be at least length 3.�fr Nr- )r r- zGInput data for shapiro has range zero. The results may not be accurate.i� z)p-value may not be accurate for N > 5000.) r� r rf rT �medianr r
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}d }t! j d |� � � }t j |� � |_ t; t= t |� � |d!|�"� � }t? || | |�#� � S )$a& Anderson-Darling test for data coming from a particular distribution.
The Anderson-Darling test tests the null hypothesis that a sample is
drawn from a population that follows a particular distribution.
For the Anderson-Darling test, the critical values depend on
which distribution is being tested against. This function works
for normal, exponential, logistic, or Gumbel (Extreme Value
Type I) distributions.
Parameters
----------
x : array_like
Array of sample data.
dist : {'norm', 'expon', 'logistic', 'gumbel', 'gumbel_l', 'gumbel_r', 'extreme1'}, optional
The type of distribution to test against. The default is 'norm'.
The names 'extreme1', 'gumbel_l' and 'gumbel' are synonyms for the
same distribution.
Returns
-------
result : AndersonResult
An object with the following attributes:
statistic : float
The Anderson-Darling test statistic.
critical_values : list
The critical values for this distribution.
significance_level : list
The significance levels for the corresponding critical values
in percents. The function returns critical values for a
differing set of significance levels depending on the
distribution that is being tested against.
fit_result : `~scipy.stats._result_classes.FitResult`
An object containing the results of fitting the distribution to
the data.
See Also
--------
kstest : The Kolmogorov-Smirnov test for goodness-of-fit.
Notes
-----
Critical values provided are for the following significance levels:
normal/exponential
15%, 10%, 5%, 2.5%, 1%
logistic
25%, 10%, 5%, 2.5%, 1%, 0.5%
Gumbel
25%, 10%, 5%, 2.5%, 1%
If the returned statistic is larger than these critical values then
for the corresponding significance level, the null hypothesis that
the data come from the chosen distribution can be rejected.
The returned statistic is referred to as 'A2' in the references.
References
----------
.. [1] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
.. [2] Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and
Some Comparisons, Journal of the American Statistical Association,
Vol. 69, pp. 730-737.
.. [3] Stephens, M. A. (1976). Asymptotic Results for Goodness-of-Fit
Statistics with Unknown Parameters, Annals of Statistics, Vol. 4,
pp. 357-369.
.. [4] Stephens, M. A. (1977). Goodness of Fit for the Extreme Value
Distribution, Biometrika, Vol. 64, pp. 583-588.
.. [5] Stephens, M. A. (1977). Goodness of Fit with Special Reference
to Tests for Exponentiality , Technical Report No. 262,
Department of Statistics, Stanford University, Stanford, CA.
.. [6] Stephens, M. A. (1979). Tests of Fit for the Logistic Distribution
Based on the Empirical Distribution Function, Biometrika, Vol. 66,
pp. 591-595.
Examples
--------
Test the null hypothesis that a random sample was drawn from a normal
distribution (with unspecified mean and standard deviation).
>>> import numpy as np
>>> from scipy.stats import anderson
>>> rng = np.random.default_rng()
>>> data = rng.random(size=35)
>>> res = anderson(data)
>>> res.statistic
0.8398018749744764
>>> res.critical_values
array([0.527, 0.6 , 0.719, 0.839, 0.998])
>>> res.significance_level
array([15. , 10. , 5. , 2.5, 1. ])
The value of the statistic (barely) exceeds the critical value associated
with a significance level of 2.5%, so the null hypothesis may be rejected
at a significance level of 2.5%, but not at a significance level of 1%.
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----------
samples : sequence of 1-D array_like
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Z : array_like
Sorted array of all observations.
Zstar : array_like
Sorted array of unique observations.
k : int
Number of samples.
n : array_like
Number of observations in each sample.
N : int
Total number of observations.
Returns
-------
A2aKN : float
The A2aKN statistics of Scholz and Stephens 1987.
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Parameters
----------
samples : sequence of 1-D array_like
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Zstar : array_like
Sorted array of unique observations.
k : int
Number of samples.
n : array_like
Number of observations in each sample.
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Total number of observations.
Returns
-------
A2KN : float
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that k-samples are drawn from the same population without having
to specify the distribution function of that population. The
critical values depend on the number of samples.
Parameters
----------
samples : sequence of 1-D array_like
Array of sample data in arrays.
midrank : bool, optional
Type of Anderson-Darling test which is computed. Default
(True) is the midrank test applicable to continuous and
discrete populations. If False, the right side empirical
distribution is used.
Returns
-------
res : Anderson_ksampResult
An object containing attributes:
statistic : float
Normalized k-sample Anderson-Darling test statistic.
critical_values : array
The critical values for significance levels 25%, 10%, 5%, 2.5%, 1%,
0.5%, 0.1%.
pvalue : float
The approximate p-value of the test. The value is floored / capped
at 0.1% / 25%.
Raises
------
ValueError
If less than 2 samples are provided, a sample is empty, or no
distinct observations are in the samples.
See Also
--------
ks_2samp : 2 sample Kolmogorov-Smirnov test
anderson : 1 sample Anderson-Darling test
Notes
-----
[1]_ defines three versions of the k-sample Anderson-Darling test:
one for continuous distributions and two for discrete
distributions, in which ties between samples may occur. The
default of this routine is to compute the version based on the
midrank empirical distribution function. This test is applicable
to continuous and discrete data. If midrank is set to False, the
right side empirical distribution is used for a test for discrete
data. According to [1]_, the two discrete test statistics differ
only slightly if a few collisions due to round-off errors occur in
the test not adjusted for ties between samples.
The critical values corresponding to the significance levels from 0.01
to 0.25 are taken from [1]_. p-values are floored / capped
at 0.1% / 25%. Since the range of critical values might be extended in
future releases, it is recommended not to test ``p == 0.25``, but rather
``p >= 0.25`` (analogously for the lower bound).
.. versionadded:: 0.14.0
References
----------
.. [1] Scholz, F. W and Stephens, M. A. (1987), K-Sample
Anderson-Darling Tests, Journal of the American Statistical
Association, Vol. 82, pp. 918-924.
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> res = stats.anderson_ksamp([rng.normal(size=50),
... rng.normal(loc=0.5, size=30)])
>>> res.statistic, res.pvalue
(1.974403288713695, 0.04991293614572478)
>>> res.critical_values
array([0.325, 1.226, 1.961, 2.718, 3.752, 4.592, 6.546])
The null hypothesis that the two random samples come from the same
distribution can be rejected at the 5% level because the returned
test value is greater than the critical value for 5% (1.961) but
not at the 2.5% level. The interpolation gives an approximate
p-value of 4.99%.
>>> res = stats.anderson_ksamp([rng.normal(size=50),
... rng.normal(size=30), rng.normal(size=20)])
>>> res.statistic, res.pvalue
(-0.29103725200789504, 0.25)
>>> res.critical_values
array([ 0.44925884, 1.3052767 , 1.9434184 , 2.57696569, 3.41634856,
4.07210043, 5.56419101])
The null hypothesis cannot be rejected for three samples from an
identical distribution. The reported p-value (25%) has been capped and
may not be very accurate (since it corresponds to the value 0.449
whereas the statistic is -0.291).
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The Ansari-Bradley test ([1]_, [2]_) is a non-parametric test
for the equality of the scale parameter of the distributions
from which two samples were drawn. The null hypothesis states that
the ratio of the scale of the distribution underlying `x` to the scale
of the distribution underlying `y` is 1.
Parameters
----------
x, y : array_like
Arrays of sample data.
alternative : {'two-sided', 'less', 'greater'}, optional
Defines the alternative hypothesis. Default is 'two-sided'.
The following options are available:
* 'two-sided': the ratio of scales is not equal to 1.
* 'less': the ratio of scales is less than 1.
* 'greater': the ratio of scales is greater than 1.
.. versionadded:: 1.7.0
Returns
-------
statistic : float
The Ansari-Bradley test statistic.
pvalue : float
The p-value of the hypothesis test.
See Also
--------
fligner : A non-parametric test for the equality of k variances
mood : A non-parametric test for the equality of two scale parameters
Notes
-----
The p-value given is exact when the sample sizes are both less than
55 and there are no ties, otherwise a normal approximation for the
p-value is used.
References
----------
.. [1] Ansari, A. R. and Bradley, R. A. (1960) Rank-sum tests for
dispersions, Annals of Mathematical Statistics, 31, 1174-1189.
.. [2] Sprent, Peter and N.C. Smeeton. Applied nonparametric
statistical methods. 3rd ed. Chapman and Hall/CRC. 2001.
Section 5.8.2.
.. [3] Nathaniel E. Helwig "Nonparametric Dispersion and Equality
Tests" at http://users.stat.umn.edu/~helwig/notes/npde-Notes.pdf
Examples
--------
>>> import numpy as np
>>> from scipy.stats import ansari
>>> rng = np.random.default_rng()
For these examples, we'll create three random data sets. The first
two, with sizes 35 and 25, are drawn from a normal distribution with
mean 0 and standard deviation 2. The third data set has size 25 and
is drawn from a normal distribution with standard deviation 1.25.
>>> x1 = rng.normal(loc=0, scale=2, size=35)
>>> x2 = rng.normal(loc=0, scale=2, size=25)
>>> x3 = rng.normal(loc=0, scale=1.25, size=25)
First we apply `ansari` to `x1` and `x2`. These samples are drawn
from the same distribution, so we expect the Ansari-Bradley test
should not lead us to conclude that the scales of the distributions
are different.
>>> ansari(x1, x2)
AnsariResult(statistic=541.0, pvalue=0.9762532927399098)
With a p-value close to 1, we cannot conclude that there is a
significant difference in the scales (as expected).
Now apply the test to `x1` and `x3`:
>>> ansari(x1, x3)
AnsariResult(statistic=425.0, pvalue=0.0003087020407974518)
The probability of observing such an extreme value of the statistic
under the null hypothesis of equal scales is only 0.03087%. We take this
as evidence against the null hypothesis in favor of the alternative:
the scales of the distributions from which the samples were drawn
are not equal.
We can use the `alternative` parameter to perform a one-tailed test.
In the above example, the scale of `x1` is greater than `x3` and so
the ratio of scales of `x1` and `x3` is greater than 1. This means
that the p-value when ``alternative='greater'`` should be near 0 and
hence we should be able to reject the null hypothesis:
>>> ansari(x1, x3, alternative='greater')
AnsariResult(statistic=425.0, pvalue=0.0001543510203987259)
As we can see, the p-value is indeed quite low. Use of
``alternative='less'`` should thus yield a large p-value:
>>> ansari(x1, x3, alternative='less')
AnsariResult(statistic=425.0, pvalue=0.9998643258449039)
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Perform Bartlett's test for equal variances.
Bartlett's test tests the null hypothesis that all input samples
are from populations with equal variances. For samples
from significantly non-normal populations, Levene's test
`levene` is more robust.
Parameters
----------
sample1, sample2, ... : array_like
arrays of sample data. Only 1d arrays are accepted, they may have
different lengths.
Returns
-------
statistic : float
The test statistic.
pvalue : float
The p-value of the test.
See Also
--------
fligner : A non-parametric test for the equality of k variances
levene : A robust parametric test for equality of k variances
Notes
-----
Conover et al. (1981) examine many of the existing parametric and
nonparametric tests by extensive simulations and they conclude that the
tests proposed by Fligner and Killeen (1976) and Levene (1960) appear to be
superior in terms of robustness of departures from normality and power
([3]_).
References
----------
.. [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm
.. [2] Snedecor, George W. and Cochran, William G. (1989), Statistical
Methods, Eighth Edition, Iowa State University Press.
.. [3] Park, C. and Lindsay, B. G. (1999). Robust Scale Estimation and
Hypothesis Testing based on Quadratic Inference Function. Technical
Report #99-03, Center for Likelihood Studies, Pennsylvania State
University.
.. [4] Bartlett, M. S. (1937). Properties of Sufficiency and Statistical
Tests. Proceedings of the Royal Society of London. Series A,
Mathematical and Physical Sciences, Vol. 160, No.901, pp. 268-282.
Examples
--------
Test whether or not the lists `a`, `b` and `c` come from populations
with equal variances.
>>> import numpy as np
>>> from scipy.stats import bartlett
>>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
>>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
>>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
>>> stat, p = bartlett(a, b, c)
>>> p
1.1254782518834628e-05
The very small p-value suggests that the populations do not have equal
variances.
This is not surprising, given that the sample variance of `b` is much
larger than that of `a` and `c`:
>>> [np.var(x, ddof=1) for x in [a, b, c]]
[0.007054444444444413, 0.13073888888888888, 0.008890000000000002]
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Perform Levene test for equal variances.
The Levene test tests the null hypothesis that all input samples
are from populations with equal variances. Levene's test is an
alternative to Bartlett's test `bartlett` in the case where
there are significant deviations from normality.
Parameters
----------
sample1, sample2, ... : array_like
The sample data, possibly with different lengths. Only one-dimensional
samples are accepted.
center : {'mean', 'median', 'trimmed'}, optional
Which function of the data to use in the test. The default
is 'median'.
proportiontocut : float, optional
When `center` is 'trimmed', this gives the proportion of data points
to cut from each end. (See `scipy.stats.trim_mean`.)
Default is 0.05.
Returns
-------
statistic : float
The test statistic.
pvalue : float
The p-value for the test.
Notes
-----
Three variations of Levene's test are possible. The possibilities
and their recommended usages are:
* 'median' : Recommended for skewed (non-normal) distributions>
* 'mean' : Recommended for symmetric, moderate-tailed distributions.
* 'trimmed' : Recommended for heavy-tailed distributions.
The test version using the mean was proposed in the original article
of Levene ([2]_) while the median and trimmed mean have been studied by
Brown and Forsythe ([3]_), sometimes also referred to as Brown-Forsythe
test.
References
----------
.. [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35a.htm
.. [2] Levene, H. (1960). In Contributions to Probability and Statistics:
Essays in Honor of Harold Hotelling, I. Olkin et al. eds.,
Stanford University Press, pp. 278-292.
.. [3] Brown, M. B. and Forsythe, A. B. (1974), Journal of the American
Statistical Association, 69, 364-367
Examples
--------
Test whether or not the lists `a`, `b` and `c` come from populations
with equal variances.
>>> import numpy as np
>>> from scipy.stats import levene
>>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
>>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
>>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
>>> stat, p = levene(a, b, c)
>>> p
0.002431505967249681
The small p-value suggests that the populations do not have equal
variances.
This is not surprising, given that the sample variance of `b` is much
larger than that of `a` and `c`:
>>> [np.var(x, ddof=1) for x in [a, b, c]]
[0.007054444444444413, 0.13073888888888888, 0.008890000000000002]
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||� � z }t d|� � S )a Perform a test that the probability of success is p.
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that the probability of success in a Bernoulli experiment
is `p`.
.. deprecated:: 1.10.0
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be removed in Scipy 1.12.0.
Parameters
----------
x : int or array_like
The number of successes, or if x has length 2, it is the
number of successes and the number of failures.
n : int
The number of trials. This is ignored if x gives both the
number of successes and failures.
p : float, optional
The hypothesized probability of success. ``0 <= p <= 1``. The
default value is ``p = 0.5``.
alternative : {'two-sided', 'greater', 'less'}, optional
Indicates the alternative hypothesis. The default value is
'two-sided'.
Returns
-------
p-value : float
The p-value of the hypothesis test.
References
----------
.. [1] https://en.wikipedia.org/wiki/Binomial_test
Examples
--------
>>> from scipy import stats
A car manufacturer claims that no more than 10% of their cars are unsafe.
15 cars are inspected for safety, 3 were found to be unsafe. Test the
manufacturer's claim:
>>> stats.binom_test(3, n=15, p=0.1, alternative='greater')
0.18406106910639114
The null hypothesis cannot be rejected at the 5% level of significance
because the returned p-value is greater than the critical value of 5%.
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Perform Fligner-Killeen test for equality of variance.
Fligner's test tests the null hypothesis that all input samples
are from populations with equal variances. Fligner-Killeen's test is
distribution free when populations are identical [2]_.
Parameters
----------
sample1, sample2, ... : array_like
Arrays of sample data. Need not be the same length.
center : {'mean', 'median', 'trimmed'}, optional
Keyword argument controlling which function of the data is used in
computing the test statistic. The default is 'median'.
proportiontocut : float, optional
When `center` is 'trimmed', this gives the proportion of data points
to cut from each end. (See `scipy.stats.trim_mean`.)
Default is 0.05.
Returns
-------
statistic : float
The test statistic.
pvalue : float
The p-value for the hypothesis test.
See Also
--------
bartlett : A parametric test for equality of k variances in normal samples
levene : A robust parametric test for equality of k variances
Notes
-----
As with Levene's test there are three variants of Fligner's test that
differ by the measure of central tendency used in the test. See `levene`
for more information.
Conover et al. (1981) examine many of the existing parametric and
nonparametric tests by extensive simulations and they conclude that the
tests proposed by Fligner and Killeen (1976) and Levene (1960) appear to be
superior in terms of robustness of departures from normality and power [3]_.
References
----------
.. [1] Park, C. and Lindsay, B. G. (1999). Robust Scale Estimation and
Hypothesis Testing based on Quadratic Inference Function. Technical
Report #99-03, Center for Likelihood Studies, Pennsylvania State
University.
https://cecas.clemson.edu/~cspark/cv/paper/qif/draftqif2.pdf
.. [2] Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample
tests for scale. 'Journal of the American Statistical Association.'
71(353), 210-213.
.. [3] Park, C. and Lindsay, B. G. (1999). Robust Scale Estimation and
Hypothesis Testing based on Quadratic Inference Function. Technical
Report #99-03, Center for Likelihood Studies, Pennsylvania State
University.
.. [4] Conover, W. J., Johnson, M. E. and Johnson M. M. (1981). A
comparative study of tests for homogeneity of variances, with
applications to the outer continental shelf biding data.
Technometrics, 23(4), 351-361.
Examples
--------
Test whether or not the lists `a`, `b` and `c` come from populations
with equal variances.
>>> import numpy as np
>>> from scipy.stats import fligner
>>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
>>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
>>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
>>> stat, p = fligner(a, b, c)
>>> p
0.00450826080004775
The small p-value suggests that the populations do not have equal
variances.
This is not surprising, given that the sample variance of `b` is much
larger than that of `a` and `c`:
>>> [np.var(x, ddof=1) for x in [a, b, c]]
[0.007054444444444413, 0.13073888888888888, 0.008890000000000002]
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Mood's two-sample test for scale parameters is a non-parametric
test for the null hypothesis that two samples are drawn from the
same distribution with the same scale parameter.
Parameters
----------
x, y : array_like
Arrays of sample data.
axis : int, optional
The axis along which the samples are tested. `x` and `y` can be of
different length along `axis`.
If `axis` is None, `x` and `y` are flattened and the test is done on
all values in the flattened arrays.
alternative : {'two-sided', 'less', 'greater'}, optional
Defines the alternative hypothesis. Default is 'two-sided'.
The following options are available:
* 'two-sided': the scales of the distributions underlying `x` and `y`
are different.
* 'less': the scale of the distribution underlying `x` is less than
the scale of the distribution underlying `y`.
* 'greater': the scale of the distribution underlying `x` is greater
than the scale of the distribution underlying `y`.
.. versionadded:: 1.7.0
Returns
-------
res : SignificanceResult
An object containing attributes:
statistic : scalar or ndarray
The z-score for the hypothesis test. For 1-D inputs a scalar is
returned.
pvalue : scalar ndarray
The p-value for the hypothesis test.
See Also
--------
fligner : A non-parametric test for the equality of k variances
ansari : A non-parametric test for the equality of 2 variances
bartlett : A parametric test for equality of k variances in normal samples
levene : A parametric test for equality of k variances
Notes
-----
The data are assumed to be drawn from probability distributions ``f(x)``
and ``f(x/s) / s`` respectively, for some probability density function f.
The null hypothesis is that ``s == 1``.
For multi-dimensional arrays, if the inputs are of shapes
``(n0, n1, n2, n3)`` and ``(n0, m1, n2, n3)``, then if ``axis=1``, the
resulting z and p values will have shape ``(n0, n2, n3)``. Note that
``n1`` and ``m1`` don't have to be equal, but the other dimensions do.
References
----------
[1] Mielke, Paul W. "Note on Some Squared Rank Tests with Existing Ties."
Technometrics, vol. 9, no. 2, 1967, pp. 312-14. JSTOR,
https://doi.org/10.2307/1266427. Accessed 18 May 2022.
Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> x2 = rng.standard_normal((2, 45, 6, 7))
>>> x1 = rng.standard_normal((2, 30, 6, 7))
>>> res = stats.mood(x1, x2, axis=1)
>>> res.pvalue.shape
(2, 6, 7)
Find the number of points where the difference in scale is not significant:
>>> (res.pvalue > 0.1).sum()
78
Perform the test with different scales:
>>> x1 = rng.standard_normal((2, 30))
>>> x2 = rng.standard_normal((2, 35)) * 10.0
>>> stats.mood(x1, x2, axis=1)
SignificanceResult(statistic=array([-5.76174136, -6.12650783]),
pvalue=array([8.32505043e-09, 8.98287869e-10]))
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k r||_ |S )#a� Calculate the Wilcoxon signed-rank test.
The Wilcoxon signed-rank test tests the null hypothesis that two
related paired samples come from the same distribution. In particular,
it tests whether the distribution of the differences ``x - y`` is symmetric
about zero. It is a non-parametric version of the paired T-test.
Parameters
----------
x : array_like
Either the first set of measurements (in which case ``y`` is the second
set of measurements), or the differences between two sets of
measurements (in which case ``y`` is not to be specified.) Must be
one-dimensional.
y : array_like, optional
Either the second set of measurements (if ``x`` is the first set of
measurements), or not specified (if ``x`` is the differences between
two sets of measurements.) Must be one-dimensional.
zero_method : {"wilcox", "pratt", "zsplit"}, optional
There are different conventions for handling pairs of observations
with equal values ("zero-differences", or "zeros").
* "wilcox": Discards all zero-differences (default); see [4]_.
* "pratt": Includes zero-differences in the ranking process,
but drops the ranks of the zeros (more conservative); see [3]_.
In this case, the normal approximation is adjusted as in [5]_.
* "zsplit": Includes zero-differences in the ranking process and
splits the zero rank between positive and negative ones.
correction : bool, optional
If True, apply continuity correction by adjusting the Wilcoxon rank
statistic by 0.5 towards the mean value when computing the
z-statistic if a normal approximation is used. Default is False.
alternative : {"two-sided", "greater", "less"}, optional
Defines the alternative hypothesis. Default is 'two-sided'.
In the following, let ``d`` represent the difference between the paired
samples: ``d = x - y`` if both ``x`` and ``y`` are provided, or
``d = x`` otherwise.
* 'two-sided': the distribution underlying ``d`` is not symmetric
about zero.
* 'less': the distribution underlying ``d`` is stochastically less
than a distribution symmetric about zero.
* 'greater': the distribution underlying ``d`` is stochastically
greater than a distribution symmetric about zero.
method : {"auto", "exact", "approx"}, optional
Method to calculate the p-value, see Notes. Default is "auto".
Returns
-------
An object with the following attributes.
statistic : array_like
If `alternative` is "two-sided", the sum of the ranks of the
differences above or below zero, whichever is smaller.
Otherwise the sum of the ranks of the differences above zero.
pvalue : array_like
The p-value for the test depending on `alternative` and `method`.
zstatistic : array_like
When ``method = 'approx'``, this is the normalized z-statistic::
z = (T - mn - d) / se
where ``T`` is `statistic` as defined above, ``mn`` is the mean of the
distribution under the null hypothesis, ``d`` is a continuity
correction, and ``se`` is the standard error.
When ``method != 'approx'``, this attribute is not available.
See Also
--------
kruskal, mannwhitneyu
Notes
-----
In the following, let ``d`` represent the difference between the paired
samples: ``d = x - y`` if both ``x`` and ``y`` are provided, or ``d = x``
otherwise. Assume that all elements of ``d`` are independent and
identically distributed observations, and all are distinct and nonzero.
- When ``len(d)`` is sufficiently large, the null distribution of the
normalized test statistic (`zstatistic` above) is approximately normal,
and ``method = 'approx'`` can be used to compute the p-value.
- When ``len(d)`` is small, the normal approximation may not be accurate,
and ``method='exact'`` is preferred (at the cost of additional
execution time).
- The default, ``method='auto'``, selects between the two: when
``len(d) <= 50``, the exact method is used; otherwise, the approximate
method is used.
The presence of "ties" (i.e. not all elements of ``d`` are unique) and
"zeros" (i.e. elements of ``d`` are zero) changes the null distribution
of the test statistic, and ``method='exact'`` no longer calculates
the exact p-value. If ``method='approx'``, the z-statistic is adjusted
for more accurate comparison against the standard normal, but still,
for finite sample sizes, the standard normal is only an approximation of
the true null distribution of the z-statistic. There is no clear
consensus among references on which method most accurately approximates
the p-value for small samples in the presence of zeros and/or ties. In any
case, this is the behavior of `wilcoxon` when ``method='auto':
``method='exact'`` is used when ``len(d) <= 50`` *and there are no zeros*;
otherwise, ``method='approx'`` is used.
References
----------
.. [1] https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
.. [2] Conover, W.J., Practical Nonparametric Statistics, 1971.
.. [3] Pratt, J.W., Remarks on Zeros and Ties in the Wilcoxon Signed
Rank Procedures, Journal of the American Statistical Association,
Vol. 54, 1959, pp. 655-667. :doi:`10.1080/01621459.1959.10501526`
.. [4] Wilcoxon, F., Individual Comparisons by Ranking Methods,
Biometrics Bulletin, Vol. 1, 1945, pp. 80-83. :doi:`10.2307/3001968`
.. [5] Cureton, E.E., The Normal Approximation to the Signed-Rank
Sampling Distribution When Zero Differences are Present,
Journal of the American Statistical Association, Vol. 62, 1967,
pp. 1068-1069. :doi:`10.1080/01621459.1967.10500917`
Examples
--------
In [4]_, the differences in height between cross- and self-fertilized
corn plants is given as follows:
>>> d = [6, 8, 14, 16, 23, 24, 28, 29, 41, -48, 49, 56, 60, -67, 75]
Cross-fertilized plants appear to be higher. To test the null
hypothesis that there is no height difference, we can apply the
two-sided test:
>>> from scipy.stats import wilcoxon
>>> res = wilcoxon(d)
>>> res.statistic, res.pvalue
(24.0, 0.041259765625)
Hence, we would reject the null hypothesis at a confidence level of 5%,
concluding that there is a difference in height between the groups.
To confirm that the median of the differences can be assumed to be
positive, we use:
>>> res = wilcoxon(d, alternative='greater')
>>> res.statistic, res.pvalue
(96.0, 0.0206298828125)
This shows that the null hypothesis that the median is negative can be
rejected at a confidence level of 5% in favor of the alternative that
the median is greater than zero. The p-values above are exact. Using the
normal approximation gives very similar values:
>>> res = wilcoxon(d, method='approx')
>>> res.statistic, res.pvalue
(24.0, 0.04088813291185591)
Note that the statistic changed to 96 in the one-sided case (the sum
of ranks of positive differences) whereas it is 24 in the two-sided
case (the minimum of sum of ranks above and below zero).
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Perform a Mood's median test.
Test that two or more samples come from populations with the same median.
Let ``n = len(samples)`` be the number of samples. The "grand median" of
all the data is computed, and a contingency table is formed by
classifying the values in each sample as being above or below the grand
median. The contingency table, along with `correction` and `lambda_`,
are passed to `scipy.stats.chi2_contingency` to compute the test statistic
and p-value.
Parameters
----------
sample1, sample2, ... : array_like
The set of samples. There must be at least two samples.
Each sample must be a one-dimensional sequence containing at least
one value. The samples are not required to have the same length.
ties : str, optional
Determines how values equal to the grand median are classified in
the contingency table. The string must be one of::
"below":
Values equal to the grand median are counted as "below".
"above":
Values equal to the grand median are counted as "above".
"ignore":
Values equal to the grand median are not counted.
The default is "below".
correction : bool, optional
If True, *and* there are just two samples, apply Yates' correction
for continuity when computing the test statistic associated with
the contingency table. Default is True.
lambda_ : float or str, optional
By default, the statistic computed in this test is Pearson's
chi-squared statistic. `lambda_` allows a statistic from the
Cressie-Read power divergence family to be used instead. See
`power_divergence` for details.
Default is 1 (Pearson's chi-squared statistic).
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
res : MedianTestResult
An object containing attributes:
statistic : float
The test statistic. The statistic that is returned is determined
by `lambda_`. The default is Pearson's chi-squared statistic.
pvalue : float
The p-value of the test.
median : float
The grand median.
table : ndarray
The contingency table. The shape of the table is (2, n), where
n is the number of samples. The first row holds the counts of the
values above the grand median, and the second row holds the counts
of the values below the grand median. The table allows further
analysis with, for example, `scipy.stats.chi2_contingency`, or with
`scipy.stats.fisher_exact` if there are two samples, without having
to recompute the table. If ``nan_policy`` is "propagate" and there
are nans in the input, the return value for ``table`` is ``None``.
See Also
--------
kruskal : Compute the Kruskal-Wallis H-test for independent samples.
mannwhitneyu : Computes the Mann-Whitney rank test on samples x and y.
Notes
-----
.. versionadded:: 0.15.0
References
----------
.. [1] Mood, A. M., Introduction to the Theory of Statistics. McGraw-Hill
(1950), pp. 394-399.
.. [2] Zar, J. H., Biostatistical Analysis, 5th ed. Prentice Hall (2010).
See Sections 8.12 and 10.15.
Examples
--------
A biologist runs an experiment in which there are three groups of plants.
Group 1 has 16 plants, group 2 has 15 plants, and group 3 has 17 plants.
Each plant produces a number of seeds. The seed counts for each group
are::
Group 1: 10 14 14 18 20 22 24 25 31 31 32 39 43 43 48 49
Group 2: 28 30 31 33 34 35 36 40 44 55 57 61 91 92 99
Group 3: 0 3 9 22 23 25 25 33 34 34 40 45 46 48 62 67 84
The following code applies Mood's median test to these samples.
>>> g1 = [10, 14, 14, 18, 20, 22, 24, 25, 31, 31, 32, 39, 43, 43, 48, 49]
>>> g2 = [28, 30, 31, 33, 34, 35, 36, 40, 44, 55, 57, 61, 91, 92, 99]
>>> g3 = [0, 3, 9, 22, 23, 25, 25, 33, 34, 34, 40, 45, 46, 48, 62, 67, 84]
>>> from scipy.stats import median_test
>>> res = median_test(g1, g2, g3)
The median is
>>> res.median
34.0
and the contingency table is
>>> res.table
array([[ 5, 10, 7],
[11, 5, 10]])
`p` is too large to conclude that the medians are not the same:
>>> res.pvalue
0.12609082774093244
The "G-test" can be performed by passing ``lambda_="log-likelihood"`` to
`median_test`.
>>> res = median_test(g1, g2, g3, lambda_="log-likelihood")
>>> res.pvalue
0.12224779737117837
The median occurs several times in the data, so we'll get a different
result if, for example, ``ties="above"`` is used:
>>> res = median_test(g1, g2, g3, ties="above")
>>> res.pvalue
0.063873276069553273
>>> res.table
array([[ 5, 11, 9],
[11, 4, 8]])
This example demonstrates that if the data set is not large and there
are values equal to the median, the p-value can be sensitive to the
choice of `ties`.
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z dz t z |z S )a Compute the circular mean for samples in a range.
Parameters
----------
samples : array_like
Input array.
high : float or int, optional
High boundary for the sample range. Default is ``2*pi``.
low : float or int, optional
Low boundary for the sample range. Default is 0.
axis : int, optional
Axis along which means are computed. The default is to compute
the mean of the flattened array.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
circmean : float
Circular mean.
See Also
--------
circstd : Circular standard deviation.
circvar : Circular variance.
Examples
--------
For simplicity, all angles are printed out in degrees.
>>> import numpy as np
>>> from scipy.stats import circmean
>>> import matplotlib.pyplot as plt
>>> angles = np.deg2rad(np.array([20, 30, 330]))
>>> circmean = circmean(angles)
>>> np.rad2deg(circmean)
7.294976657784009
>>> mean = angles.mean()
>>> np.rad2deg(mean)
126.66666666666666
Plot and compare the circular mean against the arithmetic mean.
>>> plt.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
... np.sin(np.linspace(0, 2*np.pi, 500)),
... c='k')
>>> plt.scatter(np.cos(angles), np.sin(angles), c='k')
>>> plt.scatter(np.cos(circmean), np.sin(circmean), c='b',
... label='circmean')
>>> plt.scatter(np.cos(mean), np.sin(mean), c='r', label='mean')
>>> plt.legend()
>>> plt.axis('equal')
>>> plt.show()
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Parameters
----------
samples : array_like
Input array.
high : float or int, optional
High boundary for the sample range. Default is ``2*pi``.
low : float or int, optional
Low boundary for the sample range. Default is 0.
axis : int, optional
Axis along which variances are computed. The default is to compute
the variance of the flattened array.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
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values. Default is 'propagate'.
Returns
-------
circvar : float
Circular variance.
See Also
--------
circmean : Circular mean.
circstd : Circular standard deviation.
Notes
-----
This uses the following definition of circular variance: ``1-R``, where
``R`` is the mean resultant vector. The
returned value is in the range [0, 1], 0 standing for no variance, and 1
for a large variance. In the limit of small angles, this value is similar
to half the 'linear' variance.
References
----------
.. [1] Fisher, N.I. *Statistical analysis of circular data*. Cambridge
University Press, 1993.
Examples
--------
>>> import numpy as np
>>> from scipy.stats import circvar
>>> import matplotlib.pyplot as plt
>>> samples_1 = np.array([0.072, -0.158, 0.077, 0.108, 0.286,
... 0.133, -0.473, -0.001, -0.348, 0.131])
>>> samples_2 = np.array([0.111, -0.879, 0.078, 0.733, 0.421,
... 0.104, -0.136, -0.867, 0.012, 0.105])
>>> circvar_1 = circvar(samples_1)
>>> circvar_2 = circvar(samples_2)
Plot the samples.
>>> fig, (left, right) = plt.subplots(ncols=2)
>>> for image in (left, right):
... image.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
... np.sin(np.linspace(0, 2*np.pi, 500)),
... c='k')
... image.axis('equal')
... image.axis('off')
>>> left.scatter(np.cos(samples_1), np.sin(samples_1), c='k', s=15)
>>> left.set_title(f"circular variance: {np.round(circvar_1, 2)!r}")
>>> right.scatter(np.cos(samples_2), np.sin(samples_2), c='k', s=15)
>>> right.set_title(f"circular variance: {np.round(circvar_2, 2)!r}")
>>> plt.show()
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Compute the circular standard deviation for samples assumed to be in the
range [low to high].
Parameters
----------
samples : array_like
Input array.
high : float or int, optional
High boundary for the sample range. Default is ``2*pi``.
low : float or int, optional
Low boundary for the sample range. Default is 0.
axis : int, optional
Axis along which standard deviations are computed. The default is
to compute the standard deviation of the flattened array.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
normalize : boolean, optional
If True, the returned value is equal to ``sqrt(-2*log(R))`` and does
not depend on the variable units. If False (default), the returned
value is scaled by ``((high-low)/(2*pi))``.
Returns
-------
circstd : float
Circular standard deviation.
See Also
--------
circmean : Circular mean.
circvar : Circular variance.
Notes
-----
This uses a definition of circular standard deviation from [1]_.
Essentially, the calculation is as follows.
.. code-block:: python
import numpy as np
C = np.cos(samples).mean()
S = np.sin(samples).mean()
R = np.sqrt(C**2 + S**2)
l = 2*np.pi / (high-low)
circstd = np.sqrt(-2*np.log(R)) / l
In the limit of small angles, it returns a number close to the 'linear'
standard deviation.
References
----------
.. [1] Mardia, K. V. (1972). 2. In *Statistics of Directional Data*
(pp. 18-24). Academic Press. :doi:`10.1016/C2013-0-07425-7`.
Examples
--------
>>> import numpy as np
>>> from scipy.stats import circstd
>>> import matplotlib.pyplot as plt
>>> samples_1 = np.array([0.072, -0.158, 0.077, 0.108, 0.286,
... 0.133, -0.473, -0.001, -0.348, 0.131])
>>> samples_2 = np.array([0.111, -0.879, 0.078, 0.733, 0.421,
... 0.104, -0.136, -0.867, 0.012, 0.105])
>>> circstd_1 = circstd(samples_1)
>>> circstd_2 = circstd(samples_2)
Plot the samples.
>>> fig, (left, right) = plt.subplots(ncols=2)
>>> for image in (left, right):
... image.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
... np.sin(np.linspace(0, 2*np.pi, 500)),
... c='k')
... image.axis('equal')
... image.axis('off')
>>> left.scatter(np.cos(samples_1), np.sin(samples_1), c='k', s=15)
>>> left.set_title(f"circular std: {np.round(circstd_1, 2)!r}")
>>> right.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
... np.sin(np.linspace(0, 2*np.pi, 500)),
... c='k')
>>> right.scatter(np.cos(samples_2), np.sin(samples_2), c='k', s=15)
>>> right.set_title(f"circular std: {np.round(circstd_2, 2)!r}")
>>> plt.show()
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d� � d � � S ) a
Computes sample statistics for directional data.
Computes the directional mean (also called the mean direction vector) and
mean resultant length of a sample of vectors.
The directional mean is a measure of "preferred direction" of vector data.
It is analogous to the sample mean, but it is for use when the length of
the data is irrelevant (e.g. unit vectors).
The mean resultant length is a value between 0 and 1 used to quantify the
dispersion of directional data: the smaller the mean resultant length, the
greater the dispersion. Several definitions of directional variance
involving the mean resultant length are given in [1]_ and [2]_.
Parameters
----------
samples : array_like
Input array. Must be at least two-dimensional, and the last axis of the
input must correspond with the dimensionality of the vector space.
When the input is exactly two dimensional, this means that each row
of the data is a vector observation.
axis : int, default: 0
Axis along which the directional mean is computed.
normalize: boolean, default: True
If True, normalize the input to ensure that each observation is a
unit vector. It the observations are already unit vectors, consider
setting this to False to avoid unnecessary computation.
Returns
-------
res : DirectionalStats
An object containing attributes:
mean_direction : ndarray
Directional mean.
mean_resultant_length : ndarray
The mean resultant length [1]_.
See also
--------
circmean: circular mean; i.e. directional mean for 2D *angles*
circvar: circular variance; i.e. directional variance for 2D *angles*
Notes
-----
This uses a definition of directional mean from [1]_.
Assuming the observations are unit vectors, the calculation is as follows.
.. code-block:: python
mean = samples.mean(axis=0)
mean_resultant_length = np.linalg.norm(mean)
mean_direction = mean / mean_resultant_length
This definition is appropriate for *directional* data (i.e. vector data
for which the magnitude of each observation is irrelevant) but not
for *axial* data (i.e. vector data for which the magnitude and *sign* of
each observation is irrelevant).
Several definitions of directional variance involving the mean resultant
length ``R`` have been proposed, including ``1 - R`` [1]_, ``1 - R**2``
[2]_, and ``2 * (1 - R)`` [2]_. Rather than choosing one, this function
returns ``R`` as attribute `mean_resultant_length` so the user can compute
their preferred measure of dispersion.
References
----------
.. [1] Mardia, Jupp. (2000). *Directional Statistics*
(p. 163). Wiley.
.. [2] https://en.wikipedia.org/wiki/Directional_statistics
Examples
--------
>>> import numpy as np
>>> from scipy.stats import directional_stats
>>> data = np.array([[3, 4], # first observation, 2D vector space
... [6, -8]]) # second observation
>>> dirstats = directional_stats(data)
>>> dirstats.mean_direction
array([1., 0.])
In contrast, the regular sample mean of the vectors would be influenced
by the magnitude of each observation. Furthermore, the result would not be
a unit vector.
>>> data.mean(axis=0)
array([4.5, -2.])
An exemplary use case for `directional_stats` is to find a *meaningful*
center for a set of observations on a sphere, e.g. geographical locations.
>>> data = np.array([[0.8660254, 0.5, 0.],
... [0.8660254, -0.5, 0.]])
>>> dirstats = directional_stats(data)
>>> dirstats.mean_direction
array([1., 0., 0.])
The regular sample mean on the other hand yields a result which does not
lie on the surface of the sphere.
>>> data.mean(axis=0)
array([0.8660254, 0., 0.])
The function also returns the mean resultant length, which
can be used to calculate a directional variance. For example, using the
definition ``Var(z) = 1 - R`` from [2]_ where ``R`` is the
mean resultant length, we can calculate the directional variance of the
vectors in the above example as:
>>> 1 - dirstats.mean_resultant_length
0.13397459716167093
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