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2378 lines
76 KiB
Python
2378 lines
76 KiB
Python
# Author: Travis Oliphant, 2002
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#
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# Further updates and enhancements by many SciPy developers.
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#
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from __future__ import division, print_function, absolute_import
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import math
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import warnings
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import numpy as np
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from numpy import (isscalar, r_, log, sum, around, unique, asarray,
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zeros, arange, sort, amin, amax, any, atleast_1d, sqrt, ceil,
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floor, array, poly1d, compress, not_equal, pi, exp, ravel, angle)
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from numpy.testing.decorators import setastest
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from scipy._lib.six import string_types
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from ._numpy_compat import count_nonzero
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from scipy import optimize
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from scipy import special
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from . import statlib
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from . import stats
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from .stats import find_repeats
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from .contingency import chi2_contingency
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from . import distributions
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from ._distn_infrastructure import rv_generic
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__all__ = ['mvsdist',
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'bayes_mvs', 'kstat', 'kstatvar', 'probplot', 'ppcc_max', 'ppcc_plot',
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'boxcox_llf', 'boxcox', 'boxcox_normmax', 'boxcox_normplot',
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'shapiro', 'anderson', 'ansari', 'bartlett', 'levene', 'binom_test',
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'fligner', 'mood', 'wilcoxon', 'median_test',
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'pdf_fromgamma', 'circmean', 'circvar', 'circstd', 'anderson_ksamp'
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]
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def bayes_mvs(data, alpha=0.90):
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"""
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Bayesian confidence intervals for the mean, var, and std.
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Parameters
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----------
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data : array_like
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Input data, if multi-dimensional it is flattened to 1-D by `bayes_mvs`.
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Requires 2 or more data points.
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alpha : float, optional
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Probability that the returned confidence interval contains
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the true parameter.
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Returns
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-------
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mean_cntr, var_cntr, std_cntr : tuple
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The three results are for the mean, variance and standard deviation,
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respectively. Each result is a tuple of the form::
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(center, (lower, upper))
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with `center` the mean of the conditional pdf of the value given the
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data, and `(lower, upper)` a confidence interval, centered on the
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median, containing the estimate to a probability `alpha`.
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Notes
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-----
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Each tuple of mean, variance, and standard deviation estimates represent
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the (center, (lower, upper)) with center the mean of the conditional pdf
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of the value given the data and (lower, upper) is a confidence interval
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centered on the median, containing the estimate to a probability
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`alpha`.
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Converts data to 1-D and assumes all data has the same mean and variance.
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Uses Jeffrey's prior for variance and std.
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Equivalent to tuple((x.mean(), x.interval(alpha)) for x in mvsdist(dat))
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References
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----------
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T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and
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standard-deviation from data", http://hdl.handle.net/1877/438, 2006.
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"""
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res = mvsdist(data)
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if alpha >= 1 or alpha <= 0:
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raise ValueError("0 < alpha < 1 is required, but alpha=%s was given." % alpha)
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return tuple((x.mean(), x.interval(alpha)) for x in res)
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def mvsdist(data):
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"""
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'Frozen' distributions for mean, variance, and standard deviation of data.
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Parameters
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----------
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data : array_like
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Input array. Converted to 1-D using ravel.
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Requires 2 or more data-points.
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Returns
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-------
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mdist : "frozen" distribution object
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Distribution object representing the mean of the data
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vdist : "frozen" distribution object
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Distribution object representing the variance of the data
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sdist : "frozen" distribution object
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Distribution object representing the standard deviation of the data
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Notes
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-----
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The return values from bayes_mvs(data) is equivalent to
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``tuple((x.mean(), x.interval(0.90)) for x in mvsdist(data))``.
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In other words, calling ``<dist>.mean()`` and ``<dist>.interval(0.90)``
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on the three distribution objects returned from this function will give
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the same results that are returned from `bayes_mvs`.
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Examples
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--------
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>>> from scipy.stats import mvsdist
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>>> data = [6, 9, 12, 7, 8, 8, 13]
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>>> mean, var, std = mvsdist(data)
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We now have frozen distribution objects "mean", "var" and "std" that we can
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examine:
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>>> mean.mean()
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9.0
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>>> mean.interval(0.95)
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(6.6120585482655692, 11.387941451734431)
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>>> mean.std()
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1.1952286093343936
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"""
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x = ravel(data)
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n = len(x)
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if (n < 2):
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raise ValueError("Need at least 2 data-points.")
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xbar = x.mean()
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C = x.var()
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if (n > 1000): # gaussian approximations for large n
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mdist = distributions.norm(loc=xbar, scale=math.sqrt(C/n))
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sdist = distributions.norm(loc=math.sqrt(C), scale=math.sqrt(C/(2.*n)))
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vdist = distributions.norm(loc=C, scale=math.sqrt(2.0/n)*C)
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else:
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nm1 = n-1
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fac = n*C/2.
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val = nm1/2.
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mdist = distributions.t(nm1,loc=xbar,scale=math.sqrt(C/nm1))
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sdist = distributions.gengamma(val,-2,scale=math.sqrt(fac))
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vdist = distributions.invgamma(val,scale=fac)
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return mdist, vdist, sdist
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def kstat(data,n=2):
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"""
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Return the nth k-statistic (1<=n<=4 so far).
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The nth k-statistic is the unique symmetric unbiased estimator of the nth
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cumulant kappa_n.
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Parameters
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----------
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data : array_like
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Input array.
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n : int, {1, 2, 3, 4}, optional
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Default is equal to 2.
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Returns
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-------
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kstat : float
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The nth k-statistic.
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See Also
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--------
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kstatvar: Returns an unbiased estimator of the variance of the k-statistic.
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Notes
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-----
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The cumulants are related to central moments but are specifically defined
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using a power series expansion of the logarithm of the characteristic
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function (which is the Fourier transform of the PDF).
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In particular let phi(t) be the characteristic function, then::
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ln phi(t) = > kappa_n (it)^n / n! (sum from n=0 to inf)
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The first few cumulants (kappa_n) in terms of central moments (mu_n) are::
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kappa_1 = mu_1
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kappa_2 = mu_2
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kappa_3 = mu_3
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kappa_4 = mu_4 - 3*mu_2**2
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kappa_5 = mu_5 - 10*mu_2 * mu_3
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References
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----------
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http://mathworld.wolfram.com/k-Statistic.html
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http://mathworld.wolfram.com/Cumulant.html
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"""
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if n > 4 or n < 1:
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raise ValueError("k-statistics only supported for 1<=n<=4")
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n = int(n)
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S = zeros(n+1,'d')
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data = ravel(data)
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N = len(data)
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for k in range(1,n+1):
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S[k] = sum(data**k,axis=0)
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if n == 1:
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return S[1]*1.0/N
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elif n == 2:
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return (N*S[2]-S[1]**2.0)/(N*(N-1.0))
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elif n == 3:
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return (2*S[1]**3 - 3*N*S[1]*S[2]+N*N*S[3]) / (N*(N-1.0)*(N-2.0))
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elif n == 4:
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return (-6*S[1]**4 + 12*N*S[1]**2 * S[2] - 3*N*(N-1.0)*S[2]**2 -
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4*N*(N+1)*S[1]*S[3] + N*N*(N+1)*S[4]) / \
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(N*(N-1.0)*(N-2.0)*(N-3.0))
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else:
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raise ValueError("Should not be here.")
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def kstatvar(data,n=2):
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"""
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Returns an unbiased estimator of the variance of the k-statistic.
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See `kstat` for more details of the k-statistic.
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Parameters
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----------
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data : array_like
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Input array.
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n : int, {1, 2}, optional
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Default is equal to 2.
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Returns
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-------
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kstatvar : float
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The nth k-statistic variance.
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See Also
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--------
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kstat
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"""
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data = ravel(data)
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N = len(data)
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if n == 1:
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return kstat(data,n=2)*1.0/N
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elif n == 2:
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k2 = kstat(data,n=2)
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k4 = kstat(data,n=4)
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return (2*k2*k2*N + (N-1)*k4)/(N*(N+1))
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else:
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raise ValueError("Only n=1 or n=2 supported.")
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def _calc_uniform_order_statistic_medians(x):
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"""See Notes section of `probplot` for details."""
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N = len(x)
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osm_uniform = np.zeros(N, dtype=np.float64)
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osm_uniform[-1] = 0.5**(1.0 / N)
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osm_uniform[0] = 1 - osm_uniform[-1]
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i = np.arange(2, N)
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osm_uniform[1:-1] = (i - 0.3175) / (N + 0.365)
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return osm_uniform
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def _parse_dist_kw(dist, enforce_subclass=True):
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"""Parse `dist` keyword.
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Parameters
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----------
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dist : str or stats.distributions instance.
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Several functions take `dist` as a keyword, hence this utility
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function.
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enforce_subclass : bool, optional
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If True (default), `dist` needs to be a
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`_distn_infrastructure.rv_generic` instance.
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It can sometimes be useful to set this keyword to False, if a function
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wants to accept objects that just look somewhat like such an instance
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(for example, they have a ``ppf`` method).
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"""
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if isinstance(dist, rv_generic):
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pass
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elif isinstance(dist, string_types):
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try:
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dist = getattr(distributions, dist)
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except AttributeError:
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raise ValueError("%s is not a valid distribution name" % dist)
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elif enforce_subclass:
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msg = ("`dist` should be a stats.distributions instance or a string "
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"with the name of such a distribution.")
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raise ValueError(msg)
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return dist
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def probplot(x, sparams=(), dist='norm', fit=True, plot=None):
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"""
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Calculate quantiles for a probability plot, and optionally show the plot.
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Generates a probability plot of sample data against the quantiles of a
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specified theoretical distribution (the normal distribution by default).
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`probplot` optionally calculates a best-fit line for the data and plots the
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results using Matplotlib or a given plot function.
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Parameters
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----------
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x : array_like
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Sample/response data from which `probplot` creates the plot.
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sparams : tuple, optional
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Distribution-specific shape parameters (shape parameters plus location
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and scale).
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dist : str or stats.distributions instance, optional
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Distribution or distribution function name. The default is 'norm' for a
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normal probability plot. Objects that look enough like a
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stats.distributions instance (i.e. they have a ``ppf`` method) are also
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accepted.
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fit : bool, optional
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Fit a least-squares regression (best-fit) line to the sample data if
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True (default).
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plot : object, optional
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If given, plots the quantiles and least squares fit.
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`plot` is an object that has to have methods "plot" and "text".
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The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
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or a custom object with the same methods.
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Default is None, which means that no plot is created.
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Returns
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-------
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(osm, osr) : tuple of ndarrays
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Tuple of theoretical quantiles (osm, or order statistic medians) and
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ordered responses (osr). `osr` is simply sorted input `x`.
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For details on how `osm` is calculated see the Notes section.
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(slope, intercept, r) : tuple of floats, optional
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Tuple containing the result of the least-squares fit, if that is
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performed by `probplot`. `r` is the square root of the coefficient of
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determination. If ``fit=False`` and ``plot=None``, this tuple is not
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returned.
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Notes
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-----
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Even if `plot` is given, the figure is not shown or saved by `probplot`;
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``plt.show()`` or ``plt.savefig('figname.png')`` should be used after
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calling `probplot`.
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`probplot` generates a probability plot, which should not be confused with
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a Q-Q or a P-P plot. Statsmodels has more extensive functionality of this
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type, see ``statsmodels.api.ProbPlot``.
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The formula used for the theoretical quantiles (horizontal axis of the
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probability plot) is Filliben's estimate::
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quantiles = dist.ppf(val), for
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0.5**(1/n), for i = n
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val = (i - 0.3175) / (n + 0.365), for i = 2, ..., n-1
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1 - 0.5**(1/n), for i = 1
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where ``i`` indicates the i-th ordered value and ``n`` is the total number
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of values.
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Examples
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--------
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>>> from scipy import stats
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>>> import matplotlib.pyplot as plt
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>>> nsample = 100
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>>> np.random.seed(7654321)
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A t distribution with small degrees of freedom:
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>>> ax1 = plt.subplot(221)
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>>> x = stats.t.rvs(3, size=nsample)
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>>> res = stats.probplot(x, plot=plt)
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A t distribution with larger degrees of freedom:
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>>> ax2 = plt.subplot(222)
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>>> x = stats.t.rvs(25, size=nsample)
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>>> res = stats.probplot(x, plot=plt)
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A mixture of two normal distributions with broadcasting:
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>>> ax3 = plt.subplot(223)
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>>> x = stats.norm.rvs(loc=[0,5], scale=[1,1.5],
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... size=(nsample/2.,2)).ravel()
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>>> res = stats.probplot(x, plot=plt)
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A standard normal distribution:
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>>> ax4 = plt.subplot(224)
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>>> x = stats.norm.rvs(loc=0, scale=1, size=nsample)
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>>> res = stats.probplot(x, plot=plt)
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Produce a new figure with a loggamma distribution, using the ``dist`` and
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``sparams`` keywords:
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>>> fig = plt.figure()
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>>> ax = fig.add_subplot(111)
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>>> x = stats.loggamma.rvs(c=2.5, size=500)
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>>> stats.probplot(x, dist=stats.loggamma, sparams=(2.5,), plot=ax)
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>>> ax.set_title("Probplot for loggamma dist with shape parameter 2.5")
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Show the results with Matplotlib:
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>>> plt.show()
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"""
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x = np.asarray(x)
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osm_uniform = _calc_uniform_order_statistic_medians(x)
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dist = _parse_dist_kw(dist, enforce_subclass=False)
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if sparams is None:
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sparams = ()
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if isscalar(sparams):
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sparams = (sparams,)
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if not isinstance(sparams, tuple):
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sparams = tuple(sparams)
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osm = dist.ppf(osm_uniform, *sparams)
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osr = sort(x)
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if fit or (plot is not None):
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# perform a linear fit.
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slope, intercept, r, prob, sterrest = stats.linregress(osm, osr)
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if plot is not None:
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plot.plot(osm, osr, 'bo', osm, slope*osm + intercept, 'r-')
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try:
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if hasattr(plot, 'set_title'):
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# Matplotlib Axes instance or something that looks like it
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plot.set_title('Probability Plot')
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plot.set_xlabel('Quantiles')
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plot.set_ylabel('Ordered Values')
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else:
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# matplotlib.pyplot module
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plot.title('Probability Plot')
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plot.xlabel('Quantiles')
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plot.ylabel('Ordered Values')
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except:
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# Not an MPL object or something that looks (enough) like it.
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# Don't crash on adding labels or title
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pass
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# Add R^2 value to the plot as text
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xmin = amin(osm)
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xmax = amax(osm)
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ymin = amin(x)
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ymax = amax(x)
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posx = xmin + 0.70 * (xmax - xmin)
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posy = ymin + 0.01 * (ymax - ymin)
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plot.text(posx, posy, "$R^2=%1.4f$" % r)
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if fit:
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return (osm, osr), (slope, intercept, r)
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else:
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return osm, osr
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def ppcc_max(x, brack=(0.0,1.0), dist='tukeylambda'):
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"""Returns the shape parameter that maximizes the probability plot
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correlation coefficient for the given data to a one-parameter
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family of distributions.
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See also ppcc_plot
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"""
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dist = _parse_dist_kw(dist)
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osm_uniform = _calc_uniform_order_statistic_medians(x)
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osr = sort(x)
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# this function computes the x-axis values of the probability plot
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# and computes a linear regression (including the correlation)
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# and returns 1-r so that a minimization function maximizes the
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# correlation
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def tempfunc(shape, mi, yvals, func):
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xvals = func(mi, shape)
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r, prob = stats.pearsonr(xvals, yvals)
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return 1-r
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return optimize.brent(tempfunc, brack=brack, args=(osm_uniform, osr, dist.ppf))
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def ppcc_plot(x,a,b,dist='tukeylambda', plot=None, N=80):
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"""Returns (shape, ppcc), and optionally plots shape vs. ppcc
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(probability plot correlation coefficient) as a function of shape
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parameter for a one-parameter family of distributions from shape
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value a to b.
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See also ppcc_max
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"""
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svals = r_[a:b:complex(N)]
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ppcc = svals*0.0
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k = 0
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for sval in svals:
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r1,r2 = probplot(x,sval,dist=dist,fit=1)
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ppcc[k] = r2[-1]
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k += 1
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if plot is not None:
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plot.plot(svals, ppcc, 'x')
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plot.title('(%s) PPCC Plot' % dist)
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plot.xlabel('Prob Plot Corr. Coef.')
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plot.ylabel('Shape Values')
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return svals, ppcc
|
|
|
|
|
|
def boxcox_llf(lmb, data):
|
|
r"""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
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> from mpl_toolkits.axes_grid1.inset_locator import inset_axes
|
|
>>> np.random.seed(1245)
|
|
|
|
Generate some random variates and calculate Box-Cox 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=np.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('$\lambda=%1.2f$' % lmbda)
|
|
|
|
>>> plt.show()
|
|
|
|
"""
|
|
data = np.asarray(data)
|
|
N = data.shape[0]
|
|
if N == 0:
|
|
return np.nan
|
|
|
|
y = boxcox(data, lmb)
|
|
y_mean = np.mean(y, axis=0)
|
|
llf = (lmb - 1) * np.sum(np.log(data), axis=0)
|
|
llf -= N / 2.0 * np.log(np.sum((y - y_mean)**2. / N, axis=0))
|
|
return llf
|
|
|
|
|
|
def _boxcox_conf_interval(x, lmax, alpha):
|
|
# Need to find the lambda for which
|
|
# f(x,lmbda) >= f(x,lmax) - 0.5*chi^2_alpha;1
|
|
fac = 0.5 * distributions.chi2.ppf(1 - alpha, 1)
|
|
target = boxcox_llf(lmax, x) - fac
|
|
|
|
def rootfunc(lmbda, data, target):
|
|
return boxcox_llf(lmbda, data) - target
|
|
|
|
# Find positive endpoint of interval in which answer is to be found
|
|
newlm = lmax + 0.5
|
|
N = 0
|
|
while (rootfunc(newlm, x, target) > 0.0) and (N < 500):
|
|
newlm += 0.1
|
|
N += 1
|
|
|
|
if N == 500:
|
|
raise RuntimeError("Could not find endpoint.")
|
|
|
|
lmplus = optimize.brentq(rootfunc, lmax, newlm, args=(x, target))
|
|
|
|
# Now find negative interval in the same way
|
|
newlm = lmax - 0.5
|
|
N = 0
|
|
while (rootfunc(newlm, x, target) > 0.0) and (N < 500):
|
|
newlm -= 0.1
|
|
N += 1
|
|
|
|
if N == 500:
|
|
raise RuntimeError("Could not find endpoint.")
|
|
|
|
lmminus = optimize.brentq(rootfunc, newlm, lmax, args=(x, target))
|
|
return lmminus, lmplus
|
|
|
|
|
|
def boxcox(x, lmbda=None, alpha=None):
|
|
r"""
|
|
Return a positive dataset transformed by a Box-Cox power transformation.
|
|
|
|
Parameters
|
|
----------
|
|
x : ndarray
|
|
Input array. Should be 1-dimensional.
|
|
lmbda : {None, scalar}, optional
|
|
If `lmbda` is not None, do the transformation for that value.
|
|
|
|
If `lmbda` is None, find the lambda that maximizes the log-likelihood
|
|
function and return it as the second output argument.
|
|
alpha : {None, float}, optional
|
|
If `alpha` is not None, return the ``100 * (1-alpha)%`` confidence
|
|
interval for `lmbda` as the third output argument.
|
|
Must be between 0.0 and 1.0.
|
|
|
|
Returns
|
|
-------
|
|
boxcox : ndarray
|
|
Box-Cox 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.
|
|
(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
|
|
>>> 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)
|
|
>>> stats.probplot(xt, dist=stats.norm, plot=ax2)
|
|
>>> ax2.set_title('Probplot after Box-Cox transformation')
|
|
|
|
>>> plt.show()
|
|
|
|
"""
|
|
x = np.asarray(x)
|
|
if x.size == 0:
|
|
return x
|
|
|
|
if any(x <= 0):
|
|
raise ValueError("Data must be positive.")
|
|
|
|
if lmbda is not None: # single transformation
|
|
return special.boxcox(x, lmbda)
|
|
|
|
# If lmbda=None, find the lmbda that maximizes the log-likelihood function.
|
|
lmax = boxcox_normmax(x, method='mle')
|
|
y = boxcox(x, lmax)
|
|
|
|
if alpha is None:
|
|
return y, lmax
|
|
else:
|
|
# Find confidence interval
|
|
interval = _boxcox_conf_interval(x, lmax, alpha)
|
|
return y, lmax, interval
|
|
|
|
|
|
def boxcox_normmax(x, brack=(-2.0, 2.0), method='pearsonr'):
|
|
"""Compute optimal Box-Cox transform parameter for input data.
|
|
|
|
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.
|
|
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.
|
|
|
|
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
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> np.random.seed(1234) # make this example reproducible
|
|
|
|
Generate some data and determine optimal ``lmbda`` in various ways:
|
|
|
|
>>> x = stats.loggamma.rvs(5, size=30) + 5
|
|
>>> y, lmax_mle = stats.boxcox(x)
|
|
>>> lmax_pearsonr = stats.boxcox_normmax(x)
|
|
|
|
>>> lmax_mle
|
|
7.177...
|
|
>>> lmax_pearsonr
|
|
7.916...
|
|
>>> stats.boxcox_normmax(x, method='all')
|
|
array([ 7.91667384, 7.17718692])
|
|
|
|
>>> fig = plt.figure()
|
|
>>> ax = fig.add_subplot(111)
|
|
>>> stats.boxcox_normplot(x, -10, 10, plot=ax)
|
|
>>> ax.axvline(lmax_mle, color='r')
|
|
>>> ax.axvline(lmax_pearsonr, color='g', ls='--')
|
|
|
|
>>> plt.show()
|
|
|
|
"""
|
|
def _pearsonr(x, brack):
|
|
osm_uniform = _calc_uniform_order_statistic_medians(x)
|
|
xvals = distributions.norm.ppf(osm_uniform)
|
|
|
|
def _eval_pearsonr(lmbda, xvals, samps):
|
|
# This function computes the x-axis values of the probability plot
|
|
# and computes a linear regression (including the correlation) and
|
|
# returns ``1 - r`` so that a minimization function maximizes the
|
|
# correlation.
|
|
y = boxcox(samps, lmbda)
|
|
yvals = np.sort(y)
|
|
r, prob = stats.pearsonr(xvals, yvals)
|
|
return 1 - r
|
|
|
|
return optimize.brent(_eval_pearsonr, brack=brack, args=(xvals, x))
|
|
|
|
def _mle(x, brack):
|
|
def _eval_mle(lmb, data):
|
|
# function to minimize
|
|
return -boxcox_llf(lmb, data)
|
|
|
|
return optimize.brent(_eval_mle, brack=brack, args=(x,))
|
|
|
|
def _all(x, brack):
|
|
maxlog = np.zeros(2, dtype=np.float)
|
|
maxlog[0] = _pearsonr(x, brack)
|
|
maxlog[1] = _mle(x, brack)
|
|
return maxlog
|
|
|
|
methods = {'pearsonr': _pearsonr,
|
|
'mle': _mle,
|
|
'all': _all}
|
|
if method not in methods.keys():
|
|
raise ValueError("Method %s not recognized." % method)
|
|
|
|
optimfunc = methods[method]
|
|
return optimfunc(x, brack)
|
|
|
|
|
|
def boxcox_normplot(x, la, lb, plot=None, N=80):
|
|
"""Compute parameters for a Box-Cox normality plot, optionally show it.
|
|
|
|
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)
|
|
>>> 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()
|
|
|
|
"""
|
|
x = np.asarray(x)
|
|
if x.size == 0:
|
|
return x
|
|
|
|
if lb <= la:
|
|
raise ValueError("`lb` has to be larger than `la`.")
|
|
|
|
lmbdas = np.linspace(la, lb, num=N)
|
|
ppcc = lmbdas * 0.0
|
|
for i, val in enumerate(lmbdas):
|
|
# Determine for each lmbda the correlation coefficient of transformed x
|
|
z = boxcox(x, lmbda=val)
|
|
_, r2 = probplot(z, dist='norm', fit=True)
|
|
ppcc[i] = r2[-1]
|
|
|
|
if plot is not None:
|
|
plot.plot(lmbdas, ppcc, 'x')
|
|
try:
|
|
if hasattr(plot, 'set_title'):
|
|
# Matplotlib Axes instance or something that looks like it
|
|
plot.set_title('Box-Cox Normality Plot')
|
|
plot.set_ylabel('Prob Plot Corr. Coef.')
|
|
plot.set_xlabel('$\lambda$')
|
|
else:
|
|
# matplotlib.pyplot module
|
|
plot.title('Box-Cox Normality Plot')
|
|
plot.ylabel('Prob Plot Corr. Coef.')
|
|
plot.xlabel('$\lambda$')
|
|
except Exception:
|
|
# Not an MPL object or something that looks (enough) like it.
|
|
# Don't crash on adding labels or title
|
|
pass
|
|
|
|
return lmbdas, ppcc
|
|
|
|
|
|
def shapiro(x, a=None, reta=False):
|
|
"""
|
|
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.
|
|
a : array_like, optional
|
|
Array of internal parameters used in the calculation. If these
|
|
are not given, they will be computed internally. If x has length
|
|
n, then a must have length n/2.
|
|
reta : bool, optional
|
|
Whether or not to return the internally computed a values. The
|
|
default is False.
|
|
|
|
Returns
|
|
-------
|
|
W : float
|
|
The test statistic.
|
|
p-value : float
|
|
The p-value for the hypothesis test.
|
|
a : array_like, optional
|
|
If `reta` is True, then these are the internally computed "a"
|
|
values that may be passed into this function on future calls.
|
|
|
|
See Also
|
|
--------
|
|
anderson : The Anderson-Darling test for normality
|
|
|
|
References
|
|
----------
|
|
.. [1] http://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
|
|
|
|
"""
|
|
N = len(x)
|
|
if N < 3:
|
|
raise ValueError("Data must be at least length 3.")
|
|
if a is None:
|
|
a = zeros(N,'f')
|
|
init = 0
|
|
else:
|
|
if len(a) != N//2:
|
|
raise ValueError("len(a) must equal len(x)/2")
|
|
init = 1
|
|
y = sort(x)
|
|
a, w, pw, ifault = statlib.swilk(y, a[:N//2], init)
|
|
if ifault not in [0,2]:
|
|
warnings.warn(str(ifault))
|
|
if N > 5000:
|
|
warnings.warn("p-value may not be accurate for N > 5000.")
|
|
if reta:
|
|
return w, pw, a
|
|
else:
|
|
return w, pw
|
|
|
|
# Values from Stephens, M A, "EDF Statistics for Goodness of Fit and
|
|
# Some Comparisons", Journal of he American Statistical
|
|
# Association, Vol. 69, Issue 347, Sept. 1974, pp 730-737
|
|
_Avals_norm = array([0.576, 0.656, 0.787, 0.918, 1.092])
|
|
_Avals_expon = array([0.922, 1.078, 1.341, 1.606, 1.957])
|
|
# From Stephens, M A, "Goodness of Fit for the Extreme Value Distribution",
|
|
# Biometrika, Vol. 64, Issue 3, Dec. 1977, pp 583-588.
|
|
_Avals_gumbel = array([0.474, 0.637, 0.757, 0.877, 1.038])
|
|
# From Stephens, M A, "Tests of Fit for the Logistic Distribution Based
|
|
# on the Empirical Distribution Function.", Biometrika,
|
|
# Vol. 66, Issue 3, Dec. 1979, pp 591-595.
|
|
_Avals_logistic = array([0.426, 0.563, 0.660, 0.769, 0.906, 1.010])
|
|
|
|
|
|
def anderson(x,dist='norm'):
|
|
"""
|
|
Anderson-Darling test for data coming from a particular distribution
|
|
|
|
The Anderson-Darling test is a modification of the Kolmogorov-
|
|
Smirnov test `kstest` for 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','extreme1'}, optional
|
|
the type of distribution to test against. The default is 'norm'
|
|
and 'extreme1' is a synonym for 'gumbel'
|
|
|
|
Returns
|
|
-------
|
|
A2 : float
|
|
The Anderson-Darling test statistic
|
|
critical : list
|
|
The critical values for this distribution
|
|
sig : 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.
|
|
|
|
Notes
|
|
-----
|
|
Critical values provided are for the following significance levels:
|
|
|
|
normal/exponenential
|
|
15%, 10%, 5%, 2.5%, 1%
|
|
logistic
|
|
25%, 10%, 5%, 2.5%, 1%, 0.5%
|
|
Gumbel
|
|
25%, 10%, 5%, 2.5%, 1%
|
|
|
|
If A2 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.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://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.
|
|
|
|
"""
|
|
if dist not in ['norm','expon','gumbel','extreme1','logistic']:
|
|
raise ValueError("Invalid distribution; dist must be 'norm', "
|
|
"'expon', 'gumbel', 'extreme1' or 'logistic'.")
|
|
y = sort(x)
|
|
xbar = np.mean(x, axis=0)
|
|
N = len(y)
|
|
if dist == 'norm':
|
|
s = np.std(x, ddof=1, axis=0)
|
|
w = (y-xbar)/s
|
|
z = distributions.norm.cdf(w)
|
|
sig = array([15,10,5,2.5,1])
|
|
critical = around(_Avals_norm / (1.0 + 4.0/N - 25.0/N/N),3)
|
|
elif dist == 'expon':
|
|
w = y / xbar
|
|
z = distributions.expon.cdf(w)
|
|
sig = array([15,10,5,2.5,1])
|
|
critical = around(_Avals_expon / (1.0 + 0.6/N),3)
|
|
elif dist == 'logistic':
|
|
def rootfunc(ab,xj,N):
|
|
a,b = ab
|
|
tmp = (xj-a)/b
|
|
tmp2 = exp(tmp)
|
|
val = [sum(1.0/(1+tmp2),axis=0)-0.5*N,
|
|
sum(tmp*(1.0-tmp2)/(1+tmp2),axis=0)+N]
|
|
return array(val)
|
|
sol0 = array([xbar,np.std(x, ddof=1, axis=0)])
|
|
sol = optimize.fsolve(rootfunc,sol0,args=(x,N),xtol=1e-5)
|
|
w = (y-sol[0])/sol[1]
|
|
z = distributions.logistic.cdf(w)
|
|
sig = array([25,10,5,2.5,1,0.5])
|
|
critical = around(_Avals_logistic / (1.0+0.25/N),3)
|
|
else: # (dist == 'gumbel') or (dist == 'extreme1'):
|
|
# the following is incorrect, see ticket:1097
|
|
#def fixedsolve(th,xj,N):
|
|
# val = stats.sum(xj)*1.0/N
|
|
# tmp = exp(-xj/th)
|
|
# term = sum(xj*tmp,axis=0)
|
|
# term /= sum(tmp,axis=0)
|
|
# return val - term
|
|
#s = optimize.fixed_point(fixedsolve, 1.0, args=(x,N),xtol=1e-5)
|
|
#xbar = -s*log(sum(exp(-x/s),axis=0)*1.0/N)
|
|
xbar, s = distributions.gumbel_l.fit(x)
|
|
w = (y-xbar)/s
|
|
z = distributions.gumbel_l.cdf(w)
|
|
sig = array([25,10,5,2.5,1])
|
|
critical = around(_Avals_gumbel / (1.0 + 0.2/sqrt(N)),3)
|
|
|
|
i = arange(1,N+1)
|
|
S = sum((2*i-1.0)/N*(log(z)+log(1-z[::-1])),axis=0)
|
|
A2 = -N-S
|
|
return A2, critical, sig
|
|
|
|
|
|
def _anderson_ksamp_midrank(samples, Z, Zstar, k, n, N):
|
|
"""
|
|
Compute A2akN equation 7 of Scholz and Stephens.
|
|
|
|
Parameters
|
|
----------
|
|
samples : sequence of 1-D array_like
|
|
Array of sample arrays.
|
|
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.
|
|
"""
|
|
|
|
A2akN = 0.
|
|
Z_ssorted_left = Z.searchsorted(Zstar, 'left')
|
|
if N == Zstar.size:
|
|
lj = 1.
|
|
else:
|
|
lj = Z.searchsorted(Zstar, 'right') - Z_ssorted_left
|
|
Bj = Z_ssorted_left + lj / 2.
|
|
for i in arange(0, k):
|
|
s = np.sort(samples[i])
|
|
s_ssorted_right = s.searchsorted(Zstar, side='right')
|
|
Mij = s_ssorted_right.astype(np.float)
|
|
fij = s_ssorted_right - s.searchsorted(Zstar, 'left')
|
|
Mij -= fij / 2.
|
|
inner = lj / float(N) * (N * Mij - Bj * n[i])**2 / \
|
|
(Bj * (N - Bj) - N * lj / 4.)
|
|
A2akN += inner.sum() / n[i]
|
|
A2akN *= (N - 1.) / N
|
|
return A2akN
|
|
|
|
|
|
def _anderson_ksamp_right(samples, Z, Zstar, k, n, N):
|
|
"""
|
|
Compute A2akN equation 6 of Scholz & Stephens.
|
|
|
|
Parameters
|
|
----------
|
|
samples : sequence of 1-D array_like
|
|
Array of sample arrays.
|
|
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
|
|
-------
|
|
A2KN : float
|
|
The A2KN statistics of Scholz and Stephens 1987.
|
|
"""
|
|
|
|
A2kN = 0.
|
|
lj = Z.searchsorted(Zstar[:-1], 'right') - Z.searchsorted(Zstar[:-1],
|
|
'left')
|
|
Bj = lj.cumsum()
|
|
for i in arange(0, k):
|
|
s = np.sort(samples[i])
|
|
Mij = s.searchsorted(Zstar[:-1], side='right')
|
|
inner = lj / float(N) * (N * Mij - Bj * n[i])**2 / (Bj * (N - Bj))
|
|
A2kN += inner.sum() / n[i]
|
|
return A2kN
|
|
|
|
|
|
def anderson_ksamp(samples, midrank=True):
|
|
"""The Anderson-Darling test for k-samples.
|
|
|
|
The k-sample Anderson-Darling test is a modification of the
|
|
one-sample Anderson-Darling test. It tests the null hypothesis
|
|
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
|
|
-------
|
|
A2 : float
|
|
Normalized k-sample Anderson-Darling test statistic.
|
|
critical : array
|
|
The critical values for significance levels 25%, 10%, 5%, 2.5%, 1%.
|
|
p : float
|
|
An approximate significance level at which the null hypothesis for the
|
|
provided samples can be rejected.
|
|
|
|
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.
|
|
|
|
.. 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
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> np.random.seed(314159)
|
|
|
|
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
|
|
significance level of 3.1%:
|
|
|
|
>>> stats.anderson_ksamp([np.random.normal(size=50),
|
|
... np.random.normal(loc=0.5, size=30)])
|
|
(2.4615796189876105,
|
|
array([ 0.325, 1.226, 1.961, 2.718, 3.752]),
|
|
0.03134990135800783)
|
|
|
|
|
|
The null hypothesis cannot be rejected for three samples from an
|
|
identical distribution. The approximate p-value (87%) has to be
|
|
computed by extrapolation and may not be very accurate:
|
|
|
|
>>> stats.anderson_ksamp([np.random.normal(size=50),
|
|
... np.random.normal(size=30), np.random.normal(size=20)])
|
|
(-0.73091722665244196,
|
|
array([ 0.44925884, 1.3052767 , 1.9434184 , 2.57696569, 3.41634856]),
|
|
0.8789283903979661)
|
|
|
|
"""
|
|
k = len(samples)
|
|
if (k < 2):
|
|
raise ValueError("anderson_ksamp needs at least two samples")
|
|
|
|
samples = list(map(np.asarray, samples))
|
|
Z = np.sort(np.hstack(samples))
|
|
N = Z.size
|
|
Zstar = np.unique(Z)
|
|
if Zstar.size < 2:
|
|
raise ValueError("anderson_ksamp needs more than one distinct "
|
|
"observation")
|
|
|
|
n = np.array([sample.size for sample in samples])
|
|
if any(n == 0):
|
|
raise ValueError("anderson_ksamp encountered sample without "
|
|
"observations")
|
|
|
|
if midrank:
|
|
A2kN = _anderson_ksamp_midrank(samples, Z, Zstar, k, n, N)
|
|
else:
|
|
A2kN = _anderson_ksamp_right(samples, Z, Zstar, k, n, N)
|
|
|
|
h = (1. / arange(1, N)).sum()
|
|
H = (1. / n).sum()
|
|
g = 0
|
|
for l in arange(1, N-1):
|
|
inner = np.array([1. / ((N - l) * m) for m in arange(l+1, N)])
|
|
g += inner.sum()
|
|
|
|
a = (4*g - 6) * (k - 1) + (10 - 6*g)*H
|
|
b = (2*g - 4)*k**2 + 8*h*k + (2*g - 14*h - 4)*H - 8*h + 4*g - 6
|
|
c = (6*h + 2*g - 2)*k**2 + (4*h - 4*g + 6)*k + (2*h - 6)*H + 4*h
|
|
d = (2*h + 6)*k**2 - 4*h*k
|
|
sigmasq = (a*N**3 + b*N**2 + c*N + d) / ((N - 1.) * (N - 2.) * (N - 3.))
|
|
m = k - 1
|
|
A2 = (A2kN - m) / math.sqrt(sigmasq)
|
|
|
|
# The b_i values are the interpolation coefficients from Table 2
|
|
# of Scholz and Stephens 1987
|
|
b0 = np.array([0.675, 1.281, 1.645, 1.96, 2.326])
|
|
b1 = np.array([-0.245, 0.25, 0.678, 1.149, 1.822])
|
|
b2 = np.array([-0.105, -0.305, -0.362, -0.391, -0.396])
|
|
critical = b0 + b1 / math.sqrt(m) + b2 / m
|
|
pf = np.polyfit(critical, log(np.array([0.25, 0.1, 0.05, 0.025, 0.01])), 2)
|
|
if A2 < critical.min() or A2 > critical.max():
|
|
warnings.warn("approximate p-value will be computed by extrapolation")
|
|
|
|
p = math.exp(np.polyval(pf, A2))
|
|
return A2, critical, p
|
|
|
|
|
|
def ansari(x,y):
|
|
"""
|
|
Perform the Ansari-Bradley test for equal scale parameters
|
|
|
|
The Ansari-Bradley test is a non-parametric test for the equality
|
|
of the scale parameter of the distributions from which two
|
|
samples were drawn.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
arrays of sample data
|
|
|
|
Returns
|
|
-------
|
|
AB : float
|
|
The Ansari-Bradley test statistic
|
|
p-value : 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] Sprent, Peter and N.C. Smeeton. Applied nonparametric statistical
|
|
methods. 3rd ed. Chapman and Hall/CRC. 2001. Section 5.8.2.
|
|
|
|
"""
|
|
x,y = asarray(x),asarray(y)
|
|
n = len(x)
|
|
m = len(y)
|
|
if m < 1:
|
|
raise ValueError("Not enough other observations.")
|
|
if n < 1:
|
|
raise ValueError("Not enough test observations.")
|
|
N = m+n
|
|
xy = r_[x,y] # combine
|
|
rank = stats.rankdata(xy)
|
|
symrank = amin(array((rank,N-rank+1)),0)
|
|
AB = sum(symrank[:n],axis=0)
|
|
uxy = unique(xy)
|
|
repeats = (len(uxy) != len(xy))
|
|
exact = ((m < 55) and (n < 55) and not repeats)
|
|
if repeats and ((m < 55) or (n < 55)):
|
|
warnings.warn("Ties preclude use of exact statistic.")
|
|
if exact:
|
|
astart, a1, ifault = statlib.gscale(n,m)
|
|
ind = AB-astart
|
|
total = sum(a1,axis=0)
|
|
if ind < len(a1)/2.0:
|
|
cind = int(ceil(ind))
|
|
if (ind == cind):
|
|
pval = 2.0*sum(a1[:cind+1],axis=0)/total
|
|
else:
|
|
pval = 2.0*sum(a1[:cind],axis=0)/total
|
|
else:
|
|
find = int(floor(ind))
|
|
if (ind == floor(ind)):
|
|
pval = 2.0*sum(a1[find:],axis=0)/total
|
|
else:
|
|
pval = 2.0*sum(a1[find+1:],axis=0)/total
|
|
return AB, min(1.0,pval)
|
|
|
|
# otherwise compute normal approximation
|
|
if N % 2: # N odd
|
|
mnAB = n*(N+1.0)**2 / 4.0 / N
|
|
varAB = n*m*(N+1.0)*(3+N**2)/(48.0*N**2)
|
|
else:
|
|
mnAB = n*(N+2.0)/4.0
|
|
varAB = m*n*(N+2)*(N-2.0)/48/(N-1.0)
|
|
if repeats: # adjust variance estimates
|
|
# compute sum(tj * rj**2,axis=0)
|
|
fac = sum(symrank**2,axis=0)
|
|
if N % 2: # N odd
|
|
varAB = m*n*(16*N*fac-(N+1)**4)/(16.0 * N**2 * (N-1))
|
|
else: # N even
|
|
varAB = m*n*(16*fac-N*(N+2)**2)/(16.0 * N * (N-1))
|
|
z = (AB - mnAB)/sqrt(varAB)
|
|
pval = distributions.norm.sf(abs(z)) * 2.0
|
|
return AB, pval
|
|
|
|
|
|
def bartlett(*args):
|
|
"""
|
|
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. May be different lengths.
|
|
|
|
Returns
|
|
-------
|
|
T : float
|
|
The test statistic.
|
|
p-value : float
|
|
The p-value of the test.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://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.
|
|
|
|
"""
|
|
k = len(args)
|
|
if k < 2:
|
|
raise ValueError("Must enter at least two input sample vectors.")
|
|
Ni = zeros(k)
|
|
ssq = zeros(k,'d')
|
|
for j in range(k):
|
|
Ni[j] = len(args[j])
|
|
ssq[j] = np.var(args[j], ddof=1)
|
|
Ntot = sum(Ni,axis=0)
|
|
spsq = sum((Ni-1)*ssq,axis=0)/(1.0*(Ntot-k))
|
|
numer = (Ntot*1.0-k)*log(spsq) - sum((Ni-1.0)*log(ssq),axis=0)
|
|
denom = 1.0 + (1.0/(3*(k-1)))*((sum(1.0/(Ni-1.0),axis=0))-1.0/(Ntot-k))
|
|
T = numer / denom
|
|
pval = distributions.chi2.sf(T,k-1) # 1 - cdf
|
|
return T, pval
|
|
|
|
|
|
def levene(*args, **kwds):
|
|
"""
|
|
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
|
|
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
|
|
-------
|
|
W : float
|
|
The test statistic.
|
|
p-value : 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.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://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
|
|
|
|
"""
|
|
# Handle keyword arguments.
|
|
center = 'median'
|
|
proportiontocut = 0.05
|
|
for kw, value in kwds.items():
|
|
if kw not in ['center', 'proportiontocut']:
|
|
raise TypeError("levene() got an unexpected keyword "
|
|
"argument '%s'" % kw)
|
|
if kw == 'center':
|
|
center = value
|
|
else:
|
|
proportiontocut = value
|
|
|
|
k = len(args)
|
|
if k < 2:
|
|
raise ValueError("Must enter at least two input sample vectors.")
|
|
Ni = zeros(k)
|
|
Yci = zeros(k, 'd')
|
|
|
|
if center not in ['mean', 'median', 'trimmed']:
|
|
raise ValueError("Keyword argument <center> must be 'mean', 'median'"
|
|
+ "or 'trimmed'.")
|
|
|
|
if center == 'median':
|
|
func = lambda x: np.median(x, axis=0)
|
|
elif center == 'mean':
|
|
func = lambda x: np.mean(x, axis=0)
|
|
else: # center == 'trimmed'
|
|
args = tuple(stats.trimboth(np.sort(arg), proportiontocut)
|
|
for arg in args)
|
|
func = lambda x: np.mean(x, axis=0)
|
|
|
|
for j in range(k):
|
|
Ni[j] = len(args[j])
|
|
Yci[j] = func(args[j])
|
|
Ntot = sum(Ni, axis=0)
|
|
|
|
# compute Zij's
|
|
Zij = [None]*k
|
|
for i in range(k):
|
|
Zij[i] = abs(asarray(args[i])-Yci[i])
|
|
# compute Zbari
|
|
Zbari = zeros(k, 'd')
|
|
Zbar = 0.0
|
|
for i in range(k):
|
|
Zbari[i] = np.mean(Zij[i], axis=0)
|
|
Zbar += Zbari[i]*Ni[i]
|
|
Zbar /= Ntot
|
|
|
|
numer = (Ntot-k) * sum(Ni*(Zbari-Zbar)**2, axis=0)
|
|
|
|
# compute denom_variance
|
|
dvar = 0.0
|
|
for i in range(k):
|
|
dvar += sum((Zij[i]-Zbari[i])**2, axis=0)
|
|
|
|
denom = (k-1.0)*dvar
|
|
|
|
W = numer / denom
|
|
pval = distributions.f.sf(W, k-1, Ntot-k) # 1 - cdf
|
|
return W, pval
|
|
|
|
|
|
@setastest(False)
|
|
def binom_test(x, n=None, p=0.5):
|
|
"""
|
|
Perform a test that the probability of success is p.
|
|
|
|
This is an exact, two-sided test of the null hypothesis
|
|
that the probability of success in a Bernoulli experiment
|
|
is `p`.
|
|
|
|
Parameters
|
|
----------
|
|
x : integer 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 : integer
|
|
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
|
|
|
|
Returns
|
|
-------
|
|
p-value : float
|
|
The p-value of the hypothesis test
|
|
|
|
References
|
|
----------
|
|
.. [1] http://en.wikipedia.org/wiki/Binomial_test
|
|
|
|
"""
|
|
x = atleast_1d(x).astype(np.integer)
|
|
if len(x) == 2:
|
|
n = x[1]+x[0]
|
|
x = x[0]
|
|
elif len(x) == 1:
|
|
x = x[0]
|
|
if n is None or n < x:
|
|
raise ValueError("n must be >= x")
|
|
n = np.int_(n)
|
|
else:
|
|
raise ValueError("Incorrect length for x.")
|
|
|
|
if (p > 1.0) or (p < 0.0):
|
|
raise ValueError("p must be in range [0,1]")
|
|
|
|
d = distributions.binom.pmf(x, n, p)
|
|
rerr = 1+1e-7
|
|
if (x == p*n):
|
|
# special case as shortcut, would also be handled by `else` below
|
|
pval = 1.
|
|
elif (x < p*n):
|
|
i = np.arange(np.ceil(p*n), n+1)
|
|
y = np.sum(distributions.binom.pmf(i, n, p) <= d*rerr, axis=0)
|
|
pval = (distributions.binom.cdf(x, n, p) +
|
|
distributions.binom.sf(n-y, n, p))
|
|
else:
|
|
i = np.arange(np.floor(p*n) + 1)
|
|
y = np.sum(distributions.binom.pmf(i, n, p) <= d*rerr, axis=0)
|
|
pval = (distributions.binom.cdf(y-1, n, p) +
|
|
distributions.binom.sf(x-1, n, p))
|
|
|
|
return min(1.0, pval)
|
|
|
|
|
|
def _apply_func(x, g, func):
|
|
# g is list of indices into x
|
|
# separating x into different groups
|
|
# func should be applied over the groups
|
|
g = unique(r_[0, g, len(x)])
|
|
output = []
|
|
for k in range(len(g)-1):
|
|
output.append(func(x[g[k]:g[k+1]]))
|
|
return asarray(output)
|
|
|
|
|
|
def fligner(*args, **kwds):
|
|
"""
|
|
Perform Fligner's test for equal variances.
|
|
|
|
Fligner's test tests the null hypothesis that all input samples
|
|
are from populations with equal variances. Fligner's test is
|
|
non-parametric in contrast to Bartlett's test `bartlett` and
|
|
Levene's test `levene`.
|
|
|
|
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
|
|
-------
|
|
Xsq : float
|
|
The test statistic.
|
|
p-value : float
|
|
The p-value for the hypothesis test.
|
|
|
|
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.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://www.stat.psu.edu/~bgl/center/tr/TR993.ps
|
|
|
|
.. [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.
|
|
|
|
"""
|
|
# Handle keyword arguments.
|
|
center = 'median'
|
|
proportiontocut = 0.05
|
|
for kw, value in kwds.items():
|
|
if kw not in ['center', 'proportiontocut']:
|
|
raise TypeError("fligner() got an unexpected keyword "
|
|
"argument '%s'" % kw)
|
|
if kw == 'center':
|
|
center = value
|
|
else:
|
|
proportiontocut = value
|
|
|
|
k = len(args)
|
|
if k < 2:
|
|
raise ValueError("Must enter at least two input sample vectors.")
|
|
|
|
if center not in ['mean','median','trimmed']:
|
|
raise ValueError("Keyword argument <center> must be 'mean', 'median'"
|
|
+ "or 'trimmed'.")
|
|
|
|
if center == 'median':
|
|
func = lambda x: np.median(x, axis=0)
|
|
elif center == 'mean':
|
|
func = lambda x: np.mean(x, axis=0)
|
|
else: # center == 'trimmed'
|
|
args = tuple(stats.trimboth(arg, proportiontocut) for arg in args)
|
|
func = lambda x: np.mean(x, axis=0)
|
|
|
|
Ni = asarray([len(args[j]) for j in range(k)])
|
|
Yci = asarray([func(args[j]) for j in range(k)])
|
|
Ntot = sum(Ni, axis=0)
|
|
# compute Zij's
|
|
Zij = [abs(asarray(args[i]) - Yci[i]) for i in range(k)]
|
|
allZij = []
|
|
g = [0]
|
|
for i in range(k):
|
|
allZij.extend(list(Zij[i]))
|
|
g.append(len(allZij))
|
|
|
|
ranks = stats.rankdata(allZij)
|
|
a = distributions.norm.ppf(ranks/(2*(Ntot + 1.0)) + 0.5)
|
|
|
|
# compute Aibar
|
|
Aibar = _apply_func(a, g, sum) / Ni
|
|
anbar = np.mean(a, axis=0)
|
|
varsq = np.var(a, axis=0, ddof=1)
|
|
Xsq = sum(Ni*(asarray(Aibar) - anbar)**2.0, axis=0)/varsq
|
|
pval = distributions.chi2.sf(Xsq, k - 1) # 1 - cdf
|
|
return Xsq, pval
|
|
|
|
|
|
def mood(x, y, axis=0):
|
|
"""
|
|
Perform Mood's test for equal scale parameters.
|
|
|
|
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.
|
|
|
|
Returns
|
|
-------
|
|
z : scalar or ndarray
|
|
The z-score for the hypothesis test. For 1-D inputs a scalar is
|
|
returned.
|
|
p-value : 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.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x2 = np.random.randn(2, 45, 6, 7)
|
|
>>> x1 = np.random.randn(2, 30, 6, 7)
|
|
>>> z, p = stats.mood(x1, x2, axis=1)
|
|
>>> p.shape
|
|
(2, 6, 7)
|
|
|
|
Find the number of points where the difference in scale is not significant:
|
|
|
|
>>> (p > 0.1).sum()
|
|
74
|
|
|
|
Perform the test with different scales:
|
|
|
|
>>> x1 = np.random.randn(2, 30)
|
|
>>> x2 = np.random.randn(2, 35) * 10.0
|
|
>>> stats.mood(x1, x2, axis=1)
|
|
(array([-5.84332354, -5.6840814 ]), array([5.11694980e-09, 1.31517628e-08]))
|
|
|
|
"""
|
|
x = np.asarray(x, dtype=float)
|
|
y = np.asarray(y, dtype=float)
|
|
|
|
if axis is None:
|
|
x = x.flatten()
|
|
y = y.flatten()
|
|
axis = 0
|
|
|
|
# Determine shape of the result arrays
|
|
res_shape = tuple([x.shape[ax] for ax in range(len(x.shape)) if ax != axis])
|
|
if not (res_shape == tuple([y.shape[ax] for ax in range(len(y.shape)) if
|
|
ax != axis])):
|
|
raise ValueError("Dimensions of x and y on all axes except `axis` "
|
|
"should match")
|
|
|
|
n = x.shape[axis]
|
|
m = y.shape[axis]
|
|
N = m + n
|
|
if N < 3:
|
|
raise ValueError("Not enough observations.")
|
|
|
|
xy = np.concatenate((x, y), axis=axis)
|
|
if axis != 0:
|
|
xy = np.rollaxis(xy, axis)
|
|
|
|
xy = xy.reshape(xy.shape[0], -1)
|
|
|
|
# Generalized to the n-dimensional case by adding the axis argument, and
|
|
# using for loops, since rankdata is not vectorized. For improving
|
|
# performance consider vectorizing rankdata function.
|
|
all_ranks = np.zeros_like(xy)
|
|
for j in range(xy.shape[1]):
|
|
all_ranks[:, j] = stats.rankdata(xy[:, j])
|
|
|
|
Ri = all_ranks[:n]
|
|
M = sum((Ri - (N + 1.0) / 2) ** 2, axis=0)
|
|
# Approx stat.
|
|
mnM = n * (N * N - 1.0) / 12
|
|
varM = m * n * (N + 1.0) * (N + 2) * (N - 2) / 180
|
|
z = (M - mnM) / sqrt(varM)
|
|
|
|
# sf for right tail, cdf for left tail. Factor 2 for two-sidedness
|
|
z_pos = z > 0
|
|
pval = np.zeros_like(z)
|
|
pval[z_pos] = 2 * distributions.norm.sf(z[z_pos])
|
|
pval[~z_pos] = 2 * distributions.norm.cdf(z[~z_pos])
|
|
|
|
if res_shape == ():
|
|
# Return scalars, not 0-D arrays
|
|
z = z[0]
|
|
pval = pval[0]
|
|
else:
|
|
z.shape = res_shape
|
|
pval.shape = res_shape
|
|
|
|
return z, pval
|
|
|
|
|
|
def wilcoxon(x, y=None, zero_method="wilcox", correction=False):
|
|
"""
|
|
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
|
|
The first set of measurements.
|
|
y : array_like, optional
|
|
The second set of measurements. If `y` is not given, then the `x`
|
|
array is considered to be the differences between the two sets of
|
|
measurements.
|
|
zero_method : string, {"pratt", "wilcox", "zsplit"}, optional
|
|
"pratt":
|
|
Pratt treatment: includes zero-differences in the ranking process
|
|
(more conservative)
|
|
"wilcox":
|
|
Wilcox treatment: discards all zero-differences
|
|
"zsplit":
|
|
Zero rank split: just like Pratt, but spliting 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. Default is False.
|
|
|
|
Returns
|
|
-------
|
|
T : float
|
|
The sum of the ranks of the differences above or below zero, whichever
|
|
is smaller.
|
|
p-value : float
|
|
The two-sided p-value for the test.
|
|
|
|
Notes
|
|
-----
|
|
Because the normal approximation is used for the calculations, the
|
|
samples used should be large. A typical rule is to require that
|
|
n > 20.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
|
|
|
|
"""
|
|
|
|
if zero_method not in ["wilcox", "pratt", "zsplit"]:
|
|
raise ValueError("Zero method should be either 'wilcox' \
|
|
or 'pratt' or 'zsplit'")
|
|
|
|
if y is None:
|
|
d = x
|
|
else:
|
|
x, y = map(asarray, (x, y))
|
|
if len(x) != len(y):
|
|
raise ValueError('Unequal N in wilcoxon. Aborting.')
|
|
d = x-y
|
|
|
|
if zero_method == "wilcox":
|
|
d = compress(not_equal(d, 0), d, axis=-1) # Keep all non-zero differences
|
|
|
|
count = len(d)
|
|
if (count < 10):
|
|
warnings.warn("Warning: sample size too small for normal approximation.")
|
|
r = stats.rankdata(abs(d))
|
|
r_plus = sum((d > 0) * r, axis=0)
|
|
r_minus = sum((d < 0) * r, axis=0)
|
|
|
|
if zero_method == "zsplit":
|
|
r_zero = sum((d == 0) * r, axis=0)
|
|
r_plus += r_zero / 2.
|
|
r_minus += r_zero / 2.
|
|
|
|
T = min(r_plus, r_minus)
|
|
mn = count*(count + 1.) * 0.25
|
|
se = count*(count + 1.) * (2. * count + 1.)
|
|
|
|
if zero_method == "pratt":
|
|
r = r[d != 0]
|
|
|
|
replist, repnum = find_repeats(r)
|
|
if repnum.size != 0:
|
|
# Correction for repeated elements.
|
|
se -= 0.5 * (repnum * (repnum * repnum - 1)).sum()
|
|
|
|
se = sqrt(se / 24)
|
|
correction = 0.5 * int(bool(correction)) * np.sign(T - mn)
|
|
z = (T - mn - correction) / se
|
|
prob = 2. * distributions.norm.sf(abs(z))
|
|
return T, prob
|
|
|
|
|
|
@setastest(False)
|
|
def median_test(*args, **kwds):
|
|
"""
|
|
Mood's median test.
|
|
|
|
Test that two or more samples come from populations with the same median.
|
|
|
|
Let ``n = len(args)`` 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).
|
|
|
|
Returns
|
|
-------
|
|
stat : float
|
|
The test statistic. The statistic that is returned is determined by
|
|
`lambda_`. The default is Pearson's chi-squared statistic.
|
|
p : float
|
|
The p-value of the test.
|
|
m : 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.
|
|
|
|
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]
|
|
>>> stat, p, med, tbl = median_test(g1, g2, g3)
|
|
|
|
The median is
|
|
|
|
>>> med
|
|
34.0
|
|
|
|
and the contingency table is
|
|
|
|
>>> tbl
|
|
array([[ 5, 10, 7],
|
|
[11, 5, 10]])
|
|
|
|
`p` is too large to conclude that the medians are not the same:
|
|
|
|
>>> p
|
|
0.12609082774093244
|
|
|
|
The "G-test" can be performed by passing ``lambda_="log-likelihood"`` to
|
|
`median_test`.
|
|
|
|
>>> g, p, med, tbl = median_test(g1, g2, g3, lambda_="log-likelihood")
|
|
>>> p
|
|
0.12224779737117837
|
|
|
|
The median occurs several times in the data, so we'll get a different
|
|
result if, for example, ``ties="above"`` is used:
|
|
|
|
>>> stat, p, med, tbl = median_test(g1, g2, g3, ties="above")
|
|
>>> p
|
|
0.063873276069553273
|
|
|
|
>>> tbl
|
|
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`.
|
|
|
|
"""
|
|
ties = kwds.pop('ties', 'below')
|
|
correction = kwds.pop('correction', True)
|
|
lambda_ = kwds.pop('lambda_', None)
|
|
|
|
if len(kwds) > 0:
|
|
bad_kwd = kwds.keys()[0]
|
|
raise TypeError("median_test() got an unexpected keyword "
|
|
"argument %r" % bad_kwd)
|
|
|
|
if len(args) < 2:
|
|
raise ValueError('median_test requires two or more samples.')
|
|
|
|
ties_options = ['below', 'above', 'ignore']
|
|
if ties not in ties_options:
|
|
raise ValueError("invalid 'ties' option '%s'; 'ties' must be one "
|
|
"of: %s" % (ties, str(ties_options)[1:-1]))
|
|
|
|
data = [np.asarray(arg) for arg in args]
|
|
|
|
# Validate the sizes and shapes of the arguments.
|
|
for k, d in enumerate(data):
|
|
if d.size == 0:
|
|
raise ValueError("Sample %d is empty. All samples must "
|
|
"contain at least one value." % (k + 1))
|
|
if d.ndim != 1:
|
|
raise ValueError("Sample %d has %d dimensions. All "
|
|
"samples must be one-dimensional sequences." %
|
|
(k + 1, d.ndim))
|
|
|
|
grand_median = np.median(np.concatenate(data))
|
|
|
|
# Create the contingency table.
|
|
table = np.zeros((2, len(data)), dtype=np.int64)
|
|
for k, sample in enumerate(data):
|
|
nabove = count_nonzero(sample > grand_median)
|
|
nbelow = count_nonzero(sample < grand_median)
|
|
nequal = sample.size - (nabove + nbelow)
|
|
table[0, k] += nabove
|
|
table[1, k] += nbelow
|
|
if ties == "below":
|
|
table[1, k] += nequal
|
|
elif ties == "above":
|
|
table[0, k] += nequal
|
|
|
|
# Check that no row or column of the table is all zero.
|
|
# Such a table can not be given to chi2_contingency, because it would have
|
|
# a zero in the table of expected frequencies.
|
|
rowsums = table.sum(axis=1)
|
|
if rowsums[0] == 0:
|
|
raise ValueError("All values are below the grand median (%r)." %
|
|
grand_median)
|
|
if rowsums[1] == 0:
|
|
raise ValueError("All values are above the grand median (%r)." %
|
|
grand_median)
|
|
if ties == "ignore":
|
|
# We already checked that each sample has at least one value, but it
|
|
# is possible that all those values equal the grand median. If `ties`
|
|
# is "ignore", that would result in a column of zeros in `table`. We
|
|
# check for that case here.
|
|
zero_cols = np.where((table == 0).all(axis=0))[0]
|
|
if len(zero_cols) > 0:
|
|
msg = ("All values in sample %d are equal to the grand "
|
|
"median (%r), so they are ignored, resulting in an "
|
|
"empty sample." % (zero_cols[0] + 1, grand_median))
|
|
raise ValueError(msg)
|
|
|
|
stat, p, dof, expected = chi2_contingency(table, lambda_=lambda_,
|
|
correction=correction)
|
|
return stat, p, grand_median, table
|
|
|
|
|
|
def _hermnorm(N):
|
|
# return the negatively normalized hermite polynomials up to order N-1
|
|
# (inclusive)
|
|
# using the recursive relationship
|
|
# p_n+1 = p_n(x)' - x*p_n(x)
|
|
# and p_0(x) = 1
|
|
plist = [None]*N
|
|
plist[0] = poly1d(1)
|
|
for n in range(1,N):
|
|
plist[n] = plist[n-1].deriv() - poly1d([1,0])*plist[n-1]
|
|
return plist
|
|
|
|
|
|
def pdf_fromgamma(g1, g2, g3=0.0, g4=None):
|
|
if g4 is None:
|
|
g4 = 3*g2*g2
|
|
sigsq = 1.0/g2
|
|
sig = sqrt(sigsq)
|
|
mu = g1*sig**3.0
|
|
p12 = _hermnorm(13)
|
|
for k in range(13):
|
|
p12[k] = p12[k]/sig**k
|
|
|
|
# Add all of the terms to polynomial
|
|
totp = p12[0] - (g1/6.0*p12[3]) + \
|
|
(g2/24.0*p12[4] + g1*g1/72.0*p12[6]) - \
|
|
(g3/120.0*p12[5] + g1*g2/144.0*p12[7] + g1**3.0/1296.0*p12[9]) + \
|
|
(g4/720*p12[6] + (g2*g2/1152.0+g1*g3/720)*p12[8] +
|
|
g1*g1*g2/1728.0*p12[10] + g1**4.0/31104.0*p12[12])
|
|
# Final normalization
|
|
totp = totp / sqrt(2*pi)/sig
|
|
|
|
def thefunc(x):
|
|
xn = (x-mu)/sig
|
|
return totp(xn)*exp(-xn*xn/2.0)
|
|
return thefunc
|
|
|
|
|
|
def _circfuncs_common(samples, high, low):
|
|
samples = np.asarray(samples)
|
|
if samples.size == 0:
|
|
return np.nan, np.nan
|
|
|
|
ang = (samples - low)*2*pi / (high-low)
|
|
return samples, ang
|
|
|
|
|
|
def circmean(samples, high=2*pi, low=0, axis=None):
|
|
"""
|
|
Compute the circular mean for samples in a range.
|
|
|
|
Parameters
|
|
----------
|
|
samples : array_like
|
|
Input array.
|
|
high : float or int, optional
|
|
High boundary for circular mean range. Default is ``2*pi``.
|
|
low : float or int, optional
|
|
Low boundary for circular mean range. Default is 0.
|
|
axis : int, optional
|
|
Axis along which means are computed. The default is to compute
|
|
the mean of the flattened array.
|
|
|
|
Returns
|
|
-------
|
|
circmean : float
|
|
Circular mean.
|
|
|
|
"""
|
|
samples, ang = _circfuncs_common(samples, high, low)
|
|
res = angle(np.mean(exp(1j*ang), axis=axis))
|
|
mask = res < 0
|
|
if (mask.ndim > 0):
|
|
res[mask] += 2*pi
|
|
elif mask:
|
|
res = res + 2*pi
|
|
|
|
return res*(high-low)/2.0/pi + low
|
|
|
|
|
|
def circvar(samples, high=2*pi, low=0, axis=None):
|
|
"""
|
|
Compute the circular variance for samples assumed to be in a range
|
|
|
|
Parameters
|
|
----------
|
|
samples : array_like
|
|
Input array.
|
|
low : float or int, optional
|
|
Low boundary for circular variance range. Default is 0.
|
|
high : float or int, optional
|
|
High boundary for circular variance range. Default is ``2*pi``.
|
|
axis : int, optional
|
|
Axis along which variances are computed. The default is to compute
|
|
the variance of the flattened array.
|
|
|
|
Returns
|
|
-------
|
|
circvar : float
|
|
Circular variance.
|
|
|
|
Notes
|
|
-----
|
|
This uses a definition of circular variance that in the limit of small
|
|
angles returns a number close to the 'linear' variance.
|
|
|
|
"""
|
|
samples, ang = _circfuncs_common(samples, high, low)
|
|
res = np.mean(exp(1j*ang), axis=axis)
|
|
R = abs(res)
|
|
return ((high-low)/2.0/pi)**2 * 2 * log(1/R)
|
|
|
|
|
|
def circstd(samples, high=2*pi, low=0, axis=None):
|
|
"""
|
|
Compute the circular standard deviation for samples assumed to be in the
|
|
range [low to high].
|
|
|
|
Parameters
|
|
----------
|
|
samples : array_like
|
|
Input array.
|
|
low : float or int, optional
|
|
Low boundary for circular standard deviation range. Default is 0.
|
|
high : float or int, optional
|
|
High boundary for circular standard deviation range.
|
|
Default is ``2*pi``.
|
|
axis : int, optional
|
|
Axis along which standard deviations are computed. The default is
|
|
to compute the standard deviation of the flattened array.
|
|
|
|
Returns
|
|
-------
|
|
circstd : float
|
|
Circular standard deviation.
|
|
|
|
Notes
|
|
-----
|
|
This uses a definition of circular standard deviation that in the limit of
|
|
small angles returns a number close to the 'linear' standard deviation.
|
|
|
|
"""
|
|
samples, ang = _circfuncs_common(samples, high, low)
|
|
res = np.mean(exp(1j*ang), axis=axis)
|
|
R = abs(res)
|
|
return ((high-low)/2.0/pi) * sqrt(-2*log(R))
|
|
|
|
|
|
# Tests to include (from R) -- some of these already in stats.
|
|
########
|
|
# X Ansari-Bradley
|
|
# X Bartlett (and Levene)
|
|
# X Binomial
|
|
# Y Pearson's Chi-squared (stats.chisquare)
|
|
# Y Association Between Paired samples (stats.pearsonr, stats.spearmanr)
|
|
# stats.kendalltau) -- these need work though
|
|
# Fisher's exact test
|
|
# X Fligner-Killeen Test
|
|
# Y Friedman Rank Sum (stats.friedmanchisquare?)
|
|
# Y Kruskal-Wallis
|
|
# Y Kolmogorov-Smirnov
|
|
# Cochran-Mantel-Haenszel Chi-Squared for Count
|
|
# McNemar's Chi-squared for Count
|
|
# X Mood Two-Sample
|
|
# X Test For Equal Means in One-Way Layout (see stats.ttest also)
|
|
# Pairwise Comparisons of proportions
|
|
# Pairwise t tests
|
|
# Tabulate p values for pairwise comparisons
|
|
# Pairwise Wilcoxon rank sum tests
|
|
# Power calculations two sample test of prop.
|
|
# Power calculations for one and two sample t tests
|
|
# Equal or Given Proportions
|
|
# Trend in Proportions
|
|
# Quade Test
|
|
# Y Student's T Test
|
|
# Y F Test to compare two variances
|
|
# XY Wilcoxon Rank Sum and Signed Rank Tests
|