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8059 lines
274 KiB
8059 lines
274 KiB
# Copyright 2002 Gary Strangman. All rights reserved
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# Copyright 2002-2016 The SciPy Developers
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#
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# The original code from Gary Strangman was heavily adapted for
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# use in SciPy by Travis Oliphant. The original code came with the
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# following disclaimer:
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#
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# This software is provided "as-is". There are no expressed or implied
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# warranties of any kind, including, but not limited to, the warranties
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# of merchantability and fitness for a given application. In no event
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# shall Gary Strangman be liable for any direct, indirect, incidental,
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# special, exemplary or consequential damages (including, but not limited
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# to, loss of use, data or profits, or business interruption) however
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# caused and on any theory of liability, whether in contract, strict
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# liability or tort (including negligence or otherwise) arising in any way
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# out of the use of this software, even if advised of the possibility of
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# such damage.
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"""
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A collection of basic statistical functions for Python. The function
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names appear below.
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Some scalar functions defined here are also available in the scipy.special
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package where they work on arbitrary sized arrays.
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Disclaimers: The function list is obviously incomplete and, worse, the
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functions are not optimized. All functions have been tested (some more
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so than others), but they are far from bulletproof. Thus, as with any
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free software, no warranty or guarantee is expressed or implied. :-) A
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few extra functions that don't appear in the list below can be found by
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interested treasure-hunters. These functions don't necessarily have
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both list and array versions but were deemed useful.
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Central Tendency
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----------------
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.. autosummary::
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:toctree: generated/
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gmean
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hmean
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mode
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Moments
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-------
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.. autosummary::
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:toctree: generated/
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moment
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variation
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skew
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kurtosis
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normaltest
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Altered Versions
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----------------
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.. autosummary::
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:toctree: generated/
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tmean
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tvar
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tstd
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tsem
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describe
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Frequency Stats
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---------------
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.. autosummary::
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:toctree: generated/
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itemfreq
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scoreatpercentile
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percentileofscore
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cumfreq
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relfreq
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Variability
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-----------
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.. autosummary::
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:toctree: generated/
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obrientransform
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sem
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zmap
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zscore
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gstd
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iqr
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median_abs_deviation
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Trimming Functions
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------------------
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.. autosummary::
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:toctree: generated/
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trimboth
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trim1
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Correlation Functions
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---------------------
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.. autosummary::
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:toctree: generated/
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pearsonr
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fisher_exact
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spearmanr
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pointbiserialr
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kendalltau
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weightedtau
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linregress
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theilslopes
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multiscale_graphcorr
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Inferential Stats
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-----------------
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.. autosummary::
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:toctree: generated/
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ttest_1samp
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ttest_ind
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ttest_ind_from_stats
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ttest_rel
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chisquare
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power_divergence
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kstest
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ks_1samp
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ks_2samp
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epps_singleton_2samp
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mannwhitneyu
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ranksums
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wilcoxon
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kruskal
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friedmanchisquare
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brunnermunzel
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combine_pvalues
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Statistical Distances
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---------------------
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.. autosummary::
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:toctree: generated/
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wasserstein_distance
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energy_distance
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ANOVA Functions
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---------------
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.. autosummary::
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:toctree: generated/
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f_oneway
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Support Functions
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-----------------
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.. autosummary::
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:toctree: generated/
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rankdata
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rvs_ratio_uniforms
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References
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----------
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.. [CRCProbStat2000] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
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Probability and Statistics Tables and Formulae. Chapman & Hall: New
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York. 2000.
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"""
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import warnings
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import math
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from math import gcd
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from collections import namedtuple
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import numpy as np
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from numpy import array, asarray, ma
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from scipy.spatial.distance import cdist
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from scipy.ndimage import measurements
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from scipy._lib._util import (_lazywhere, check_random_state, MapWrapper,
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rng_integers, float_factorial)
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import scipy.special as special
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from scipy import linalg
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from . import distributions
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from . import mstats_basic
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from ._stats_mstats_common import (_find_repeats, linregress, theilslopes,
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siegelslopes)
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from ._stats import (_kendall_dis, _toint64, _weightedrankedtau,
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_local_correlations)
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from ._rvs_sampling import rvs_ratio_uniforms
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from ._hypotests import epps_singleton_2samp
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__all__ = ['find_repeats', 'gmean', 'hmean', 'mode', 'tmean', 'tvar',
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'tmin', 'tmax', 'tstd', 'tsem', 'moment', 'variation',
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'skew', 'kurtosis', 'describe', 'skewtest', 'kurtosistest',
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'normaltest', 'jarque_bera', 'itemfreq',
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'scoreatpercentile', 'percentileofscore',
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'cumfreq', 'relfreq', 'obrientransform',
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'sem', 'zmap', 'zscore', 'iqr', 'gstd', 'median_absolute_deviation',
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'median_abs_deviation',
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'sigmaclip', 'trimboth', 'trim1', 'trim_mean',
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'f_oneway', 'F_onewayConstantInputWarning',
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'F_onewayBadInputSizesWarning',
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'PearsonRConstantInputWarning', 'PearsonRNearConstantInputWarning',
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'pearsonr', 'fisher_exact', 'SpearmanRConstantInputWarning',
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'spearmanr', 'pointbiserialr',
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'kendalltau', 'weightedtau', 'multiscale_graphcorr',
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'linregress', 'siegelslopes', 'theilslopes', 'ttest_1samp',
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'ttest_ind', 'ttest_ind_from_stats', 'ttest_rel',
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'kstest', 'ks_1samp', 'ks_2samp',
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'chisquare', 'power_divergence', 'mannwhitneyu',
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'tiecorrect', 'ranksums', 'kruskal', 'friedmanchisquare',
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'rankdata', 'rvs_ratio_uniforms',
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'combine_pvalues', 'wasserstein_distance', 'energy_distance',
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'brunnermunzel', 'epps_singleton_2samp']
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def _contains_nan(a, nan_policy='propagate'):
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policies = ['propagate', 'raise', 'omit']
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if nan_policy not in policies:
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raise ValueError("nan_policy must be one of {%s}" %
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', '.join("'%s'" % s for s in policies))
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try:
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# Calling np.sum to avoid creating a huge array into memory
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# e.g. np.isnan(a).any()
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with np.errstate(invalid='ignore'):
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contains_nan = np.isnan(np.sum(a))
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except TypeError:
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# This can happen when attempting to sum things which are not
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# numbers (e.g. as in the function `mode`). Try an alternative method:
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try:
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contains_nan = np.nan in set(a.ravel())
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except TypeError:
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# Don't know what to do. Fall back to omitting nan values and
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# issue a warning.
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contains_nan = False
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nan_policy = 'omit'
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warnings.warn("The input array could not be properly checked for nan "
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"values. nan values will be ignored.", RuntimeWarning)
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if contains_nan and nan_policy == 'raise':
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raise ValueError("The input contains nan values")
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return contains_nan, nan_policy
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def _chk_asarray(a, axis):
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if axis is None:
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a = np.ravel(a)
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outaxis = 0
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else:
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a = np.asarray(a)
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outaxis = axis
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if a.ndim == 0:
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a = np.atleast_1d(a)
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return a, outaxis
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def _chk2_asarray(a, b, axis):
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if axis is None:
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a = np.ravel(a)
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b = np.ravel(b)
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outaxis = 0
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else:
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a = np.asarray(a)
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b = np.asarray(b)
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outaxis = axis
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if a.ndim == 0:
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a = np.atleast_1d(a)
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if b.ndim == 0:
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b = np.atleast_1d(b)
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return a, b, outaxis
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def _shape_with_dropped_axis(a, axis):
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"""
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Given an array `a` and an integer `axis`, return the shape
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of `a` with the `axis` dimension removed.
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Examples
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--------
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>>> a = np.zeros((3, 5, 2))
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>>> _shape_with_dropped_axis(a, 1)
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(3, 2)
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"""
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shp = list(a.shape)
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try:
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del shp[axis]
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except IndexError:
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raise np.AxisError(axis, a.ndim) from None
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return tuple(shp)
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def _broadcast_shapes(shape1, shape2):
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"""
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Given two shapes (i.e. tuples of integers), return the shape
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that would result from broadcasting two arrays with the given
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shapes.
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Examples
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--------
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>>> _broadcast_shapes((2, 1), (4, 1, 3))
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(4, 2, 3)
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"""
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d = len(shape1) - len(shape2)
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if d <= 0:
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shp1 = (1,)*(-d) + shape1
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shp2 = shape2
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elif d > 0:
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shp1 = shape1
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shp2 = (1,)*d + shape2
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shape = []
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for n1, n2 in zip(shp1, shp2):
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if n1 == 1:
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n = n2
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elif n2 == 1 or n1 == n2:
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n = n1
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else:
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raise ValueError(f'shapes {shape1} and {shape2} could not be '
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'broadcast together')
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shape.append(n)
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return tuple(shape)
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def _broadcast_shapes_with_dropped_axis(a, b, axis):
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"""
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Given two arrays `a` and `b` and an integer `axis`, find the
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shape of the broadcast result after dropping `axis` from the
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shapes of `a` and `b`.
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Examples
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--------
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>>> a = np.zeros((5, 2, 1))
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>>> b = np.zeros((1, 9, 3))
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>>> _broadcast_shapes_with_dropped_axis(a, b, 1)
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(5, 3)
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"""
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shp1 = _shape_with_dropped_axis(a, axis)
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shp2 = _shape_with_dropped_axis(b, axis)
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try:
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shp = _broadcast_shapes(shp1, shp2)
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except ValueError:
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raise ValueError(f'non-axis shapes {shp1} and {shp2} could not be '
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'broadcast together') from None
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return shp
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def gmean(a, axis=0, dtype=None):
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"""
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Compute the geometric mean along the specified axis.
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Return the geometric average of the array elements.
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That is: n-th root of (x1 * x2 * ... * xn)
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Parameters
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----------
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a : array_like
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Input array or object that can be converted to an array.
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axis : int or None, optional
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Axis along which the geometric mean is computed. Default is 0.
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If None, compute over the whole array `a`.
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dtype : dtype, optional
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Type of the returned array and of the accumulator in which the
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elements are summed. If dtype is not specified, it defaults to the
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dtype of a, unless a has an integer dtype with a precision less than
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that of the default platform integer. In that case, the default
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platform integer is used.
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Returns
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-------
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gmean : ndarray
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See `dtype` parameter above.
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See Also
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--------
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numpy.mean : Arithmetic average
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numpy.average : Weighted average
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hmean : Harmonic mean
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Notes
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-----
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The geometric average is computed over a single dimension of the input
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array, axis=0 by default, or all values in the array if axis=None.
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float64 intermediate and return values are used for integer inputs.
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Use masked arrays to ignore any non-finite values in the input or that
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arise in the calculations such as Not a Number and infinity because masked
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arrays automatically mask any non-finite values.
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Examples
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--------
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>>> from scipy.stats import gmean
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>>> gmean([1, 4])
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2.0
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>>> gmean([1, 2, 3, 4, 5, 6, 7])
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3.3800151591412964
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"""
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if not isinstance(a, np.ndarray):
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# if not an ndarray object attempt to convert it
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log_a = np.log(np.array(a, dtype=dtype))
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elif dtype:
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# Must change the default dtype allowing array type
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if isinstance(a, np.ma.MaskedArray):
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log_a = np.log(np.ma.asarray(a, dtype=dtype))
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else:
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log_a = np.log(np.asarray(a, dtype=dtype))
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else:
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log_a = np.log(a)
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return np.exp(log_a.mean(axis=axis))
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def hmean(a, axis=0, dtype=None):
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"""
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Calculate the harmonic mean along the specified axis.
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That is: n / (1/x1 + 1/x2 + ... + 1/xn)
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Parameters
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----------
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a : array_like
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Input array, masked array or object that can be converted to an array.
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axis : int or None, optional
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Axis along which the harmonic mean is computed. Default is 0.
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If None, compute over the whole array `a`.
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dtype : dtype, optional
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Type of the returned array and of the accumulator in which the
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elements are summed. If `dtype` is not specified, it defaults to the
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dtype of `a`, unless `a` has an integer `dtype` with a precision less
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than that of the default platform integer. In that case, the default
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platform integer is used.
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Returns
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-------
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hmean : ndarray
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See `dtype` parameter above.
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See Also
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--------
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numpy.mean : Arithmetic average
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numpy.average : Weighted average
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gmean : Geometric mean
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|
Notes
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-----
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The harmonic mean is computed over a single dimension of the input
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array, axis=0 by default, or all values in the array if axis=None.
|
|
float64 intermediate and return values are used for integer inputs.
|
|
|
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Use masked arrays to ignore any non-finite values in the input or that
|
|
arise in the calculations such as Not a Number and infinity.
|
|
|
|
Examples
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--------
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>>> from scipy.stats import hmean
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>>> hmean([1, 4])
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1.6000000000000001
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>>> hmean([1, 2, 3, 4, 5, 6, 7])
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2.6997245179063363
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"""
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if not isinstance(a, np.ndarray):
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a = np.array(a, dtype=dtype)
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if np.all(a >= 0):
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# Harmonic mean only defined if greater than or equal to to zero.
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if isinstance(a, np.ma.MaskedArray):
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size = a.count(axis)
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else:
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if axis is None:
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a = a.ravel()
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size = a.shape[0]
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else:
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size = a.shape[axis]
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with np.errstate(divide='ignore'):
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return size / np.sum(1.0 / a, axis=axis, dtype=dtype)
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else:
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raise ValueError("Harmonic mean only defined if all elements greater "
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"than or equal to zero")
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|
|
|
|
ModeResult = namedtuple('ModeResult', ('mode', 'count'))
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|
|
|
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def mode(a, axis=0, nan_policy='propagate'):
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"""
|
|
Return an array of the modal (most common) value in the passed array.
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If there is more than one such value, only the smallest is returned.
|
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The bin-count for the modal bins is also returned.
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|
|
Parameters
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|
----------
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a : array_like
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n-dimensional array of which to find mode(s).
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axis : int or None, optional
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Axis along which to operate. Default is 0. If None, compute over
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the whole array `a`.
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|
nan_policy : {'propagate', 'raise', 'omit'}, optional
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|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
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* 'propagate': returns nan
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* 'raise': throws an error
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* 'omit': performs the calculations ignoring nan values
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|
|
Returns
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|
-------
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|
mode : ndarray
|
|
Array of modal values.
|
|
count : ndarray
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Array of counts for each mode.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[6, 8, 3, 0],
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... [3, 2, 1, 7],
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... [8, 1, 8, 4],
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... [5, 3, 0, 5],
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... [4, 7, 5, 9]])
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|
>>> from scipy import stats
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>>> stats.mode(a)
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ModeResult(mode=array([[3, 1, 0, 0]]), count=array([[1, 1, 1, 1]]))
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|
|
To get mode of whole array, specify ``axis=None``:
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>>> stats.mode(a, axis=None)
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ModeResult(mode=array([3]), count=array([3]))
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"""
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|
a, axis = _chk_asarray(a, axis)
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if a.size == 0:
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return ModeResult(np.array([]), np.array([]))
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|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
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|
|
if contains_nan and nan_policy == 'omit':
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a = ma.masked_invalid(a)
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return mstats_basic.mode(a, axis)
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|
|
if a.dtype == object and np.nan in set(a.ravel()):
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# Fall back to a slower method since np.unique does not work with NaN
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|
scores = set(np.ravel(a)) # get ALL unique values
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|
testshape = list(a.shape)
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|
testshape[axis] = 1
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oldmostfreq = np.zeros(testshape, dtype=a.dtype)
|
|
oldcounts = np.zeros(testshape, dtype=int)
|
|
|
|
for score in scores:
|
|
template = (a == score)
|
|
counts = np.expand_dims(np.sum(template, axis), axis)
|
|
mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
|
|
oldcounts = np.maximum(counts, oldcounts)
|
|
oldmostfreq = mostfrequent
|
|
|
|
return ModeResult(mostfrequent, oldcounts)
|
|
|
|
def _mode1D(a):
|
|
vals, cnts = np.unique(a, return_counts=True)
|
|
return vals[cnts.argmax()], cnts.max()
|
|
|
|
# np.apply_along_axis will convert the _mode1D tuples to a numpy array, casting types in the process
|
|
# This recreates the results without that issue
|
|
# View of a, rotated so the requested axis is last
|
|
in_dims = list(range(a.ndim))
|
|
a_view = np.transpose(a, in_dims[:axis] + in_dims[axis+1:] + [axis])
|
|
|
|
inds = np.ndindex(a_view.shape[:-1])
|
|
modes = np.empty(a_view.shape[:-1], dtype=a.dtype)
|
|
counts = np.zeros(a_view.shape[:-1], dtype=np.int_)
|
|
for ind in inds:
|
|
modes[ind], counts[ind] = _mode1D(a_view[ind])
|
|
newshape = list(a.shape)
|
|
newshape[axis] = 1
|
|
return ModeResult(modes.reshape(newshape), counts.reshape(newshape))
|
|
|
|
|
|
def _mask_to_limits(a, limits, inclusive):
|
|
"""Mask an array for values outside of given limits.
|
|
|
|
This is primarily a utility function.
|
|
|
|
Parameters
|
|
----------
|
|
a : array
|
|
limits : (float or None, float or None)
|
|
A tuple consisting of the (lower limit, upper limit). Values in the
|
|
input array less than the lower limit or greater than the upper limit
|
|
will be masked out. None implies no limit.
|
|
inclusive : (bool, bool)
|
|
A tuple consisting of the (lower flag, upper flag). These flags
|
|
determine whether values exactly equal to lower or upper are allowed.
|
|
|
|
Returns
|
|
-------
|
|
A MaskedArray.
|
|
|
|
Raises
|
|
------
|
|
A ValueError if there are no values within the given limits.
|
|
|
|
"""
|
|
lower_limit, upper_limit = limits
|
|
lower_include, upper_include = inclusive
|
|
am = ma.MaskedArray(a)
|
|
if lower_limit is not None:
|
|
if lower_include:
|
|
am = ma.masked_less(am, lower_limit)
|
|
else:
|
|
am = ma.masked_less_equal(am, lower_limit)
|
|
|
|
if upper_limit is not None:
|
|
if upper_include:
|
|
am = ma.masked_greater(am, upper_limit)
|
|
else:
|
|
am = ma.masked_greater_equal(am, upper_limit)
|
|
|
|
if am.count() == 0:
|
|
raise ValueError("No array values within given limits")
|
|
|
|
return am
|
|
|
|
|
|
def tmean(a, limits=None, inclusive=(True, True), axis=None):
|
|
"""
|
|
Compute the trimmed mean.
|
|
|
|
This function finds the arithmetic mean of given values, ignoring values
|
|
outside the given `limits`.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of values.
|
|
limits : None or (lower limit, upper limit), optional
|
|
Values in the input array less than the lower limit or greater than the
|
|
upper limit will be ignored. When limits is None (default), then all
|
|
values are used. Either of the limit values in the tuple can also be
|
|
None representing a half-open interval.
|
|
inclusive : (bool, bool), optional
|
|
A tuple consisting of the (lower flag, upper flag). These flags
|
|
determine whether values exactly equal to the lower or upper limits
|
|
are included. The default value is (True, True).
|
|
axis : int or None, optional
|
|
Axis along which to compute test. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
tmean : float
|
|
Trimmed mean.
|
|
|
|
See Also
|
|
--------
|
|
trim_mean : Returns mean after trimming a proportion from both tails.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.tmean(x)
|
|
9.5
|
|
>>> stats.tmean(x, (3,17))
|
|
10.0
|
|
|
|
"""
|
|
a = asarray(a)
|
|
if limits is None:
|
|
return np.mean(a, None)
|
|
|
|
am = _mask_to_limits(a.ravel(), limits, inclusive)
|
|
return am.mean(axis=axis)
|
|
|
|
|
|
def tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
|
|
"""
|
|
Compute the trimmed variance.
|
|
|
|
This function computes the sample variance of an array of values,
|
|
while ignoring values which are outside of given `limits`.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of values.
|
|
limits : None or (lower limit, upper limit), optional
|
|
Values in the input array less than the lower limit or greater than the
|
|
upper limit will be ignored. When limits is None, then all values are
|
|
used. Either of the limit values in the tuple can also be None
|
|
representing a half-open interval. The default value is None.
|
|
inclusive : (bool, bool), optional
|
|
A tuple consisting of the (lower flag, upper flag). These flags
|
|
determine whether values exactly equal to the lower or upper limits
|
|
are included. The default value is (True, True).
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over the
|
|
whole array `a`.
|
|
ddof : int, optional
|
|
Delta degrees of freedom. Default is 1.
|
|
|
|
Returns
|
|
-------
|
|
tvar : float
|
|
Trimmed variance.
|
|
|
|
Notes
|
|
-----
|
|
`tvar` computes the unbiased sample variance, i.e. it uses a correction
|
|
factor ``n / (n - 1)``.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.tvar(x)
|
|
35.0
|
|
>>> stats.tvar(x, (3,17))
|
|
20.0
|
|
|
|
"""
|
|
a = asarray(a)
|
|
a = a.astype(float)
|
|
if limits is None:
|
|
return a.var(ddof=ddof, axis=axis)
|
|
am = _mask_to_limits(a, limits, inclusive)
|
|
amnan = am.filled(fill_value=np.nan)
|
|
return np.nanvar(amnan, ddof=ddof, axis=axis)
|
|
|
|
|
|
def tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate'):
|
|
"""
|
|
Compute the trimmed minimum.
|
|
|
|
This function finds the miminum value of an array `a` along the
|
|
specified axis, but only considering values greater than a specified
|
|
lower limit.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of values.
|
|
lowerlimit : None or float, optional
|
|
Values in the input array less than the given limit will be ignored.
|
|
When lowerlimit is None, then all values are used. The default value
|
|
is None.
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over the
|
|
whole array `a`.
|
|
inclusive : {True, False}, optional
|
|
This flag determines whether values exactly equal to the lower limit
|
|
are included. The default value is True.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
tmin : float, int or ndarray
|
|
Trimmed minimum.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.tmin(x)
|
|
0
|
|
|
|
>>> stats.tmin(x, 13)
|
|
13
|
|
|
|
>>> stats.tmin(x, 13, inclusive=False)
|
|
14
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
am = _mask_to_limits(a, (lowerlimit, None), (inclusive, False))
|
|
|
|
contains_nan, nan_policy = _contains_nan(am, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
am = ma.masked_invalid(am)
|
|
|
|
res = ma.minimum.reduce(am, axis).data
|
|
if res.ndim == 0:
|
|
return res[()]
|
|
return res
|
|
|
|
|
|
def tmax(a, upperlimit=None, axis=0, inclusive=True, nan_policy='propagate'):
|
|
"""
|
|
Compute the trimmed maximum.
|
|
|
|
This function computes the maximum value of an array along a given axis,
|
|
while ignoring values larger than a specified upper limit.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of values.
|
|
upperlimit : None or float, optional
|
|
Values in the input array greater than the given limit will be ignored.
|
|
When upperlimit is None, then all values are used. The default value
|
|
is None.
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over the
|
|
whole array `a`.
|
|
inclusive : {True, False}, optional
|
|
This flag determines whether values exactly equal to the upper limit
|
|
are included. The default value is True.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
tmax : float, int or ndarray
|
|
Trimmed maximum.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.tmax(x)
|
|
19
|
|
|
|
>>> stats.tmax(x, 13)
|
|
13
|
|
|
|
>>> stats.tmax(x, 13, inclusive=False)
|
|
12
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
am = _mask_to_limits(a, (None, upperlimit), (False, inclusive))
|
|
|
|
contains_nan, nan_policy = _contains_nan(am, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
am = ma.masked_invalid(am)
|
|
|
|
res = ma.maximum.reduce(am, axis).data
|
|
if res.ndim == 0:
|
|
return res[()]
|
|
return res
|
|
|
|
|
|
def tstd(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
|
|
"""
|
|
Compute the trimmed sample standard deviation.
|
|
|
|
This function finds the sample standard deviation of given values,
|
|
ignoring values outside the given `limits`.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of values.
|
|
limits : None or (lower limit, upper limit), optional
|
|
Values in the input array less than the lower limit or greater than the
|
|
upper limit will be ignored. When limits is None, then all values are
|
|
used. Either of the limit values in the tuple can also be None
|
|
representing a half-open interval. The default value is None.
|
|
inclusive : (bool, bool), optional
|
|
A tuple consisting of the (lower flag, upper flag). These flags
|
|
determine whether values exactly equal to the lower or upper limits
|
|
are included. The default value is (True, True).
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over the
|
|
whole array `a`.
|
|
ddof : int, optional
|
|
Delta degrees of freedom. Default is 1.
|
|
|
|
Returns
|
|
-------
|
|
tstd : float
|
|
Trimmed sample standard deviation.
|
|
|
|
Notes
|
|
-----
|
|
`tstd` computes the unbiased sample standard deviation, i.e. it uses a
|
|
correction factor ``n / (n - 1)``.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.tstd(x)
|
|
5.9160797830996161
|
|
>>> stats.tstd(x, (3,17))
|
|
4.4721359549995796
|
|
|
|
"""
|
|
return np.sqrt(tvar(a, limits, inclusive, axis, ddof))
|
|
|
|
|
|
def tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
|
|
"""
|
|
Compute the trimmed standard error of the mean.
|
|
|
|
This function finds the standard error of the mean for given
|
|
values, ignoring values outside the given `limits`.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of values.
|
|
limits : None or (lower limit, upper limit), optional
|
|
Values in the input array less than the lower limit or greater than the
|
|
upper limit will be ignored. When limits is None, then all values are
|
|
used. Either of the limit values in the tuple can also be None
|
|
representing a half-open interval. The default value is None.
|
|
inclusive : (bool, bool), optional
|
|
A tuple consisting of the (lower flag, upper flag). These flags
|
|
determine whether values exactly equal to the lower or upper limits
|
|
are included. The default value is (True, True).
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over the
|
|
whole array `a`.
|
|
ddof : int, optional
|
|
Delta degrees of freedom. Default is 1.
|
|
|
|
Returns
|
|
-------
|
|
tsem : float
|
|
Trimmed standard error of the mean.
|
|
|
|
Notes
|
|
-----
|
|
`tsem` uses unbiased sample standard deviation, i.e. it uses a
|
|
correction factor ``n / (n - 1)``.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.tsem(x)
|
|
1.3228756555322954
|
|
>>> stats.tsem(x, (3,17))
|
|
1.1547005383792515
|
|
|
|
"""
|
|
a = np.asarray(a).ravel()
|
|
if limits is None:
|
|
return a.std(ddof=ddof) / np.sqrt(a.size)
|
|
|
|
am = _mask_to_limits(a, limits, inclusive)
|
|
sd = np.sqrt(np.ma.var(am, ddof=ddof, axis=axis))
|
|
return sd / np.sqrt(am.count())
|
|
|
|
|
|
#####################################
|
|
# MOMENTS #
|
|
#####################################
|
|
|
|
def moment(a, moment=1, axis=0, nan_policy='propagate'):
|
|
r"""
|
|
Calculate the nth moment about the mean for a sample.
|
|
|
|
A moment is a specific quantitative measure of the shape of a set of
|
|
points. It is often used to calculate coefficients of skewness and kurtosis
|
|
due to its close relationship with them.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
moment : int or array_like of ints, optional
|
|
Order of central moment that is returned. Default is 1.
|
|
axis : int or None, optional
|
|
Axis along which the central moment is computed. Default is 0.
|
|
If None, compute over the whole array `a`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
n-th central moment : ndarray or float
|
|
The appropriate moment along the given axis or over all values if axis
|
|
is None. The denominator for the moment calculation is the number of
|
|
observations, no degrees of freedom correction is done.
|
|
|
|
See Also
|
|
--------
|
|
kurtosis, skew, describe
|
|
|
|
Notes
|
|
-----
|
|
The k-th central moment of a data sample is:
|
|
|
|
.. math::
|
|
|
|
m_k = \frac{1}{n} \sum_{i = 1}^n (x_i - \bar{x})^k
|
|
|
|
Where n is the number of samples and x-bar is the mean. This function uses
|
|
exponentiation by squares [1]_ for efficiency.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://eli.thegreenplace.net/2009/03/21/efficient-integer-exponentiation-algorithms
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import moment
|
|
>>> moment([1, 2, 3, 4, 5], moment=1)
|
|
0.0
|
|
>>> moment([1, 2, 3, 4, 5], moment=2)
|
|
2.0
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.moment(a, moment, axis)
|
|
|
|
if a.size == 0:
|
|
# empty array, return nan(s) with shape matching `moment`
|
|
if np.isscalar(moment):
|
|
return np.nan
|
|
else:
|
|
return np.full(np.asarray(moment).shape, np.nan, dtype=np.float64)
|
|
|
|
# for array_like moment input, return a value for each.
|
|
if not np.isscalar(moment):
|
|
mmnt = [_moment(a, i, axis) for i in moment]
|
|
return np.array(mmnt)
|
|
else:
|
|
return _moment(a, moment, axis)
|
|
|
|
|
|
def _moment(a, moment, axis):
|
|
if np.abs(moment - np.round(moment)) > 0:
|
|
raise ValueError("All moment parameters must be integers")
|
|
|
|
if moment == 0:
|
|
# When moment equals 0, the result is 1, by definition.
|
|
shape = list(a.shape)
|
|
del shape[axis]
|
|
if shape:
|
|
# return an actual array of the appropriate shape
|
|
return np.ones(shape, dtype=float)
|
|
else:
|
|
# the input was 1D, so return a scalar instead of a rank-0 array
|
|
return 1.0
|
|
|
|
elif moment == 1:
|
|
# By definition the first moment about the mean is 0.
|
|
shape = list(a.shape)
|
|
del shape[axis]
|
|
if shape:
|
|
# return an actual array of the appropriate shape
|
|
return np.zeros(shape, dtype=float)
|
|
else:
|
|
# the input was 1D, so return a scalar instead of a rank-0 array
|
|
return np.float64(0.0)
|
|
else:
|
|
# Exponentiation by squares: form exponent sequence
|
|
n_list = [moment]
|
|
current_n = moment
|
|
while current_n > 2:
|
|
if current_n % 2:
|
|
current_n = (current_n - 1) / 2
|
|
else:
|
|
current_n /= 2
|
|
n_list.append(current_n)
|
|
|
|
# Starting point for exponentiation by squares
|
|
a_zero_mean = a - np.expand_dims(np.mean(a, axis), axis)
|
|
if n_list[-1] == 1:
|
|
s = a_zero_mean.copy()
|
|
else:
|
|
s = a_zero_mean**2
|
|
|
|
# Perform multiplications
|
|
for n in n_list[-2::-1]:
|
|
s = s**2
|
|
if n % 2:
|
|
s *= a_zero_mean
|
|
return np.mean(s, axis)
|
|
|
|
|
|
def variation(a, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Compute the coefficient of variation.
|
|
|
|
The coefficient of variation is the ratio of the biased standard
|
|
deviation to the mean.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
axis : int or None, optional
|
|
Axis along which to calculate the coefficient of variation. Default
|
|
is 0. If None, compute over the whole array `a`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
variation : ndarray
|
|
The calculated variation along the requested axis.
|
|
|
|
References
|
|
----------
|
|
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
|
|
Probability and Statistics Tables and Formulae. Chapman & Hall: New
|
|
York. 2000.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import variation
|
|
>>> variation([1, 2, 3, 4, 5])
|
|
0.47140452079103173
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.variation(a, axis)
|
|
|
|
return a.std(axis) / a.mean(axis)
|
|
|
|
|
|
def skew(a, axis=0, bias=True, nan_policy='propagate'):
|
|
r"""
|
|
Compute the sample skewness of a data set.
|
|
|
|
For normally distributed data, the skewness should be about zero. For
|
|
unimodal continuous distributions, a skewness value greater than zero means
|
|
that there is more weight in the right tail of the distribution. The
|
|
function `skewtest` can be used to determine if the skewness value
|
|
is close enough to zero, statistically speaking.
|
|
|
|
Parameters
|
|
----------
|
|
a : ndarray
|
|
Input array.
|
|
axis : int or None, optional
|
|
Axis along which skewness is calculated. Default is 0.
|
|
If None, compute over the whole array `a`.
|
|
bias : bool, optional
|
|
If False, then the calculations are corrected for statistical bias.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
skewness : ndarray
|
|
The skewness of values along an axis, returning 0 where all values are
|
|
equal.
|
|
|
|
Notes
|
|
-----
|
|
The sample skewness is computed as the Fisher-Pearson coefficient
|
|
of skewness, i.e.
|
|
|
|
.. math::
|
|
|
|
g_1=\frac{m_3}{m_2^{3/2}}
|
|
|
|
where
|
|
|
|
.. math::
|
|
|
|
m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i
|
|
|
|
is the biased sample :math:`i\texttt{th}` central moment, and :math:`\bar{x}` is
|
|
the sample mean. If ``bias`` is False, the calculations are
|
|
corrected for bias and the value computed is the adjusted
|
|
Fisher-Pearson standardized moment coefficient, i.e.
|
|
|
|
.. math::
|
|
|
|
G_1=\frac{k_3}{k_2^{3/2}}=
|
|
\frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}.
|
|
|
|
References
|
|
----------
|
|
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
|
|
Probability and Statistics Tables and Formulae. Chapman & Hall: New
|
|
York. 2000.
|
|
Section 2.2.24.1
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import skew
|
|
>>> skew([1, 2, 3, 4, 5])
|
|
0.0
|
|
>>> skew([2, 8, 0, 4, 1, 9, 9, 0])
|
|
0.2650554122698573
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
n = a.shape[axis]
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.skew(a, axis, bias)
|
|
|
|
m2 = moment(a, 2, axis)
|
|
m3 = moment(a, 3, axis)
|
|
zero = (m2 == 0)
|
|
vals = _lazywhere(~zero, (m2, m3),
|
|
lambda m2, m3: m3 / m2**1.5,
|
|
0.)
|
|
if not bias:
|
|
can_correct = (n > 2) & (m2 > 0)
|
|
if can_correct.any():
|
|
m2 = np.extract(can_correct, m2)
|
|
m3 = np.extract(can_correct, m3)
|
|
nval = np.sqrt((n - 1.0) * n) / (n - 2.0) * m3 / m2**1.5
|
|
np.place(vals, can_correct, nval)
|
|
|
|
if vals.ndim == 0:
|
|
return vals.item()
|
|
|
|
return vals
|
|
|
|
|
|
def kurtosis(a, axis=0, fisher=True, bias=True, nan_policy='propagate'):
|
|
"""
|
|
Compute the kurtosis (Fisher or Pearson) of a dataset.
|
|
|
|
Kurtosis is the fourth central moment divided by the square of the
|
|
variance. If Fisher's definition is used, then 3.0 is subtracted from
|
|
the result to give 0.0 for a normal distribution.
|
|
|
|
If bias is False then the kurtosis is calculated using k statistics to
|
|
eliminate bias coming from biased moment estimators
|
|
|
|
Use `kurtosistest` to see if result is close enough to normal.
|
|
|
|
Parameters
|
|
----------
|
|
a : array
|
|
Data for which the kurtosis is calculated.
|
|
axis : int or None, optional
|
|
Axis along which the kurtosis is calculated. Default is 0.
|
|
If None, compute over the whole array `a`.
|
|
fisher : bool, optional
|
|
If True, Fisher's definition is used (normal ==> 0.0). If False,
|
|
Pearson's definition is used (normal ==> 3.0).
|
|
bias : bool, optional
|
|
If False, then the calculations are corrected for statistical bias.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan. 'propagate' returns nan,
|
|
'raise' throws an error, 'omit' performs the calculations ignoring nan
|
|
values. Default is 'propagate'.
|
|
|
|
Returns
|
|
-------
|
|
kurtosis : array
|
|
The kurtosis of values along an axis. If all values are equal,
|
|
return -3 for Fisher's definition and 0 for Pearson's definition.
|
|
|
|
References
|
|
----------
|
|
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
|
|
Probability and Statistics Tables and Formulae. Chapman & Hall: New
|
|
York. 2000.
|
|
|
|
Examples
|
|
--------
|
|
In Fisher's definiton, the kurtosis of the normal distribution is zero.
|
|
In the following example, the kurtosis is close to zero, because it was
|
|
calculated from the dataset, not from the continuous distribution.
|
|
|
|
>>> from scipy.stats import norm, kurtosis
|
|
>>> data = norm.rvs(size=1000, random_state=3)
|
|
>>> kurtosis(data)
|
|
-0.06928694200380558
|
|
|
|
The distribution with a higher kurtosis has a heavier tail.
|
|
The zero valued kurtosis of the normal distribution in Fisher's definition
|
|
can serve as a reference point.
|
|
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> import scipy.stats as stats
|
|
>>> from scipy.stats import kurtosis
|
|
|
|
>>> x = np.linspace(-5, 5, 100)
|
|
>>> ax = plt.subplot()
|
|
>>> distnames = ['laplace', 'norm', 'uniform']
|
|
|
|
>>> for distname in distnames:
|
|
... if distname == 'uniform':
|
|
... dist = getattr(stats, distname)(loc=-2, scale=4)
|
|
... else:
|
|
... dist = getattr(stats, distname)
|
|
... data = dist.rvs(size=1000)
|
|
... kur = kurtosis(data, fisher=True)
|
|
... y = dist.pdf(x)
|
|
... ax.plot(x, y, label="{}, {}".format(distname, round(kur, 3)))
|
|
... ax.legend()
|
|
|
|
The Laplace distribution has a heavier tail than the normal distribution.
|
|
The uniform distribution (which has negative kurtosis) has the thinnest
|
|
tail.
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.kurtosis(a, axis, fisher, bias)
|
|
|
|
n = a.shape[axis]
|
|
m2 = moment(a, 2, axis)
|
|
m4 = moment(a, 4, axis)
|
|
zero = (m2 == 0)
|
|
with np.errstate(all='ignore'):
|
|
vals = np.where(zero, 0, m4 / m2**2.0)
|
|
|
|
if not bias:
|
|
can_correct = (n > 3) & (m2 > 0)
|
|
if can_correct.any():
|
|
m2 = np.extract(can_correct, m2)
|
|
m4 = np.extract(can_correct, m4)
|
|
nval = 1.0/(n-2)/(n-3) * ((n**2-1.0)*m4/m2**2.0 - 3*(n-1)**2.0)
|
|
np.place(vals, can_correct, nval + 3.0)
|
|
|
|
if vals.ndim == 0:
|
|
vals = vals.item() # array scalar
|
|
|
|
return vals - 3 if fisher else vals
|
|
|
|
|
|
DescribeResult = namedtuple('DescribeResult',
|
|
('nobs', 'minmax', 'mean', 'variance', 'skewness',
|
|
'kurtosis'))
|
|
|
|
|
|
def describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate'):
|
|
"""
|
|
Compute several descriptive statistics of the passed array.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input data.
|
|
axis : int or None, optional
|
|
Axis along which statistics are calculated. Default is 0.
|
|
If None, compute over the whole array `a`.
|
|
ddof : int, optional
|
|
Delta degrees of freedom (only for variance). Default is 1.
|
|
bias : bool, optional
|
|
If False, then the skewness and kurtosis calculations are corrected for
|
|
statistical bias.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
nobs : int or ndarray of ints
|
|
Number of observations (length of data along `axis`).
|
|
When 'omit' is chosen as nan_policy, each column is counted separately.
|
|
minmax: tuple of ndarrays or floats
|
|
Minimum and maximum value of data array.
|
|
mean : ndarray or float
|
|
Arithmetic mean of data along axis.
|
|
variance : ndarray or float
|
|
Unbiased variance of the data along axis, denominator is number of
|
|
observations minus one.
|
|
skewness : ndarray or float
|
|
Skewness, based on moment calculations with denominator equal to
|
|
the number of observations, i.e. no degrees of freedom correction.
|
|
kurtosis : ndarray or float
|
|
Kurtosis (Fisher). The kurtosis is normalized so that it is
|
|
zero for the normal distribution. No degrees of freedom are used.
|
|
|
|
See Also
|
|
--------
|
|
skew, kurtosis
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> a = np.arange(10)
|
|
>>> stats.describe(a)
|
|
DescribeResult(nobs=10, minmax=(0, 9), mean=4.5, variance=9.166666666666666,
|
|
skewness=0.0, kurtosis=-1.2242424242424244)
|
|
>>> b = [[1, 2], [3, 4]]
|
|
>>> stats.describe(b)
|
|
DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
|
|
mean=array([2., 3.]), variance=array([2., 2.]),
|
|
skewness=array([0., 0.]), kurtosis=array([-2., -2.]))
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.describe(a, axis, ddof, bias)
|
|
|
|
if a.size == 0:
|
|
raise ValueError("The input must not be empty.")
|
|
n = a.shape[axis]
|
|
mm = (np.min(a, axis=axis), np.max(a, axis=axis))
|
|
m = np.mean(a, axis=axis)
|
|
v = np.var(a, axis=axis, ddof=ddof)
|
|
sk = skew(a, axis, bias=bias)
|
|
kurt = kurtosis(a, axis, bias=bias)
|
|
|
|
return DescribeResult(n, mm, m, v, sk, kurt)
|
|
|
|
#####################################
|
|
# NORMALITY TESTS #
|
|
#####################################
|
|
|
|
|
|
SkewtestResult = namedtuple('SkewtestResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def skewtest(a, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Test whether the skew is different from the normal distribution.
|
|
|
|
This function tests the null hypothesis that the skewness of
|
|
the population that the sample was drawn from is the same
|
|
as that of a corresponding normal distribution.
|
|
|
|
Parameters
|
|
----------
|
|
a : array
|
|
The data to be tested.
|
|
axis : int or None, optional
|
|
Axis along which statistics are calculated. Default is 0.
|
|
If None, compute over the whole array `a`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The computed z-score for this test.
|
|
pvalue : float
|
|
Two-sided p-value for the hypothesis test.
|
|
|
|
Notes
|
|
-----
|
|
The sample size must be at least 8.
|
|
|
|
References
|
|
----------
|
|
.. [1] R. B. D'Agostino, A. J. Belanger and R. B. D'Agostino Jr.,
|
|
"A suggestion for using powerful and informative tests of
|
|
normality", American Statistician 44, pp. 316-321, 1990.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import skewtest
|
|
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8])
|
|
SkewtestResult(statistic=1.0108048609177787, pvalue=0.3121098361421897)
|
|
>>> skewtest([2, 8, 0, 4, 1, 9, 9, 0])
|
|
SkewtestResult(statistic=0.44626385374196975, pvalue=0.6554066631275459)
|
|
>>> skewtest([1, 2, 3, 4, 5, 6, 7, 8000])
|
|
SkewtestResult(statistic=3.571773510360407, pvalue=0.0003545719905823133)
|
|
>>> skewtest([100, 100, 100, 100, 100, 100, 100, 101])
|
|
SkewtestResult(statistic=3.5717766638478072, pvalue=0.000354567720281634)
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.skewtest(a, axis)
|
|
|
|
if axis is None:
|
|
a = np.ravel(a)
|
|
axis = 0
|
|
b2 = skew(a, axis)
|
|
n = a.shape[axis]
|
|
if n < 8:
|
|
raise ValueError(
|
|
"skewtest is not valid with less than 8 samples; %i samples"
|
|
" were given." % int(n))
|
|
y = b2 * math.sqrt(((n + 1) * (n + 3)) / (6.0 * (n - 2)))
|
|
beta2 = (3.0 * (n**2 + 27*n - 70) * (n+1) * (n+3) /
|
|
((n-2.0) * (n+5) * (n+7) * (n+9)))
|
|
W2 = -1 + math.sqrt(2 * (beta2 - 1))
|
|
delta = 1 / math.sqrt(0.5 * math.log(W2))
|
|
alpha = math.sqrt(2.0 / (W2 - 1))
|
|
y = np.where(y == 0, 1, y)
|
|
Z = delta * np.log(y / alpha + np.sqrt((y / alpha)**2 + 1))
|
|
|
|
return SkewtestResult(Z, 2 * distributions.norm.sf(np.abs(Z)))
|
|
|
|
|
|
KurtosistestResult = namedtuple('KurtosistestResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def kurtosistest(a, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Test whether a dataset has normal kurtosis.
|
|
|
|
This function tests the null hypothesis that the kurtosis
|
|
of the population from which the sample was drawn is that
|
|
of the normal distribution: ``kurtosis = 3(n-1)/(n+1)``.
|
|
|
|
Parameters
|
|
----------
|
|
a : array
|
|
Array of the sample data.
|
|
axis : int or None, optional
|
|
Axis along which to compute test. Default is 0. If None,
|
|
compute over the whole array `a`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The computed z-score for this test.
|
|
pvalue : float
|
|
The two-sided p-value for the hypothesis test.
|
|
|
|
Notes
|
|
-----
|
|
Valid only for n>20. This function uses the method described in [1]_.
|
|
|
|
References
|
|
----------
|
|
.. [1] see e.g. F. J. Anscombe, W. J. Glynn, "Distribution of the kurtosis
|
|
statistic b2 for normal samples", Biometrika, vol. 70, pp. 227-234, 1983.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import kurtosistest
|
|
>>> kurtosistest(list(range(20)))
|
|
KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.08804338332528348)
|
|
|
|
>>> np.random.seed(28041990)
|
|
>>> s = np.random.normal(0, 1, 1000)
|
|
>>> kurtosistest(s)
|
|
KurtosistestResult(statistic=1.2317590987707365, pvalue=0.21803908613450895)
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.kurtosistest(a, axis)
|
|
|
|
n = a.shape[axis]
|
|
if n < 5:
|
|
raise ValueError(
|
|
"kurtosistest requires at least 5 observations; %i observations"
|
|
" were given." % int(n))
|
|
if n < 20:
|
|
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
|
|
"anyway, n=%i" % int(n))
|
|
b2 = kurtosis(a, axis, fisher=False)
|
|
|
|
E = 3.0*(n-1) / (n+1)
|
|
varb2 = 24.0*n*(n-2)*(n-3) / ((n+1)*(n+1.)*(n+3)*(n+5)) # [1]_ Eq. 1
|
|
x = (b2-E) / np.sqrt(varb2) # [1]_ Eq. 4
|
|
# [1]_ Eq. 2:
|
|
sqrtbeta1 = 6.0*(n*n-5*n+2)/((n+7)*(n+9)) * np.sqrt((6.0*(n+3)*(n+5)) /
|
|
(n*(n-2)*(n-3)))
|
|
# [1]_ Eq. 3:
|
|
A = 6.0 + 8.0/sqrtbeta1 * (2.0/sqrtbeta1 + np.sqrt(1+4.0/(sqrtbeta1**2)))
|
|
term1 = 1 - 2/(9.0*A)
|
|
denom = 1 + x*np.sqrt(2/(A-4.0))
|
|
term2 = np.sign(denom) * np.where(denom == 0.0, np.nan,
|
|
np.power((1-2.0/A)/np.abs(denom), 1/3.0))
|
|
if np.any(denom == 0):
|
|
msg = "Test statistic not defined in some cases due to division by " \
|
|
"zero. Return nan in that case..."
|
|
warnings.warn(msg, RuntimeWarning)
|
|
|
|
Z = (term1 - term2) / np.sqrt(2/(9.0*A)) # [1]_ Eq. 5
|
|
if Z.ndim == 0:
|
|
Z = Z[()]
|
|
|
|
# zprob uses upper tail, so Z needs to be positive
|
|
return KurtosistestResult(Z, 2 * distributions.norm.sf(np.abs(Z)))
|
|
|
|
|
|
NormaltestResult = namedtuple('NormaltestResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def normaltest(a, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Test whether a sample differs from a normal distribution.
|
|
|
|
This function tests the null hypothesis that a sample comes
|
|
from a normal distribution. It is based on D'Agostino and
|
|
Pearson's [1]_, [2]_ test that combines skew and kurtosis to
|
|
produce an omnibus test of normality.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
The array containing the sample to be tested.
|
|
axis : int or None, optional
|
|
Axis along which to compute test. Default is 0. If None,
|
|
compute over the whole array `a`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float or array
|
|
``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
|
|
``k`` is the z-score returned by `kurtosistest`.
|
|
pvalue : float or array
|
|
A 2-sided chi squared probability for the hypothesis test.
|
|
|
|
References
|
|
----------
|
|
.. [1] D'Agostino, R. B. (1971), "An omnibus test of normality for
|
|
moderate and large sample size", Biometrika, 58, 341-348
|
|
|
|
.. [2] D'Agostino, R. and Pearson, E. S. (1973), "Tests for departure from
|
|
normality", Biometrika, 60, 613-622
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> pts = 1000
|
|
>>> np.random.seed(28041990)
|
|
>>> a = np.random.normal(0, 1, size=pts)
|
|
>>> b = np.random.normal(2, 1, size=pts)
|
|
>>> x = np.concatenate((a, b))
|
|
>>> k2, p = stats.normaltest(x)
|
|
>>> alpha = 1e-3
|
|
>>> print("p = {:g}".format(p))
|
|
p = 3.27207e-11
|
|
>>> if p < alpha: # null hypothesis: x comes from a normal distribution
|
|
... print("The null hypothesis can be rejected")
|
|
... else:
|
|
... print("The null hypothesis cannot be rejected")
|
|
The null hypothesis can be rejected
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.normaltest(a, axis)
|
|
|
|
s, _ = skewtest(a, axis)
|
|
k, _ = kurtosistest(a, axis)
|
|
k2 = s*s + k*k
|
|
|
|
return NormaltestResult(k2, distributions.chi2.sf(k2, 2))
|
|
|
|
|
|
Jarque_beraResult = namedtuple('Jarque_beraResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def jarque_bera(x):
|
|
"""
|
|
Perform the Jarque-Bera goodness of fit test on sample data.
|
|
|
|
The Jarque-Bera test tests whether the sample data has the skewness and
|
|
kurtosis matching a normal distribution.
|
|
|
|
Note that this test only works for a large enough number of data samples
|
|
(>2000) as the test statistic asymptotically has a Chi-squared distribution
|
|
with 2 degrees of freedom.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
Observations of a random variable.
|
|
|
|
Returns
|
|
-------
|
|
jb_value : float
|
|
The test statistic.
|
|
p : float
|
|
The p-value for the hypothesis test.
|
|
|
|
References
|
|
----------
|
|
.. [1] Jarque, C. and Bera, A. (1980) "Efficient tests for normality,
|
|
homoscedasticity and serial independence of regression residuals",
|
|
6 Econometric Letters 255-259.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> np.random.seed(987654321)
|
|
>>> x = np.random.normal(0, 1, 100000)
|
|
>>> jarque_bera_test = stats.jarque_bera(x)
|
|
>>> jarque_bera_test
|
|
Jarque_beraResult(statistic=4.716570798957913, pvalue=0.0945822550304295)
|
|
>>> jarque_bera_test.statistic
|
|
4.716570798957913
|
|
>>> jarque_bera_test.pvalue
|
|
0.0945822550304295
|
|
|
|
"""
|
|
x = np.asarray(x)
|
|
n = x.size
|
|
if n == 0:
|
|
raise ValueError('At least one observation is required.')
|
|
|
|
mu = x.mean()
|
|
diffx = x - mu
|
|
skewness = (1 / n * np.sum(diffx**3)) / (1 / n * np.sum(diffx**2))**(3 / 2.)
|
|
kurtosis = (1 / n * np.sum(diffx**4)) / (1 / n * np.sum(diffx**2))**2
|
|
jb_value = n / 6 * (skewness**2 + (kurtosis - 3)**2 / 4)
|
|
p = 1 - distributions.chi2.cdf(jb_value, 2)
|
|
|
|
return Jarque_beraResult(jb_value, p)
|
|
|
|
|
|
#####################################
|
|
# FREQUENCY FUNCTIONS #
|
|
#####################################
|
|
|
|
# deindent to work around numpy/gh-16202
|
|
@np.deprecate(
|
|
message="`itemfreq` is deprecated and will be removed in a "
|
|
"future version. Use instead `np.unique(..., return_counts=True)`")
|
|
def itemfreq(a):
|
|
"""
|
|
Return a 2-D array of item frequencies.
|
|
|
|
Parameters
|
|
----------
|
|
a : (N,) array_like
|
|
Input array.
|
|
|
|
Returns
|
|
-------
|
|
itemfreq : (K, 2) ndarray
|
|
A 2-D frequency table. Column 1 contains sorted, unique values from
|
|
`a`, column 2 contains their respective counts.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> a = np.array([1, 1, 5, 0, 1, 2, 2, 0, 1, 4])
|
|
>>> stats.itemfreq(a)
|
|
array([[ 0., 2.],
|
|
[ 1., 4.],
|
|
[ 2., 2.],
|
|
[ 4., 1.],
|
|
[ 5., 1.]])
|
|
>>> np.bincount(a)
|
|
array([2, 4, 2, 0, 1, 1])
|
|
|
|
>>> stats.itemfreq(a/10.)
|
|
array([[ 0. , 2. ],
|
|
[ 0.1, 4. ],
|
|
[ 0.2, 2. ],
|
|
[ 0.4, 1. ],
|
|
[ 0.5, 1. ]])
|
|
"""
|
|
items, inv = np.unique(a, return_inverse=True)
|
|
freq = np.bincount(inv)
|
|
return np.array([items, freq]).T
|
|
|
|
|
|
def scoreatpercentile(a, per, limit=(), interpolation_method='fraction',
|
|
axis=None):
|
|
"""
|
|
Calculate the score at a given percentile of the input sequence.
|
|
|
|
For example, the score at `per=50` is the median. If the desired quantile
|
|
lies between two data points, we interpolate between them, according to
|
|
the value of `interpolation`. If the parameter `limit` is provided, it
|
|
should be a tuple (lower, upper) of two values.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
A 1-D array of values from which to extract score.
|
|
per : array_like
|
|
Percentile(s) at which to extract score. Values should be in range
|
|
[0,100].
|
|
limit : tuple, optional
|
|
Tuple of two scalars, the lower and upper limits within which to
|
|
compute the percentile. Values of `a` outside
|
|
this (closed) interval will be ignored.
|
|
interpolation_method : {'fraction', 'lower', 'higher'}, optional
|
|
Specifies the interpolation method to use,
|
|
when the desired quantile lies between two data points `i` and `j`
|
|
The following options are available (default is 'fraction'):
|
|
|
|
* 'fraction': ``i + (j - i) * fraction`` where ``fraction`` is the
|
|
fractional part of the index surrounded by ``i`` and ``j``
|
|
* 'lower': ``i``
|
|
* 'higher': ``j``
|
|
|
|
axis : int, optional
|
|
Axis along which the percentiles are computed. Default is None. If
|
|
None, compute over the whole array `a`.
|
|
|
|
Returns
|
|
-------
|
|
score : float or ndarray
|
|
Score at percentile(s).
|
|
|
|
See Also
|
|
--------
|
|
percentileofscore, numpy.percentile
|
|
|
|
Notes
|
|
-----
|
|
This function will become obsolete in the future.
|
|
For NumPy 1.9 and higher, `numpy.percentile` provides all the functionality
|
|
that `scoreatpercentile` provides. And it's significantly faster.
|
|
Therefore it's recommended to use `numpy.percentile` for users that have
|
|
numpy >= 1.9.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> a = np.arange(100)
|
|
>>> stats.scoreatpercentile(a, 50)
|
|
49.5
|
|
|
|
"""
|
|
# adapted from NumPy's percentile function. When we require numpy >= 1.8,
|
|
# the implementation of this function can be replaced by np.percentile.
|
|
a = np.asarray(a)
|
|
if a.size == 0:
|
|
# empty array, return nan(s) with shape matching `per`
|
|
if np.isscalar(per):
|
|
return np.nan
|
|
else:
|
|
return np.full(np.asarray(per).shape, np.nan, dtype=np.float64)
|
|
|
|
if limit:
|
|
a = a[(limit[0] <= a) & (a <= limit[1])]
|
|
|
|
sorted_ = np.sort(a, axis=axis)
|
|
if axis is None:
|
|
axis = 0
|
|
|
|
return _compute_qth_percentile(sorted_, per, interpolation_method, axis)
|
|
|
|
|
|
# handle sequence of per's without calling sort multiple times
|
|
def _compute_qth_percentile(sorted_, per, interpolation_method, axis):
|
|
if not np.isscalar(per):
|
|
score = [_compute_qth_percentile(sorted_, i,
|
|
interpolation_method, axis)
|
|
for i in per]
|
|
return np.array(score)
|
|
|
|
if not (0 <= per <= 100):
|
|
raise ValueError("percentile must be in the range [0, 100]")
|
|
|
|
indexer = [slice(None)] * sorted_.ndim
|
|
idx = per / 100. * (sorted_.shape[axis] - 1)
|
|
|
|
if int(idx) != idx:
|
|
# round fractional indices according to interpolation method
|
|
if interpolation_method == 'lower':
|
|
idx = int(np.floor(idx))
|
|
elif interpolation_method == 'higher':
|
|
idx = int(np.ceil(idx))
|
|
elif interpolation_method == 'fraction':
|
|
pass # keep idx as fraction and interpolate
|
|
else:
|
|
raise ValueError("interpolation_method can only be 'fraction', "
|
|
"'lower' or 'higher'")
|
|
|
|
i = int(idx)
|
|
if i == idx:
|
|
indexer[axis] = slice(i, i + 1)
|
|
weights = array(1)
|
|
sumval = 1.0
|
|
else:
|
|
indexer[axis] = slice(i, i + 2)
|
|
j = i + 1
|
|
weights = array([(j - idx), (idx - i)], float)
|
|
wshape = [1] * sorted_.ndim
|
|
wshape[axis] = 2
|
|
weights.shape = wshape
|
|
sumval = weights.sum()
|
|
|
|
# Use np.add.reduce (== np.sum but a little faster) to coerce data type
|
|
return np.add.reduce(sorted_[tuple(indexer)] * weights, axis=axis) / sumval
|
|
|
|
|
|
def percentileofscore(a, score, kind='rank'):
|
|
"""
|
|
Compute the percentile rank of a score relative to a list of scores.
|
|
|
|
A `percentileofscore` of, for example, 80% means that 80% of the
|
|
scores in `a` are below the given score. In the case of gaps or
|
|
ties, the exact definition depends on the optional keyword, `kind`.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of scores to which `score` is compared.
|
|
score : int or float
|
|
Score that is compared to the elements in `a`.
|
|
kind : {'rank', 'weak', 'strict', 'mean'}, optional
|
|
Specifies the interpretation of the resulting score.
|
|
The following options are available (default is 'rank'):
|
|
|
|
* 'rank': Average percentage ranking of score. In case of multiple
|
|
matches, average the percentage rankings of all matching scores.
|
|
* 'weak': This kind corresponds to the definition of a cumulative
|
|
distribution function. A percentileofscore of 80% means that 80%
|
|
of values are less than or equal to the provided score.
|
|
* 'strict': Similar to "weak", except that only values that are
|
|
strictly less than the given score are counted.
|
|
* 'mean': The average of the "weak" and "strict" scores, often used
|
|
in testing. See https://en.wikipedia.org/wiki/Percentile_rank
|
|
|
|
Returns
|
|
-------
|
|
pcos : float
|
|
Percentile-position of score (0-100) relative to `a`.
|
|
|
|
See Also
|
|
--------
|
|
numpy.percentile
|
|
|
|
Examples
|
|
--------
|
|
Three-quarters of the given values lie below a given score:
|
|
|
|
>>> from scipy import stats
|
|
>>> stats.percentileofscore([1, 2, 3, 4], 3)
|
|
75.0
|
|
|
|
With multiple matches, note how the scores of the two matches, 0.6
|
|
and 0.8 respectively, are averaged:
|
|
|
|
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3)
|
|
70.0
|
|
|
|
Only 2/5 values are strictly less than 3:
|
|
|
|
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='strict')
|
|
40.0
|
|
|
|
But 4/5 values are less than or equal to 3:
|
|
|
|
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='weak')
|
|
80.0
|
|
|
|
The average between the weak and the strict scores is:
|
|
|
|
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='mean')
|
|
60.0
|
|
|
|
"""
|
|
if np.isnan(score):
|
|
return np.nan
|
|
a = np.asarray(a)
|
|
n = len(a)
|
|
if n == 0:
|
|
return 100.0
|
|
|
|
if kind == 'rank':
|
|
left = np.count_nonzero(a < score)
|
|
right = np.count_nonzero(a <= score)
|
|
pct = (right + left + (1 if right > left else 0)) * 50.0/n
|
|
return pct
|
|
elif kind == 'strict':
|
|
return np.count_nonzero(a < score) / n * 100
|
|
elif kind == 'weak':
|
|
return np.count_nonzero(a <= score) / n * 100
|
|
elif kind == 'mean':
|
|
pct = (np.count_nonzero(a < score) + np.count_nonzero(a <= score)) / n * 50
|
|
return pct
|
|
else:
|
|
raise ValueError("kind can only be 'rank', 'strict', 'weak' or 'mean'")
|
|
|
|
|
|
HistogramResult = namedtuple('HistogramResult',
|
|
('count', 'lowerlimit', 'binsize', 'extrapoints'))
|
|
|
|
|
|
def _histogram(a, numbins=10, defaultlimits=None, weights=None, printextras=False):
|
|
"""
|
|
Create a histogram.
|
|
|
|
Separate the range into several bins and return the number of instances
|
|
in each bin.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Array of scores which will be put into bins.
|
|
numbins : int, optional
|
|
The number of bins to use for the histogram. Default is 10.
|
|
defaultlimits : tuple (lower, upper), optional
|
|
The lower and upper values for the range of the histogram.
|
|
If no value is given, a range slightly larger than the range of the
|
|
values in a is used. Specifically ``(a.min() - s, a.max() + s)``,
|
|
where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
|
|
weights : array_like, optional
|
|
The weights for each value in `a`. Default is None, which gives each
|
|
value a weight of 1.0
|
|
printextras : bool, optional
|
|
If True, if there are extra points (i.e. the points that fall outside
|
|
the bin limits) a warning is raised saying how many of those points
|
|
there are. Default is False.
|
|
|
|
Returns
|
|
-------
|
|
count : ndarray
|
|
Number of points (or sum of weights) in each bin.
|
|
lowerlimit : float
|
|
Lowest value of histogram, the lower limit of the first bin.
|
|
binsize : float
|
|
The size of the bins (all bins have the same size).
|
|
extrapoints : int
|
|
The number of points outside the range of the histogram.
|
|
|
|
See Also
|
|
--------
|
|
numpy.histogram
|
|
|
|
Notes
|
|
-----
|
|
This histogram is based on numpy's histogram but has a larger range by
|
|
default if default limits is not set.
|
|
|
|
"""
|
|
a = np.ravel(a)
|
|
if defaultlimits is None:
|
|
if a.size == 0:
|
|
# handle empty arrays. Undetermined range, so use 0-1.
|
|
defaultlimits = (0, 1)
|
|
else:
|
|
# no range given, so use values in `a`
|
|
data_min = a.min()
|
|
data_max = a.max()
|
|
# Have bins extend past min and max values slightly
|
|
s = (data_max - data_min) / (2. * (numbins - 1.))
|
|
defaultlimits = (data_min - s, data_max + s)
|
|
|
|
# use numpy's histogram method to compute bins
|
|
hist, bin_edges = np.histogram(a, bins=numbins, range=defaultlimits,
|
|
weights=weights)
|
|
# hist are not always floats, convert to keep with old output
|
|
hist = np.array(hist, dtype=float)
|
|
# fixed width for bins is assumed, as numpy's histogram gives
|
|
# fixed width bins for int values for 'bins'
|
|
binsize = bin_edges[1] - bin_edges[0]
|
|
# calculate number of extra points
|
|
extrapoints = len([v for v in a
|
|
if defaultlimits[0] > v or v > defaultlimits[1]])
|
|
if extrapoints > 0 and printextras:
|
|
warnings.warn("Points outside given histogram range = %s"
|
|
% extrapoints)
|
|
|
|
return HistogramResult(hist, defaultlimits[0], binsize, extrapoints)
|
|
|
|
|
|
CumfreqResult = namedtuple('CumfreqResult',
|
|
('cumcount', 'lowerlimit', 'binsize',
|
|
'extrapoints'))
|
|
|
|
|
|
def cumfreq(a, numbins=10, defaultreallimits=None, weights=None):
|
|
"""
|
|
Return a cumulative frequency histogram, using the histogram function.
|
|
|
|
A cumulative histogram is a mapping that counts the cumulative number of
|
|
observations in all of the bins up to the specified bin.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
numbins : int, optional
|
|
The number of bins to use for the histogram. Default is 10.
|
|
defaultreallimits : tuple (lower, upper), optional
|
|
The lower and upper values for the range of the histogram.
|
|
If no value is given, a range slightly larger than the range of the
|
|
values in `a` is used. Specifically ``(a.min() - s, a.max() + s)``,
|
|
where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
|
|
weights : array_like, optional
|
|
The weights for each value in `a`. Default is None, which gives each
|
|
value a weight of 1.0
|
|
|
|
Returns
|
|
-------
|
|
cumcount : ndarray
|
|
Binned values of cumulative frequency.
|
|
lowerlimit : float
|
|
Lower real limit
|
|
binsize : float
|
|
Width of each bin.
|
|
extrapoints : int
|
|
Extra points.
|
|
|
|
Examples
|
|
--------
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> from scipy import stats
|
|
>>> x = [1, 4, 2, 1, 3, 1]
|
|
>>> res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5))
|
|
>>> res.cumcount
|
|
array([ 1., 2., 3., 3.])
|
|
>>> res.extrapoints
|
|
3
|
|
|
|
Create a normal distribution with 1000 random values
|
|
|
|
>>> rng = np.random.RandomState(seed=12345)
|
|
>>> samples = stats.norm.rvs(size=1000, random_state=rng)
|
|
|
|
Calculate cumulative frequencies
|
|
|
|
>>> res = stats.cumfreq(samples, numbins=25)
|
|
|
|
Calculate space of values for x
|
|
|
|
>>> x = res.lowerlimit + np.linspace(0, res.binsize*res.cumcount.size,
|
|
... res.cumcount.size)
|
|
|
|
Plot histogram and cumulative histogram
|
|
|
|
>>> fig = plt.figure(figsize=(10, 4))
|
|
>>> ax1 = fig.add_subplot(1, 2, 1)
|
|
>>> ax2 = fig.add_subplot(1, 2, 2)
|
|
>>> ax1.hist(samples, bins=25)
|
|
>>> ax1.set_title('Histogram')
|
|
>>> ax2.bar(x, res.cumcount, width=res.binsize)
|
|
>>> ax2.set_title('Cumulative histogram')
|
|
>>> ax2.set_xlim([x.min(), x.max()])
|
|
|
|
>>> plt.show()
|
|
|
|
"""
|
|
h, l, b, e = _histogram(a, numbins, defaultreallimits, weights=weights)
|
|
cumhist = np.cumsum(h * 1, axis=0)
|
|
return CumfreqResult(cumhist, l, b, e)
|
|
|
|
|
|
RelfreqResult = namedtuple('RelfreqResult',
|
|
('frequency', 'lowerlimit', 'binsize',
|
|
'extrapoints'))
|
|
|
|
|
|
def relfreq(a, numbins=10, defaultreallimits=None, weights=None):
|
|
"""
|
|
Return a relative frequency histogram, using the histogram function.
|
|
|
|
A relative frequency histogram is a mapping of the number of
|
|
observations in each of the bins relative to the total of observations.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
numbins : int, optional
|
|
The number of bins to use for the histogram. Default is 10.
|
|
defaultreallimits : tuple (lower, upper), optional
|
|
The lower and upper values for the range of the histogram.
|
|
If no value is given, a range slightly larger than the range of the
|
|
values in a is used. Specifically ``(a.min() - s, a.max() + s)``,
|
|
where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
|
|
weights : array_like, optional
|
|
The weights for each value in `a`. Default is None, which gives each
|
|
value a weight of 1.0
|
|
|
|
Returns
|
|
-------
|
|
frequency : ndarray
|
|
Binned values of relative frequency.
|
|
lowerlimit : float
|
|
Lower real limit.
|
|
binsize : float
|
|
Width of each bin.
|
|
extrapoints : int
|
|
Extra points.
|
|
|
|
Examples
|
|
--------
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> from scipy import stats
|
|
>>> a = np.array([2, 4, 1, 2, 3, 2])
|
|
>>> res = stats.relfreq(a, numbins=4)
|
|
>>> res.frequency
|
|
array([ 0.16666667, 0.5 , 0.16666667, 0.16666667])
|
|
>>> np.sum(res.frequency) # relative frequencies should add up to 1
|
|
1.0
|
|
|
|
Create a normal distribution with 1000 random values
|
|
|
|
>>> rng = np.random.RandomState(seed=12345)
|
|
>>> samples = stats.norm.rvs(size=1000, random_state=rng)
|
|
|
|
Calculate relative frequencies
|
|
|
|
>>> res = stats.relfreq(samples, numbins=25)
|
|
|
|
Calculate space of values for x
|
|
|
|
>>> x = res.lowerlimit + np.linspace(0, res.binsize*res.frequency.size,
|
|
... res.frequency.size)
|
|
|
|
Plot relative frequency histogram
|
|
|
|
>>> fig = plt.figure(figsize=(5, 4))
|
|
>>> ax = fig.add_subplot(1, 1, 1)
|
|
>>> ax.bar(x, res.frequency, width=res.binsize)
|
|
>>> ax.set_title('Relative frequency histogram')
|
|
>>> ax.set_xlim([x.min(), x.max()])
|
|
|
|
>>> plt.show()
|
|
|
|
"""
|
|
a = np.asanyarray(a)
|
|
h, l, b, e = _histogram(a, numbins, defaultreallimits, weights=weights)
|
|
h = h / a.shape[0]
|
|
|
|
return RelfreqResult(h, l, b, e)
|
|
|
|
|
|
#####################################
|
|
# VARIABILITY FUNCTIONS #
|
|
#####################################
|
|
|
|
def obrientransform(*args):
|
|
"""
|
|
Compute the O'Brien transform on input data (any number of arrays).
|
|
|
|
Used to test for homogeneity of variance prior to running one-way stats.
|
|
Each array in ``*args`` is one level of a factor.
|
|
If `f_oneway` is run on the transformed data and found significant,
|
|
the variances are unequal. From Maxwell and Delaney [1]_, p.112.
|
|
|
|
Parameters
|
|
----------
|
|
args : tuple of array_like
|
|
Any number of arrays.
|
|
|
|
Returns
|
|
-------
|
|
obrientransform : ndarray
|
|
Transformed data for use in an ANOVA. The first dimension
|
|
of the result corresponds to the sequence of transformed
|
|
arrays. If the arrays given are all 1-D of the same length,
|
|
the return value is a 2-D array; otherwise it is a 1-D array
|
|
of type object, with each element being an ndarray.
|
|
|
|
References
|
|
----------
|
|
.. [1] S. E. Maxwell and H. D. Delaney, "Designing Experiments and
|
|
Analyzing Data: A Model Comparison Perspective", Wadsworth, 1990.
|
|
|
|
Examples
|
|
--------
|
|
We'll test the following data sets for differences in their variance.
|
|
|
|
>>> x = [10, 11, 13, 9, 7, 12, 12, 9, 10]
|
|
>>> y = [13, 21, 5, 10, 8, 14, 10, 12, 7, 15]
|
|
|
|
Apply the O'Brien transform to the data.
|
|
|
|
>>> from scipy.stats import obrientransform
|
|
>>> tx, ty = obrientransform(x, y)
|
|
|
|
Use `scipy.stats.f_oneway` to apply a one-way ANOVA test to the
|
|
transformed data.
|
|
|
|
>>> from scipy.stats import f_oneway
|
|
>>> F, p = f_oneway(tx, ty)
|
|
>>> p
|
|
0.1314139477040335
|
|
|
|
If we require that ``p < 0.05`` for significance, we cannot conclude
|
|
that the variances are different.
|
|
|
|
"""
|
|
TINY = np.sqrt(np.finfo(float).eps)
|
|
|
|
# `arrays` will hold the transformed arguments.
|
|
arrays = []
|
|
sLast = None
|
|
|
|
for arg in args:
|
|
a = np.asarray(arg)
|
|
n = len(a)
|
|
mu = np.mean(a)
|
|
sq = (a - mu)**2
|
|
sumsq = sq.sum()
|
|
|
|
# The O'Brien transform.
|
|
t = ((n - 1.5) * n * sq - 0.5 * sumsq) / ((n - 1) * (n - 2))
|
|
|
|
# Check that the mean of the transformed data is equal to the
|
|
# original variance.
|
|
var = sumsq / (n - 1)
|
|
if abs(var - np.mean(t)) > TINY:
|
|
raise ValueError('Lack of convergence in obrientransform.')
|
|
|
|
arrays.append(t)
|
|
sLast = a.shape
|
|
|
|
if sLast:
|
|
for arr in arrays[:-1]:
|
|
if sLast != arr.shape:
|
|
return np.array(arrays, dtype=object)
|
|
return np.array(arrays)
|
|
|
|
|
|
def sem(a, axis=0, ddof=1, nan_policy='propagate'):
|
|
"""
|
|
Compute standard error of the mean.
|
|
|
|
Calculate the standard error of the mean (or standard error of
|
|
measurement) of the values in the input array.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
An array containing the values for which the standard error is
|
|
returned.
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over
|
|
the whole array `a`.
|
|
ddof : int, optional
|
|
Delta degrees-of-freedom. How many degrees of freedom to adjust
|
|
for bias in limited samples relative to the population estimate
|
|
of variance. Defaults to 1.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
s : ndarray or float
|
|
The standard error of the mean in the sample(s), along the input axis.
|
|
|
|
Notes
|
|
-----
|
|
The default value for `ddof` is different to the default (0) used by other
|
|
ddof containing routines, such as np.std and np.nanstd.
|
|
|
|
Examples
|
|
--------
|
|
Find standard error along the first axis:
|
|
|
|
>>> from scipy import stats
|
|
>>> a = np.arange(20).reshape(5,4)
|
|
>>> stats.sem(a)
|
|
array([ 2.8284, 2.8284, 2.8284, 2.8284])
|
|
|
|
Find standard error across the whole array, using n degrees of freedom:
|
|
|
|
>>> stats.sem(a, axis=None, ddof=0)
|
|
1.2893796958227628
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.sem(a, axis, ddof)
|
|
|
|
n = a.shape[axis]
|
|
s = np.std(a, axis=axis, ddof=ddof) / np.sqrt(n)
|
|
return s
|
|
|
|
|
|
def zscore(a, axis=0, ddof=0, nan_policy='propagate'):
|
|
"""
|
|
Compute the z score.
|
|
|
|
Compute the z score of each value in the sample, relative to the
|
|
sample mean and standard deviation.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
An array like object containing the sample data.
|
|
axis : int or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over
|
|
the whole array `a`.
|
|
ddof : int, optional
|
|
Degrees of freedom correction in the calculation of the
|
|
standard deviation. Default is 0.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan. 'propagate' returns nan,
|
|
'raise' throws an error, 'omit' performs the calculations ignoring nan
|
|
values. Default is 'propagate'.
|
|
|
|
Returns
|
|
-------
|
|
zscore : array_like
|
|
The z-scores, standardized by mean and standard deviation of
|
|
input array `a`.
|
|
|
|
Notes
|
|
-----
|
|
This function preserves ndarray subclasses, and works also with
|
|
matrices and masked arrays (it uses `asanyarray` instead of
|
|
`asarray` for parameters).
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([ 0.7972, 0.0767, 0.4383, 0.7866, 0.8091,
|
|
... 0.1954, 0.6307, 0.6599, 0.1065, 0.0508])
|
|
>>> from scipy import stats
|
|
>>> stats.zscore(a)
|
|
array([ 1.1273, -1.247 , -0.0552, 1.0923, 1.1664, -0.8559, 0.5786,
|
|
0.6748, -1.1488, -1.3324])
|
|
|
|
Computing along a specified axis, using n-1 degrees of freedom
|
|
(``ddof=1``) to calculate the standard deviation:
|
|
|
|
>>> b = np.array([[ 0.3148, 0.0478, 0.6243, 0.4608],
|
|
... [ 0.7149, 0.0775, 0.6072, 0.9656],
|
|
... [ 0.6341, 0.1403, 0.9759, 0.4064],
|
|
... [ 0.5918, 0.6948, 0.904 , 0.3721],
|
|
... [ 0.0921, 0.2481, 0.1188, 0.1366]])
|
|
>>> stats.zscore(b, axis=1, ddof=1)
|
|
array([[-0.19264823, -1.28415119, 1.07259584, 0.40420358],
|
|
[ 0.33048416, -1.37380874, 0.04251374, 1.00081084],
|
|
[ 0.26796377, -1.12598418, 1.23283094, -0.37481053],
|
|
[-0.22095197, 0.24468594, 1.19042819, -1.21416216],
|
|
[-0.82780366, 1.4457416 , -0.43867764, -0.1792603 ]])
|
|
|
|
"""
|
|
a = np.asanyarray(a)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
mns = np.nanmean(a=a, axis=axis, keepdims=True)
|
|
sstd = np.nanstd(a=a, axis=axis, ddof=ddof, keepdims=True)
|
|
else:
|
|
mns = a.mean(axis=axis, keepdims=True)
|
|
sstd = a.std(axis=axis, ddof=ddof, keepdims=True)
|
|
|
|
return (a - mns) / sstd
|
|
|
|
|
|
def zmap(scores, compare, axis=0, ddof=0):
|
|
"""
|
|
Calculate the relative z-scores.
|
|
|
|
Return an array of z-scores, i.e., scores that are standardized to
|
|
zero mean and unit variance, where mean and variance are calculated
|
|
from the comparison array.
|
|
|
|
Parameters
|
|
----------
|
|
scores : array_like
|
|
The input for which z-scores are calculated.
|
|
compare : array_like
|
|
The input from which the mean and standard deviation of the
|
|
normalization are taken; assumed to have the same dimension as
|
|
`scores`.
|
|
axis : int or None, optional
|
|
Axis over which mean and variance of `compare` are calculated.
|
|
Default is 0. If None, compute over the whole array `scores`.
|
|
ddof : int, optional
|
|
Degrees of freedom correction in the calculation of the
|
|
standard deviation. Default is 0.
|
|
|
|
Returns
|
|
-------
|
|
zscore : array_like
|
|
Z-scores, in the same shape as `scores`.
|
|
|
|
Notes
|
|
-----
|
|
This function preserves ndarray subclasses, and works also with
|
|
matrices and masked arrays (it uses `asanyarray` instead of
|
|
`asarray` for parameters).
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import zmap
|
|
>>> a = [0.5, 2.0, 2.5, 3]
|
|
>>> b = [0, 1, 2, 3, 4]
|
|
>>> zmap(a, b)
|
|
array([-1.06066017, 0. , 0.35355339, 0.70710678])
|
|
|
|
"""
|
|
scores, compare = map(np.asanyarray, [scores, compare])
|
|
mns = compare.mean(axis=axis, keepdims=True)
|
|
sstd = compare.std(axis=axis, ddof=ddof, keepdims=True)
|
|
return (scores - mns) / sstd
|
|
|
|
|
|
def gstd(a, axis=0, ddof=1):
|
|
"""
|
|
Calculate the geometric standard deviation of an array.
|
|
|
|
The geometric standard deviation describes the spread of a set of numbers
|
|
where the geometric mean is preferred. It is a multiplicative factor, and
|
|
so a dimensionless quantity.
|
|
|
|
It is defined as the exponent of the standard deviation of ``log(a)``.
|
|
Mathematically the population geometric standard deviation can be
|
|
evaluated as::
|
|
|
|
gstd = exp(std(log(a)))
|
|
|
|
.. versionadded:: 1.3.0
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
An array like object containing the sample data.
|
|
axis : int, tuple or None, optional
|
|
Axis along which to operate. Default is 0. If None, compute over
|
|
the whole array `a`.
|
|
ddof : int, optional
|
|
Degree of freedom correction in the calculation of the
|
|
geometric standard deviation. Default is 1.
|
|
|
|
Returns
|
|
-------
|
|
ndarray or float
|
|
An array of the geometric standard deviation. If `axis` is None or `a`
|
|
is a 1d array a float is returned.
|
|
|
|
Notes
|
|
-----
|
|
As the calculation requires the use of logarithms the geometric standard
|
|
deviation only supports strictly positive values. Any non-positive or
|
|
infinite values will raise a `ValueError`.
|
|
The geometric standard deviation is sometimes confused with the exponent of
|
|
the standard deviation, ``exp(std(a))``. Instead the geometric standard
|
|
deviation is ``exp(std(log(a)))``.
|
|
The default value for `ddof` is different to the default value (0) used
|
|
by other ddof containing functions, such as ``np.std`` and ``np.nanstd``.
|
|
|
|
Examples
|
|
--------
|
|
Find the geometric standard deviation of a log-normally distributed sample.
|
|
Note that the standard deviation of the distribution is one, on a
|
|
log scale this evaluates to approximately ``exp(1)``.
|
|
|
|
>>> from scipy.stats import gstd
|
|
>>> np.random.seed(123)
|
|
>>> sample = np.random.lognormal(mean=0, sigma=1, size=1000)
|
|
>>> gstd(sample)
|
|
2.7217860664589946
|
|
|
|
Compute the geometric standard deviation of a multidimensional array and
|
|
of a given axis.
|
|
|
|
>>> a = np.arange(1, 25).reshape(2, 3, 4)
|
|
>>> gstd(a, axis=None)
|
|
2.2944076136018947
|
|
>>> gstd(a, axis=2)
|
|
array([[1.82424757, 1.22436866, 1.13183117],
|
|
[1.09348306, 1.07244798, 1.05914985]])
|
|
>>> gstd(a, axis=(1,2))
|
|
array([2.12939215, 1.22120169])
|
|
|
|
The geometric standard deviation further handles masked arrays.
|
|
|
|
>>> a = np.arange(1, 25).reshape(2, 3, 4)
|
|
>>> ma = np.ma.masked_where(a > 16, a)
|
|
>>> ma
|
|
masked_array(
|
|
data=[[[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12]],
|
|
[[13, 14, 15, 16],
|
|
[--, --, --, --],
|
|
[--, --, --, --]]],
|
|
mask=[[[False, False, False, False],
|
|
[False, False, False, False],
|
|
[False, False, False, False]],
|
|
[[False, False, False, False],
|
|
[ True, True, True, True],
|
|
[ True, True, True, True]]],
|
|
fill_value=999999)
|
|
>>> gstd(ma, axis=2)
|
|
masked_array(
|
|
data=[[1.8242475707663655, 1.2243686572447428, 1.1318311657788478],
|
|
[1.0934830582350938, --, --]],
|
|
mask=[[False, False, False],
|
|
[False, True, True]],
|
|
fill_value=999999)
|
|
|
|
"""
|
|
a = np.asanyarray(a)
|
|
log = ma.log if isinstance(a, ma.MaskedArray) else np.log
|
|
|
|
try:
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error", RuntimeWarning)
|
|
return np.exp(np.std(log(a), axis=axis, ddof=ddof))
|
|
except RuntimeWarning as w:
|
|
if np.isinf(a).any():
|
|
raise ValueError(
|
|
'Infinite value encountered. The geometric standard deviation '
|
|
'is defined for strictly positive values only.')
|
|
a_nan = np.isnan(a)
|
|
a_nan_any = a_nan.any()
|
|
# exclude NaN's from negativity check, but
|
|
# avoid expensive masking for arrays with no NaN
|
|
if ((a_nan_any and np.less_equal(np.nanmin(a), 0)) or
|
|
(not a_nan_any and np.less_equal(a, 0).any())):
|
|
raise ValueError(
|
|
'Non positive value encountered. The geometric standard '
|
|
'deviation is defined for strictly positive values only.')
|
|
elif 'Degrees of freedom <= 0 for slice' == str(w):
|
|
raise ValueError(w)
|
|
else:
|
|
# Remaining warnings don't need to be exceptions.
|
|
return np.exp(np.std(log(a, where=~a_nan), axis=axis, ddof=ddof))
|
|
except TypeError:
|
|
raise ValueError(
|
|
'Invalid array input. The inputs could not be '
|
|
'safely coerced to any supported types')
|
|
|
|
|
|
# Private dictionary initialized only once at module level
|
|
# See https://en.wikipedia.org/wiki/Robust_measures_of_scale
|
|
_scale_conversions = {'raw': 1.0,
|
|
'normal': special.erfinv(0.5) * 2.0 * math.sqrt(2.0)}
|
|
|
|
|
|
def iqr(x, axis=None, rng=(25, 75), scale=1.0, nan_policy='propagate',
|
|
interpolation='linear', keepdims=False):
|
|
r"""
|
|
Compute the interquartile range of the data along the specified axis.
|
|
|
|
The interquartile range (IQR) is the difference between the 75th and
|
|
25th percentile of the data. It is a measure of the dispersion
|
|
similar to standard deviation or variance, but is much more robust
|
|
against outliers [2]_.
|
|
|
|
The ``rng`` parameter allows this function to compute other
|
|
percentile ranges than the actual IQR. For example, setting
|
|
``rng=(0, 100)`` is equivalent to `numpy.ptp`.
|
|
|
|
The IQR of an empty array is `np.nan`.
|
|
|
|
.. versionadded:: 0.18.0
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
Input array or object that can be converted to an array.
|
|
axis : int or sequence of int, optional
|
|
Axis along which the range is computed. The default is to
|
|
compute the IQR for the entire array.
|
|
rng : Two-element sequence containing floats in range of [0,100] optional
|
|
Percentiles over which to compute the range. Each must be
|
|
between 0 and 100, inclusive. The default is the true IQR:
|
|
`(25, 75)`. The order of the elements is not important.
|
|
scale : scalar or str, optional
|
|
The numerical value of scale will be divided out of the final
|
|
result. The following string values are recognized:
|
|
|
|
* 'raw' : No scaling, just return the raw IQR.
|
|
**Deprecated!** Use `scale=1` instead.
|
|
* 'normal' : Scale by
|
|
:math:`2 \sqrt{2} erf^{-1}(\frac{1}{2}) \approx 1.349`.
|
|
|
|
The default is 1.0. The use of scale='raw' is deprecated.
|
|
Array-like scale is also allowed, as long
|
|
as it broadcasts correctly to the output such that
|
|
``out / scale`` is a valid operation. The output dimensions
|
|
depend on the input array, `x`, the `axis` argument, and the
|
|
`keepdims` flag.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}, optional
|
|
Specifies the interpolation method to use when the percentile
|
|
boundaries lie between two data points `i` and `j`.
|
|
The following options are available (default is 'linear'):
|
|
|
|
* 'linear': `i + (j - i) * fraction`, where `fraction` is the
|
|
fractional part of the index surrounded by `i` and `j`.
|
|
* 'lower': `i`.
|
|
* 'higher': `j`.
|
|
* 'nearest': `i` or `j` whichever is nearest.
|
|
* 'midpoint': `(i + j) / 2`.
|
|
|
|
keepdims : bool, optional
|
|
If this is set to `True`, the reduced axes are left in the
|
|
result as dimensions with size one. With this option, the result
|
|
will broadcast correctly against the original array `x`.
|
|
|
|
Returns
|
|
-------
|
|
iqr : scalar or ndarray
|
|
If ``axis=None``, a scalar is returned. If the input contains
|
|
integers or floats of smaller precision than ``np.float64``, then the
|
|
output data-type is ``np.float64``. Otherwise, the output data-type is
|
|
the same as that of the input.
|
|
|
|
See Also
|
|
--------
|
|
numpy.std, numpy.var
|
|
|
|
Notes
|
|
-----
|
|
This function is heavily dependent on the version of `numpy` that is
|
|
installed. Versions greater than 1.11.0b3 are highly recommended, as they
|
|
include a number of enhancements and fixes to `numpy.percentile` and
|
|
`numpy.nanpercentile` that affect the operation of this function. The
|
|
following modifications apply:
|
|
|
|
Below 1.10.0 : `nan_policy` is poorly defined.
|
|
The default behavior of `numpy.percentile` is used for 'propagate'. This
|
|
is a hybrid of 'omit' and 'propagate' that mostly yields a skewed
|
|
version of 'omit' since NaNs are sorted to the end of the data. A
|
|
warning is raised if there are NaNs in the data.
|
|
Below 1.9.0: `numpy.nanpercentile` does not exist.
|
|
This means that `numpy.percentile` is used regardless of `nan_policy`
|
|
and a warning is issued. See previous item for a description of the
|
|
behavior.
|
|
Below 1.9.0: `keepdims` and `interpolation` are not supported.
|
|
The keywords get ignored with a warning if supplied with non-default
|
|
values. However, multiple axes are still supported.
|
|
|
|
References
|
|
----------
|
|
.. [1] "Interquartile range" https://en.wikipedia.org/wiki/Interquartile_range
|
|
.. [2] "Robust measures of scale" https://en.wikipedia.org/wiki/Robust_measures_of_scale
|
|
.. [3] "Quantile" https://en.wikipedia.org/wiki/Quantile
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import iqr
|
|
>>> x = np.array([[10, 7, 4], [3, 2, 1]])
|
|
>>> x
|
|
array([[10, 7, 4],
|
|
[ 3, 2, 1]])
|
|
>>> iqr(x)
|
|
4.0
|
|
>>> iqr(x, axis=0)
|
|
array([ 3.5, 2.5, 1.5])
|
|
>>> iqr(x, axis=1)
|
|
array([ 3., 1.])
|
|
>>> iqr(x, axis=1, keepdims=True)
|
|
array([[ 3.],
|
|
[ 1.]])
|
|
|
|
"""
|
|
x = asarray(x)
|
|
|
|
# This check prevents percentile from raising an error later. Also, it is
|
|
# consistent with `np.var` and `np.std`.
|
|
if not x.size:
|
|
return np.nan
|
|
|
|
# An error may be raised here, so fail-fast, before doing lengthy
|
|
# computations, even though `scale` is not used until later
|
|
if isinstance(scale, str):
|
|
scale_key = scale.lower()
|
|
if scale_key not in _scale_conversions:
|
|
raise ValueError("{0} not a valid scale for `iqr`".format(scale))
|
|
if scale_key == 'raw':
|
|
warnings.warn(
|
|
"use of scale='raw' is deprecated, use scale=1.0 instead",
|
|
np.VisibleDeprecationWarning
|
|
)
|
|
scale = _scale_conversions[scale_key]
|
|
|
|
# Select the percentile function to use based on nans and policy
|
|
contains_nan, nan_policy = _contains_nan(x, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
percentile_func = np.nanpercentile
|
|
else:
|
|
percentile_func = np.percentile
|
|
|
|
if len(rng) != 2:
|
|
raise TypeError("quantile range must be two element sequence")
|
|
|
|
if np.isnan(rng).any():
|
|
raise ValueError("range must not contain NaNs")
|
|
|
|
rng = sorted(rng)
|
|
pct = percentile_func(x, rng, axis=axis, interpolation=interpolation,
|
|
keepdims=keepdims)
|
|
out = np.subtract(pct[1], pct[0])
|
|
|
|
if scale != 1.0:
|
|
out /= scale
|
|
|
|
return out
|
|
|
|
|
|
def _mad_1d(x, center, nan_policy):
|
|
# Median absolute deviation for 1-d array x.
|
|
# This is a helper function for `median_abs_deviation`; it assumes its
|
|
# arguments have been validated already. In particular, x must be a
|
|
# 1-d numpy array, center must be callable, and if nan_policy is not
|
|
# 'propagate', it is assumed to be 'omit', because 'raise' is handled
|
|
# in `median_abs_deviation`.
|
|
# No warning is generated if x is empty or all nan.
|
|
isnan = np.isnan(x)
|
|
if isnan.any():
|
|
if nan_policy == 'propagate':
|
|
return np.nan
|
|
x = x[~isnan]
|
|
if x.size == 0:
|
|
# MAD of an empty array is nan.
|
|
return np.nan
|
|
# Edge cases have been handled, so do the basic MAD calculation.
|
|
med = center(x)
|
|
mad = np.median(np.abs(x - med))
|
|
return mad
|
|
|
|
|
|
def median_abs_deviation(x, axis=0, center=np.median, scale=1.0,
|
|
nan_policy='propagate'):
|
|
r"""
|
|
Compute the median absolute deviation of the data along the given axis.
|
|
|
|
The median absolute deviation (MAD, [1]_) computes the median over the
|
|
absolute deviations from the median. It is a measure of dispersion
|
|
similar to the standard deviation but more robust to outliers [2]_.
|
|
|
|
The MAD of an empty array is ``np.nan``.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
Input array or object that can be converted to an array.
|
|
axis : int or None, optional
|
|
Axis along which the range is computed. Default is 0. If None, compute
|
|
the MAD over the entire array.
|
|
center : callable, optional
|
|
A function that will return the central value. The default is to use
|
|
np.median. Any user defined function used will need to have the
|
|
function signature ``func(arr, axis)``.
|
|
scale : scalar or str, optional
|
|
The numerical value of scale will be divided out of the final
|
|
result. The default is 1.0. The string "normal" is also accepted,
|
|
and results in `scale` being the inverse of the standard normal
|
|
quantile function at 0.75, which is approximately 0.67449.
|
|
Array-like scale is also allowed, as long as it broadcasts correctly
|
|
to the output such that ``out / scale`` is a valid operation. The
|
|
output dimensions depend on the input array, `x`, and the `axis`
|
|
argument.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
mad : scalar or ndarray
|
|
If ``axis=None``, a scalar is returned. If the input contains
|
|
integers or floats of smaller precision than ``np.float64``, then the
|
|
output data-type is ``np.float64``. Otherwise, the output data-type is
|
|
the same as that of the input.
|
|
|
|
See Also
|
|
--------
|
|
numpy.std, numpy.var, numpy.median, scipy.stats.iqr, scipy.stats.tmean,
|
|
scipy.stats.tstd, scipy.stats.tvar
|
|
|
|
Notes
|
|
-----
|
|
The `center` argument only affects the calculation of the central value
|
|
around which the MAD is calculated. That is, passing in ``center=np.mean``
|
|
will calculate the MAD around the mean - it will not calculate the *mean*
|
|
absolute deviation.
|
|
|
|
The input array may contain `inf`, but if `center` returns `inf`, the
|
|
corresponding MAD for that data will be `nan`.
|
|
|
|
References
|
|
----------
|
|
.. [1] "Median absolute deviation",
|
|
https://en.wikipedia.org/wiki/Median_absolute_deviation
|
|
.. [2] "Robust measures of scale",
|
|
https://en.wikipedia.org/wiki/Robust_measures_of_scale
|
|
|
|
Examples
|
|
--------
|
|
When comparing the behavior of `median_abs_deviation` with ``np.std``,
|
|
the latter is affected when we change a single value of an array to have an
|
|
outlier value while the MAD hardly changes:
|
|
|
|
>>> from scipy import stats
|
|
>>> x = stats.norm.rvs(size=100, scale=1, random_state=123456)
|
|
>>> x.std()
|
|
0.9973906394005013
|
|
>>> stats.median_abs_deviation(x)
|
|
0.82832610097857
|
|
>>> x[0] = 345.6
|
|
>>> x.std()
|
|
34.42304872314415
|
|
>>> stats.median_abs_deviation(x)
|
|
0.8323442311590675
|
|
|
|
Axis handling example:
|
|
|
|
>>> x = np.array([[10, 7, 4], [3, 2, 1]])
|
|
>>> x
|
|
array([[10, 7, 4],
|
|
[ 3, 2, 1]])
|
|
>>> stats.median_abs_deviation(x)
|
|
array([3.5, 2.5, 1.5])
|
|
>>> stats.median_abs_deviation(x, axis=None)
|
|
2.0
|
|
|
|
Scale normal example:
|
|
|
|
>>> x = stats.norm.rvs(size=1000000, scale=2, random_state=123456)
|
|
>>> stats.median_abs_deviation(x)
|
|
1.3487398527041636
|
|
>>> stats.median_abs_deviation(x, scale='normal')
|
|
1.9996446978061115
|
|
|
|
"""
|
|
if not callable(center):
|
|
raise TypeError("The argument 'center' must be callable. The given "
|
|
f"value {repr(center)} is not callable.")
|
|
|
|
# An error may be raised here, so fail-fast, before doing lengthy
|
|
# computations, even though `scale` is not used until later
|
|
if isinstance(scale, str):
|
|
if scale.lower() == 'normal':
|
|
scale = 0.6744897501960817 # special.ndtri(0.75)
|
|
else:
|
|
raise ValueError(f"{scale} is not a valid scale value.")
|
|
|
|
x = asarray(x)
|
|
|
|
# Consistent with `np.var` and `np.std`.
|
|
if not x.size:
|
|
if axis is None:
|
|
return np.nan
|
|
nan_shape = tuple(item for i, item in enumerate(x.shape) if i != axis)
|
|
if nan_shape == ():
|
|
# Return nan, not array(nan)
|
|
return np.nan
|
|
return np.full(nan_shape, np.nan)
|
|
|
|
contains_nan, nan_policy = _contains_nan(x, nan_policy)
|
|
|
|
if contains_nan:
|
|
if axis is None:
|
|
mad = _mad_1d(x.ravel(), center, nan_policy)
|
|
else:
|
|
mad = np.apply_along_axis(_mad_1d, axis, x, center, nan_policy)
|
|
else:
|
|
if axis is None:
|
|
med = center(x, axis=None)
|
|
mad = np.median(np.abs(x - med))
|
|
else:
|
|
# Wrap the call to center() in expand_dims() so it acts like
|
|
# keepdims=True was used.
|
|
med = np.expand_dims(center(x, axis=axis), axis)
|
|
mad = np.median(np.abs(x - med), axis=axis)
|
|
|
|
return mad / scale
|
|
|
|
|
|
# Keep the top newline so that the message does not show up on the stats page
|
|
_median_absolute_deviation_deprec_msg = """
|
|
To preserve the existing default behavior, use
|
|
`scipy.stats.median_abs_deviation(..., scale=1/1.4826)`.
|
|
The value 1.4826 is not numerically precise for scaling
|
|
with a normal distribution. For a numerically precise value, use
|
|
`scipy.stats.median_abs_deviation(..., scale='normal')`.
|
|
"""
|
|
|
|
|
|
# Due to numpy/gh-16349 we need to unindent the entire docstring
|
|
@np.deprecate(old_name='median_absolute_deviation',
|
|
new_name='median_abs_deviation',
|
|
message=_median_absolute_deviation_deprec_msg)
|
|
def median_absolute_deviation(x, axis=0, center=np.median, scale=1.4826,
|
|
nan_policy='propagate'):
|
|
r"""
|
|
Compute the median absolute deviation of the data along the given axis.
|
|
|
|
The median absolute deviation (MAD, [1]_) computes the median over the
|
|
absolute deviations from the median. It is a measure of dispersion
|
|
similar to the standard deviation but more robust to outliers [2]_.
|
|
|
|
The MAD of an empty array is ``np.nan``.
|
|
|
|
.. versionadded:: 1.3.0
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
Input array or object that can be converted to an array.
|
|
axis : int or None, optional
|
|
Axis along which the range is computed. Default is 0. If None, compute
|
|
the MAD over the entire array.
|
|
center : callable, optional
|
|
A function that will return the central value. The default is to use
|
|
np.median. Any user defined function used will need to have the function
|
|
signature ``func(arr, axis)``.
|
|
scale : int, optional
|
|
The scaling factor applied to the MAD. The default scale (1.4826)
|
|
ensures consistency with the standard deviation for normally distributed
|
|
data.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
mad : scalar or ndarray
|
|
If ``axis=None``, a scalar is returned. If the input contains
|
|
integers or floats of smaller precision than ``np.float64``, then the
|
|
output data-type is ``np.float64``. Otherwise, the output data-type is
|
|
the same as that of the input.
|
|
|
|
See Also
|
|
--------
|
|
numpy.std, numpy.var, numpy.median, scipy.stats.iqr, scipy.stats.tmean,
|
|
scipy.stats.tstd, scipy.stats.tvar
|
|
|
|
Notes
|
|
-----
|
|
The `center` argument only affects the calculation of the central value
|
|
around which the MAD is calculated. That is, passing in ``center=np.mean``
|
|
will calculate the MAD around the mean - it will not calculate the *mean*
|
|
absolute deviation.
|
|
|
|
References
|
|
----------
|
|
.. [1] "Median absolute deviation",
|
|
https://en.wikipedia.org/wiki/Median_absolute_deviation
|
|
.. [2] "Robust measures of scale",
|
|
https://en.wikipedia.org/wiki/Robust_measures_of_scale
|
|
|
|
Examples
|
|
--------
|
|
When comparing the behavior of `median_absolute_deviation` with ``np.std``,
|
|
the latter is affected when we change a single value of an array to have an
|
|
outlier value while the MAD hardly changes:
|
|
|
|
>>> from scipy import stats
|
|
>>> x = stats.norm.rvs(size=100, scale=1, random_state=123456)
|
|
>>> x.std()
|
|
0.9973906394005013
|
|
>>> stats.median_absolute_deviation(x)
|
|
1.2280762773108278
|
|
>>> x[0] = 345.6
|
|
>>> x.std()
|
|
34.42304872314415
|
|
>>> stats.median_absolute_deviation(x)
|
|
1.2340335571164334
|
|
|
|
Axis handling example:
|
|
|
|
>>> x = np.array([[10, 7, 4], [3, 2, 1]])
|
|
>>> x
|
|
array([[10, 7, 4],
|
|
[ 3, 2, 1]])
|
|
>>> stats.median_absolute_deviation(x)
|
|
array([5.1891, 3.7065, 2.2239])
|
|
>>> stats.median_absolute_deviation(x, axis=None)
|
|
2.9652
|
|
"""
|
|
if isinstance(scale, str):
|
|
if scale.lower() == 'raw':
|
|
warnings.warn(
|
|
"use of scale='raw' is deprecated, use scale=1.0 instead",
|
|
np.VisibleDeprecationWarning
|
|
)
|
|
scale = 1.0
|
|
|
|
if not isinstance(scale, str):
|
|
scale = 1 / scale
|
|
|
|
return median_abs_deviation(x, axis=axis, center=center, scale=scale,
|
|
nan_policy=nan_policy)
|
|
|
|
#####################################
|
|
# TRIMMING FUNCTIONS #
|
|
#####################################
|
|
|
|
|
|
SigmaclipResult = namedtuple('SigmaclipResult', ('clipped', 'lower', 'upper'))
|
|
|
|
|
|
def sigmaclip(a, low=4., high=4.):
|
|
"""
|
|
Perform iterative sigma-clipping of array elements.
|
|
|
|
Starting from the full sample, all elements outside the critical range are
|
|
removed, i.e. all elements of the input array `c` that satisfy either of
|
|
the following conditions::
|
|
|
|
c < mean(c) - std(c)*low
|
|
c > mean(c) + std(c)*high
|
|
|
|
The iteration continues with the updated sample until no
|
|
elements are outside the (updated) range.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Data array, will be raveled if not 1-D.
|
|
low : float, optional
|
|
Lower bound factor of sigma clipping. Default is 4.
|
|
high : float, optional
|
|
Upper bound factor of sigma clipping. Default is 4.
|
|
|
|
Returns
|
|
-------
|
|
clipped : ndarray
|
|
Input array with clipped elements removed.
|
|
lower : float
|
|
Lower threshold value use for clipping.
|
|
upper : float
|
|
Upper threshold value use for clipping.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import sigmaclip
|
|
>>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
|
|
... np.linspace(0, 20, 5)))
|
|
>>> fact = 1.5
|
|
>>> c, low, upp = sigmaclip(a, fact, fact)
|
|
>>> c
|
|
array([ 9.96666667, 10. , 10.03333333, 10. ])
|
|
>>> c.var(), c.std()
|
|
(0.00055555555555555165, 0.023570226039551501)
|
|
>>> low, c.mean() - fact*c.std(), c.min()
|
|
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
|
|
>>> upp, c.mean() + fact*c.std(), c.max()
|
|
(10.035355339059327, 10.035355339059327, 10.033333333333333)
|
|
|
|
>>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
|
|
... np.linspace(-100, -50, 3)))
|
|
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
|
|
>>> (c == np.linspace(9.5, 10.5, 11)).all()
|
|
True
|
|
|
|
"""
|
|
c = np.asarray(a).ravel()
|
|
delta = 1
|
|
while delta:
|
|
c_std = c.std()
|
|
c_mean = c.mean()
|
|
size = c.size
|
|
critlower = c_mean - c_std * low
|
|
critupper = c_mean + c_std * high
|
|
c = c[(c >= critlower) & (c <= critupper)]
|
|
delta = size - c.size
|
|
|
|
return SigmaclipResult(c, critlower, critupper)
|
|
|
|
|
|
def trimboth(a, proportiontocut, axis=0):
|
|
"""
|
|
Slice off a proportion of items from both ends of an array.
|
|
|
|
Slice off the passed proportion of items from both ends of the passed
|
|
array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and**
|
|
rightmost 10% of scores). The trimmed values are the lowest and
|
|
highest ones.
|
|
Slice off less if proportion results in a non-integer slice index (i.e.
|
|
conservatively slices off `proportiontocut`).
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Data to trim.
|
|
proportiontocut : float
|
|
Proportion (in range 0-1) of total data set to trim of each end.
|
|
axis : int or None, optional
|
|
Axis along which to trim data. Default is 0. If None, compute over
|
|
the whole array `a`.
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
Trimmed version of array `a`. The order of the trimmed content
|
|
is undefined.
|
|
|
|
See Also
|
|
--------
|
|
trim_mean
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> a = np.arange(20)
|
|
>>> b = stats.trimboth(a, 0.1)
|
|
>>> b.shape
|
|
(16,)
|
|
|
|
"""
|
|
a = np.asarray(a)
|
|
|
|
if a.size == 0:
|
|
return a
|
|
|
|
if axis is None:
|
|
a = a.ravel()
|
|
axis = 0
|
|
|
|
nobs = a.shape[axis]
|
|
lowercut = int(proportiontocut * nobs)
|
|
uppercut = nobs - lowercut
|
|
if (lowercut >= uppercut):
|
|
raise ValueError("Proportion too big.")
|
|
|
|
atmp = np.partition(a, (lowercut, uppercut - 1), axis)
|
|
|
|
sl = [slice(None)] * atmp.ndim
|
|
sl[axis] = slice(lowercut, uppercut)
|
|
return atmp[tuple(sl)]
|
|
|
|
|
|
def trim1(a, proportiontocut, tail='right', axis=0):
|
|
"""
|
|
Slice off a proportion from ONE end of the passed array distribution.
|
|
|
|
If `proportiontocut` = 0.1, slices off 'leftmost' or 'rightmost'
|
|
10% of scores. The lowest or highest values are trimmed (depending on
|
|
the tail).
|
|
Slice off less if proportion results in a non-integer slice index
|
|
(i.e. conservatively slices off `proportiontocut` ).
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
proportiontocut : float
|
|
Fraction to cut off of 'left' or 'right' of distribution.
|
|
tail : {'left', 'right'}, optional
|
|
Defaults to 'right'.
|
|
axis : int or None, optional
|
|
Axis along which to trim data. Default is 0. If None, compute over
|
|
the whole array `a`.
|
|
|
|
Returns
|
|
-------
|
|
trim1 : ndarray
|
|
Trimmed version of array `a`. The order of the trimmed content is
|
|
undefined.
|
|
|
|
"""
|
|
a = np.asarray(a)
|
|
if axis is None:
|
|
a = a.ravel()
|
|
axis = 0
|
|
|
|
nobs = a.shape[axis]
|
|
|
|
# avoid possible corner case
|
|
if proportiontocut >= 1:
|
|
return []
|
|
|
|
if tail.lower() == 'right':
|
|
lowercut = 0
|
|
uppercut = nobs - int(proportiontocut * nobs)
|
|
|
|
elif tail.lower() == 'left':
|
|
lowercut = int(proportiontocut * nobs)
|
|
uppercut = nobs
|
|
|
|
atmp = np.partition(a, (lowercut, uppercut - 1), axis)
|
|
|
|
return atmp[lowercut:uppercut]
|
|
|
|
|
|
def trim_mean(a, proportiontocut, axis=0):
|
|
"""
|
|
Return mean of array after trimming distribution from both tails.
|
|
|
|
If `proportiontocut` = 0.1, slices off 'leftmost' and 'rightmost' 10% of
|
|
scores. The input is sorted before slicing. Slices off less if proportion
|
|
results in a non-integer slice index (i.e., conservatively slices off
|
|
`proportiontocut` ).
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
proportiontocut : float
|
|
Fraction to cut off of both tails of the distribution.
|
|
axis : int or None, optional
|
|
Axis along which the trimmed means are computed. Default is 0.
|
|
If None, compute over the whole array `a`.
|
|
|
|
Returns
|
|
-------
|
|
trim_mean : ndarray
|
|
Mean of trimmed array.
|
|
|
|
See Also
|
|
--------
|
|
trimboth
|
|
tmean : Compute the trimmed mean ignoring values outside given `limits`.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = np.arange(20)
|
|
>>> stats.trim_mean(x, 0.1)
|
|
9.5
|
|
>>> x2 = x.reshape(5, 4)
|
|
>>> x2
|
|
array([[ 0, 1, 2, 3],
|
|
[ 4, 5, 6, 7],
|
|
[ 8, 9, 10, 11],
|
|
[12, 13, 14, 15],
|
|
[16, 17, 18, 19]])
|
|
>>> stats.trim_mean(x2, 0.25)
|
|
array([ 8., 9., 10., 11.])
|
|
>>> stats.trim_mean(x2, 0.25, axis=1)
|
|
array([ 1.5, 5.5, 9.5, 13.5, 17.5])
|
|
|
|
"""
|
|
a = np.asarray(a)
|
|
|
|
if a.size == 0:
|
|
return np.nan
|
|
|
|
if axis is None:
|
|
a = a.ravel()
|
|
axis = 0
|
|
|
|
nobs = a.shape[axis]
|
|
lowercut = int(proportiontocut * nobs)
|
|
uppercut = nobs - lowercut
|
|
if (lowercut > uppercut):
|
|
raise ValueError("Proportion too big.")
|
|
|
|
atmp = np.partition(a, (lowercut, uppercut - 1), axis)
|
|
|
|
sl = [slice(None)] * atmp.ndim
|
|
sl[axis] = slice(lowercut, uppercut)
|
|
return np.mean(atmp[tuple(sl)], axis=axis)
|
|
|
|
|
|
F_onewayResult = namedtuple('F_onewayResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
class F_onewayConstantInputWarning(RuntimeWarning):
|
|
"""
|
|
Warning generated by `f_oneway` when an input is constant, e.g.
|
|
each of the samples provided is a constant array.
|
|
"""
|
|
|
|
def __init__(self, msg=None):
|
|
if msg is None:
|
|
msg = ("Each of the input arrays is constant;"
|
|
"the F statistic is not defined or infinite")
|
|
self.args = (msg,)
|
|
|
|
|
|
class F_onewayBadInputSizesWarning(RuntimeWarning):
|
|
"""
|
|
Warning generated by `f_oneway` when an input has length 0,
|
|
or if all the inputs have length 1.
|
|
"""
|
|
pass
|
|
|
|
|
|
def _create_f_oneway_nan_result(shape, axis):
|
|
"""
|
|
This is a helper function for f_oneway for creating the return values
|
|
in certain degenerate conditions. It creates return values that are
|
|
all nan with the appropriate shape for the given `shape` and `axis`.
|
|
"""
|
|
axis = np.core.multiarray.normalize_axis_index(axis, len(shape))
|
|
shp = shape[:axis] + shape[axis+1:]
|
|
if shp == ():
|
|
f = np.nan
|
|
prob = np.nan
|
|
else:
|
|
f = np.full(shp, fill_value=np.nan)
|
|
prob = f.copy()
|
|
return F_onewayResult(f, prob)
|
|
|
|
|
|
def _first(arr, axis):
|
|
"""
|
|
Return arr[..., 0:1, ...] where 0:1 is in the `axis` position.
|
|
"""
|
|
# When the oldest version of numpy supported by scipy is at
|
|
# least 1.15.0, this function can be replaced by np.take_along_axis
|
|
# (with appropriately configured arguments).
|
|
axis = np.core.multiarray.normalize_axis_index(axis, arr.ndim)
|
|
return arr[tuple(slice(None) if k != axis else slice(0, 1)
|
|
for k in range(arr.ndim))]
|
|
|
|
|
|
def f_oneway(*args, axis=0):
|
|
"""
|
|
Perform one-way ANOVA.
|
|
|
|
The one-way ANOVA tests the null hypothesis that two or more groups have
|
|
the same population mean. The test is applied to samples from two or
|
|
more groups, possibly with differing sizes.
|
|
|
|
Parameters
|
|
----------
|
|
sample1, sample2, ... : array_like
|
|
The sample measurements for each group. There must be at least
|
|
two arguments. If the arrays are multidimensional, then all the
|
|
dimensions of the array must be the same except for `axis`.
|
|
axis : int, optional
|
|
Axis of the input arrays along which the test is applied.
|
|
Default is 0.
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The computed F statistic of the test.
|
|
pvalue : float
|
|
The associated p-value from the F distribution.
|
|
|
|
Warns
|
|
-----
|
|
F_onewayConstantInputWarning
|
|
Raised if each of the input arrays is constant array.
|
|
In this case the F statistic is either infinite or isn't defined,
|
|
so ``np.inf`` or ``np.nan`` is returned.
|
|
|
|
F_onewayBadInputSizesWarning
|
|
Raised if the length of any input array is 0, or if all the input
|
|
arrays have length 1. ``np.nan`` is returned for the F statistic
|
|
and the p-value in these cases.
|
|
|
|
Notes
|
|
-----
|
|
The ANOVA test has important assumptions that must be satisfied in order
|
|
for the associated p-value to be valid.
|
|
|
|
1. The samples are independent.
|
|
2. Each sample is from a normally distributed population.
|
|
3. The population standard deviations of the groups are all equal. This
|
|
property is known as homoscedasticity.
|
|
|
|
If these assumptions are not true for a given set of data, it may still
|
|
be possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`)
|
|
although with some loss of power.
|
|
|
|
The length of each group must be at least one, and there must be at
|
|
least one group with length greater than one. If these conditions
|
|
are not satisfied, a warning is generated and (``np.nan``, ``np.nan``)
|
|
is returned.
|
|
|
|
If each group contains constant values, and there exist at least two
|
|
groups with different values, the function generates a warning and
|
|
returns (``np.inf``, 0).
|
|
|
|
If all values in all groups are the same, function generates a warning
|
|
and returns (``np.nan``, ``np.nan``).
|
|
|
|
The algorithm is from Heiman [2]_, pp.394-7.
|
|
|
|
References
|
|
----------
|
|
.. [1] R. Lowry, "Concepts and Applications of Inferential Statistics",
|
|
Chapter 14, 2014, http://vassarstats.net/textbook/
|
|
|
|
.. [2] G.W. Heiman, "Understanding research methods and statistics: An
|
|
integrated introduction for psychology", Houghton, Mifflin and
|
|
Company, 2001.
|
|
|
|
.. [3] G.H. McDonald, "Handbook of Biological Statistics", One-way ANOVA.
|
|
http://www.biostathandbook.com/onewayanova.html
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import f_oneway
|
|
|
|
Here are some data [3]_ on a shell measurement (the length of the anterior
|
|
adductor muscle scar, standardized by dividing by length) in the mussel
|
|
Mytilus trossulus from five locations: Tillamook, Oregon; Newport, Oregon;
|
|
Petersburg, Alaska; Magadan, Russia; and Tvarminne, Finland, taken from a
|
|
much larger data set used in McDonald et al. (1991).
|
|
|
|
>>> tillamook = [0.0571, 0.0813, 0.0831, 0.0976, 0.0817, 0.0859, 0.0735,
|
|
... 0.0659, 0.0923, 0.0836]
|
|
>>> newport = [0.0873, 0.0662, 0.0672, 0.0819, 0.0749, 0.0649, 0.0835,
|
|
... 0.0725]
|
|
>>> petersburg = [0.0974, 0.1352, 0.0817, 0.1016, 0.0968, 0.1064, 0.105]
|
|
>>> magadan = [0.1033, 0.0915, 0.0781, 0.0685, 0.0677, 0.0697, 0.0764,
|
|
... 0.0689]
|
|
>>> tvarminne = [0.0703, 0.1026, 0.0956, 0.0973, 0.1039, 0.1045]
|
|
>>> f_oneway(tillamook, newport, petersburg, magadan, tvarminne)
|
|
F_onewayResult(statistic=7.121019471642447, pvalue=0.0002812242314534544)
|
|
|
|
`f_oneway` accepts multidimensional input arrays. When the inputs
|
|
are multidimensional and `axis` is not given, the test is performed
|
|
along the first axis of the input arrays. For the following data, the
|
|
test is performed three times, once for each column.
|
|
|
|
>>> a = np.array([[9.87, 9.03, 6.81],
|
|
... [7.18, 8.35, 7.00],
|
|
... [8.39, 7.58, 7.68],
|
|
... [7.45, 6.33, 9.35],
|
|
... [6.41, 7.10, 9.33],
|
|
... [8.00, 8.24, 8.44]])
|
|
>>> b = np.array([[6.35, 7.30, 7.16],
|
|
... [6.65, 6.68, 7.63],
|
|
... [5.72, 7.73, 6.72],
|
|
... [7.01, 9.19, 7.41],
|
|
... [7.75, 7.87, 8.30],
|
|
... [6.90, 7.97, 6.97]])
|
|
>>> c = np.array([[3.31, 8.77, 1.01],
|
|
... [8.25, 3.24, 3.62],
|
|
... [6.32, 8.81, 5.19],
|
|
... [7.48, 8.83, 8.91],
|
|
... [8.59, 6.01, 6.07],
|
|
... [3.07, 9.72, 7.48]])
|
|
>>> F, p = f_oneway(a, b, c)
|
|
>>> F
|
|
array([1.75676344, 0.03701228, 3.76439349])
|
|
>>> p
|
|
array([0.20630784, 0.96375203, 0.04733157])
|
|
|
|
"""
|
|
if len(args) < 2:
|
|
raise TypeError(f'at least two inputs are required; got {len(args)}.')
|
|
|
|
args = [np.asarray(arg, dtype=float) for arg in args]
|
|
|
|
# ANOVA on N groups, each in its own array
|
|
num_groups = len(args)
|
|
|
|
# We haven't explicitly validated axis, but if it is bad, this call of
|
|
# np.concatenate will raise np.AxisError. The call will raise ValueError
|
|
# if the dimensions of all the arrays, except the axis dimension, are not
|
|
# the same.
|
|
alldata = np.concatenate(args, axis=axis)
|
|
bign = alldata.shape[axis]
|
|
|
|
# Check this after forming alldata, so shape errors are detected
|
|
# and reported before checking for 0 length inputs.
|
|
if any(arg.shape[axis] == 0 for arg in args):
|
|
warnings.warn(F_onewayBadInputSizesWarning('at least one input '
|
|
'has length 0'))
|
|
return _create_f_oneway_nan_result(alldata.shape, axis)
|
|
|
|
# Must have at least one group with length greater than 1.
|
|
if all(arg.shape[axis] == 1 for arg in args):
|
|
msg = ('all input arrays have length 1. f_oneway requires that at '
|
|
'least one input has length greater than 1.')
|
|
warnings.warn(F_onewayBadInputSizesWarning(msg))
|
|
return _create_f_oneway_nan_result(alldata.shape, axis)
|
|
|
|
# Check if the values within each group are constant, and if the common
|
|
# value in at least one group is different from that in another group.
|
|
# Based on https://github.com/scipy/scipy/issues/11669
|
|
|
|
# If axis=0, say, and the groups have shape (n0, ...), (n1, ...), ...,
|
|
# then is_const is a boolean array with shape (num_groups, ...).
|
|
# It is True if the groups along the axis slice are each consant.
|
|
# In the typical case where each input array is 1-d, is_const is a
|
|
# 1-d array with length num_groups.
|
|
is_const = np.concatenate([(_first(a, axis) == a).all(axis=axis,
|
|
keepdims=True)
|
|
for a in args], axis=axis)
|
|
|
|
# all_const is a boolean array with shape (...) (see previous comment).
|
|
# It is True if the values within each group along the axis slice are
|
|
# the same (e.g. [[3, 3, 3], [5, 5, 5, 5], [4, 4, 4]]).
|
|
all_const = is_const.all(axis=axis)
|
|
if all_const.any():
|
|
warnings.warn(F_onewayConstantInputWarning())
|
|
|
|
# all_same_const is True if all the values in the groups along the axis=0
|
|
# slice are the same (e.g. [[3, 3, 3], [3, 3, 3, 3], [3, 3, 3]]).
|
|
all_same_const = (_first(alldata, axis) == alldata).all(axis=axis)
|
|
|
|
# Determine the mean of the data, and subtract that from all inputs to a
|
|
# variance (via sum_of_sq / sq_of_sum) calculation. Variance is invariant
|
|
# to a shift in location, and centering all data around zero vastly
|
|
# improves numerical stability.
|
|
offset = alldata.mean(axis=axis, keepdims=True)
|
|
alldata -= offset
|
|
|
|
normalized_ss = _square_of_sums(alldata, axis=axis) / bign
|
|
|
|
sstot = _sum_of_squares(alldata, axis=axis) - normalized_ss
|
|
|
|
ssbn = 0
|
|
for a in args:
|
|
ssbn += _square_of_sums(a - offset, axis=axis) / a.shape[axis]
|
|
|
|
# Naming: variables ending in bn/b are for "between treatments", wn/w are
|
|
# for "within treatments"
|
|
ssbn -= normalized_ss
|
|
sswn = sstot - ssbn
|
|
dfbn = num_groups - 1
|
|
dfwn = bign - num_groups
|
|
msb = ssbn / dfbn
|
|
msw = sswn / dfwn
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|
f = msb / msw
|
|
|
|
prob = special.fdtrc(dfbn, dfwn, f) # equivalent to stats.f.sf
|
|
|
|
# Fix any f values that should be inf or nan because the corresponding
|
|
# inputs were constant.
|
|
if np.isscalar(f):
|
|
if all_same_const:
|
|
f = np.nan
|
|
prob = np.nan
|
|
elif all_const:
|
|
f = np.inf
|
|
prob = 0.0
|
|
else:
|
|
f[all_const] = np.inf
|
|
prob[all_const] = 0.0
|
|
f[all_same_const] = np.nan
|
|
prob[all_same_const] = np.nan
|
|
|
|
return F_onewayResult(f, prob)
|
|
|
|
|
|
class PearsonRConstantInputWarning(RuntimeWarning):
|
|
"""Warning generated by `pearsonr` when an input is constant."""
|
|
|
|
def __init__(self, msg=None):
|
|
if msg is None:
|
|
msg = ("An input array is constant; the correlation coefficent "
|
|
"is not defined.")
|
|
self.args = (msg,)
|
|
|
|
|
|
class PearsonRNearConstantInputWarning(RuntimeWarning):
|
|
"""Warning generated by `pearsonr` when an input is nearly constant."""
|
|
|
|
def __init__(self, msg=None):
|
|
if msg is None:
|
|
msg = ("An input array is nearly constant; the computed "
|
|
"correlation coefficent may be inaccurate.")
|
|
self.args = (msg,)
|
|
|
|
|
|
def pearsonr(x, y):
|
|
r"""
|
|
Pearson correlation coefficient and p-value for testing non-correlation.
|
|
|
|
The Pearson correlation coefficient [1]_ measures the linear relationship
|
|
between two datasets. The calculation of the p-value relies on the
|
|
assumption that each dataset is normally distributed. (See Kowalski [3]_
|
|
for a discussion of the effects of non-normality of the input on the
|
|
distribution of the correlation coefficient.) Like other correlation
|
|
coefficients, this one varies between -1 and +1 with 0 implying no
|
|
correlation. Correlations of -1 or +1 imply an exact linear relationship.
|
|
Positive correlations imply that as x increases, so does y. Negative
|
|
correlations imply that as x increases, y decreases.
|
|
|
|
The p-value roughly indicates the probability of an uncorrelated system
|
|
producing datasets that have a Pearson correlation at least as extreme
|
|
as the one computed from these datasets.
|
|
|
|
Parameters
|
|
----------
|
|
x : (N,) array_like
|
|
Input array.
|
|
y : (N,) array_like
|
|
Input array.
|
|
|
|
Returns
|
|
-------
|
|
r : float
|
|
Pearson's correlation coefficient.
|
|
p-value : float
|
|
Two-tailed p-value.
|
|
|
|
Warns
|
|
-----
|
|
PearsonRConstantInputWarning
|
|
Raised if an input is a constant array. The correlation coefficient
|
|
is not defined in this case, so ``np.nan`` is returned.
|
|
|
|
PearsonRNearConstantInputWarning
|
|
Raised if an input is "nearly" constant. The array ``x`` is considered
|
|
nearly constant if ``norm(x - mean(x)) < 1e-13 * abs(mean(x))``.
|
|
Numerical errors in the calculation ``x - mean(x)`` in this case might
|
|
result in an inaccurate calculation of r.
|
|
|
|
See Also
|
|
--------
|
|
spearmanr : Spearman rank-order correlation coefficient.
|
|
kendalltau : Kendall's tau, a correlation measure for ordinal data.
|
|
|
|
Notes
|
|
-----
|
|
The correlation coefficient is calculated as follows:
|
|
|
|
.. math::
|
|
|
|
r = \frac{\sum (x - m_x) (y - m_y)}
|
|
{\sqrt{\sum (x - m_x)^2 \sum (y - m_y)^2}}
|
|
|
|
where :math:`m_x` is the mean of the vector :math:`x` and :math:`m_y` is
|
|
the mean of the vector :math:`y`.
|
|
|
|
Under the assumption that x and y are drawn from independent normal
|
|
distributions (so the population correlation coefficient is 0), the
|
|
probability density function of the sample correlation coefficient r
|
|
is ([1]_, [2]_)::
|
|
|
|
(1 - r**2)**(n/2 - 2)
|
|
f(r) = ---------------------
|
|
B(1/2, n/2 - 1)
|
|
|
|
where n is the number of samples, and B is the beta function. This
|
|
is sometimes referred to as the exact distribution of r. This is
|
|
the distribution that is used in `pearsonr` to compute the p-value.
|
|
The distribution is a beta distribution on the interval [-1, 1],
|
|
with equal shape parameters a = b = n/2 - 1. In terms of SciPy's
|
|
implementation of the beta distribution, the distribution of r is::
|
|
|
|
dist = scipy.stats.beta(n/2 - 1, n/2 - 1, loc=-1, scale=2)
|
|
|
|
The p-value returned by `pearsonr` is a two-sided p-value. For a
|
|
given sample with correlation coefficient r, the p-value is
|
|
the probability that abs(r') of a random sample x' and y' drawn from
|
|
the population with zero correlation would be greater than or equal
|
|
to abs(r). In terms of the object ``dist`` shown above, the p-value
|
|
for a given r and length n can be computed as::
|
|
|
|
p = 2*dist.cdf(-abs(r))
|
|
|
|
When n is 2, the above continuous distribution is not well-defined.
|
|
One can interpret the limit of the beta distribution as the shape
|
|
parameters a and b approach a = b = 0 as a discrete distribution with
|
|
equal probability masses at r = 1 and r = -1. More directly, one
|
|
can observe that, given the data x = [x1, x2] and y = [y1, y2], and
|
|
assuming x1 != x2 and y1 != y2, the only possible values for r are 1
|
|
and -1. Because abs(r') for any sample x' and y' with length 2 will
|
|
be 1, the two-sided p-value for a sample of length 2 is always 1.
|
|
|
|
References
|
|
----------
|
|
.. [1] "Pearson correlation coefficient", Wikipedia,
|
|
https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
|
|
.. [2] Student, "Probable error of a correlation coefficient",
|
|
Biometrika, Volume 6, Issue 2-3, 1 September 1908, pp. 302-310.
|
|
.. [3] C. J. Kowalski, "On the Effects of Non-Normality on the Distribution
|
|
of the Sample Product-Moment Correlation Coefficient"
|
|
Journal of the Royal Statistical Society. Series C (Applied
|
|
Statistics), Vol. 21, No. 1 (1972), pp. 1-12.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> a = np.array([0, 0, 0, 1, 1, 1, 1])
|
|
>>> b = np.arange(7)
|
|
>>> stats.pearsonr(a, b)
|
|
(0.8660254037844386, 0.011724811003954649)
|
|
|
|
>>> stats.pearsonr([1, 2, 3, 4, 5], [10, 9, 2.5, 6, 4])
|
|
(-0.7426106572325057, 0.1505558088534455)
|
|
|
|
"""
|
|
n = len(x)
|
|
if n != len(y):
|
|
raise ValueError('x and y must have the same length.')
|
|
|
|
if n < 2:
|
|
raise ValueError('x and y must have length at least 2.')
|
|
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
|
|
# If an input is constant, the correlation coefficient is not defined.
|
|
if (x == x[0]).all() or (y == y[0]).all():
|
|
warnings.warn(PearsonRConstantInputWarning())
|
|
return np.nan, np.nan
|
|
|
|
# dtype is the data type for the calculations. This expression ensures
|
|
# that the data type is at least 64 bit floating point. It might have
|
|
# more precision if the input is, for example, np.longdouble.
|
|
dtype = type(1.0 + x[0] + y[0])
|
|
|
|
if n == 2:
|
|
return dtype(np.sign(x[1] - x[0])*np.sign(y[1] - y[0])), 1.0
|
|
|
|
xmean = x.mean(dtype=dtype)
|
|
ymean = y.mean(dtype=dtype)
|
|
|
|
# By using `astype(dtype)`, we ensure that the intermediate calculations
|
|
# use at least 64 bit floating point.
|
|
xm = x.astype(dtype) - xmean
|
|
ym = y.astype(dtype) - ymean
|
|
|
|
# Unlike np.linalg.norm or the expression sqrt((xm*xm).sum()),
|
|
# scipy.linalg.norm(xm) does not overflow if xm is, for example,
|
|
# [-5e210, 5e210, 3e200, -3e200]
|
|
normxm = linalg.norm(xm)
|
|
normym = linalg.norm(ym)
|
|
|
|
threshold = 1e-13
|
|
if normxm < threshold*abs(xmean) or normym < threshold*abs(ymean):
|
|
# If all the values in x (likewise y) are very close to the mean,
|
|
# the loss of precision that occurs in the subtraction xm = x - xmean
|
|
# might result in large errors in r.
|
|
warnings.warn(PearsonRNearConstantInputWarning())
|
|
|
|
r = np.dot(xm/normxm, ym/normym)
|
|
|
|
# Presumably, if abs(r) > 1, then it is only some small artifact of
|
|
# floating point arithmetic.
|
|
r = max(min(r, 1.0), -1.0)
|
|
|
|
# As explained in the docstring, the p-value can be computed as
|
|
# p = 2*dist.cdf(-abs(r))
|
|
# where dist is the beta distribution on [-1, 1] with shape parameters
|
|
# a = b = n/2 - 1. `special.btdtr` is the CDF for the beta distribution
|
|
# on [0, 1]. To use it, we make the transformation x = (r + 1)/2; the
|
|
# shape parameters do not change. Then -abs(r) used in `cdf(-abs(r))`
|
|
# becomes x = (-abs(r) + 1)/2 = 0.5*(1 - abs(r)). (r is cast to float64
|
|
# to avoid a TypeError raised by btdtr when r is higher precision.)
|
|
ab = n/2 - 1
|
|
prob = 2*special.btdtr(ab, ab, 0.5*(1 - abs(np.float64(r))))
|
|
|
|
return r, prob
|
|
|
|
|
|
def fisher_exact(table, alternative='two-sided'):
|
|
"""
|
|
Perform a Fisher exact test on a 2x2 contingency table.
|
|
|
|
Parameters
|
|
----------
|
|
table : array_like of ints
|
|
A 2x2 contingency table. Elements should be non-negative integers.
|
|
alternative : {'two-sided', 'less', 'greater'}, optional
|
|
Defines the alternative hypothesis.
|
|
The following options are available (default is 'two-sided'):
|
|
|
|
* 'two-sided'
|
|
* 'less': one-sided
|
|
* 'greater': one-sided
|
|
|
|
Returns
|
|
-------
|
|
oddsratio : float
|
|
This is prior odds ratio and not a posterior estimate.
|
|
p_value : float
|
|
P-value, the probability of obtaining a distribution at least as
|
|
extreme as the one that was actually observed, assuming that the
|
|
null hypothesis is true.
|
|
|
|
See Also
|
|
--------
|
|
chi2_contingency : Chi-square test of independence of variables in a
|
|
contingency table.
|
|
|
|
Notes
|
|
-----
|
|
The calculated odds ratio is different from the one R uses. This scipy
|
|
implementation returns the (more common) "unconditional Maximum
|
|
Likelihood Estimate", while R uses the "conditional Maximum Likelihood
|
|
Estimate".
|
|
|
|
For tables with large numbers, the (inexact) chi-square test implemented
|
|
in the function `chi2_contingency` can also be used.
|
|
|
|
Examples
|
|
--------
|
|
Say we spend a few days counting whales and sharks in the Atlantic and
|
|
Indian oceans. In the Atlantic ocean we find 8 whales and 1 shark, in the
|
|
Indian ocean 2 whales and 5 sharks. Then our contingency table is::
|
|
|
|
Atlantic Indian
|
|
whales 8 2
|
|
sharks 1 5
|
|
|
|
We use this table to find the p-value:
|
|
|
|
>>> import scipy.stats as stats
|
|
>>> oddsratio, pvalue = stats.fisher_exact([[8, 2], [1, 5]])
|
|
>>> pvalue
|
|
0.0349...
|
|
|
|
The probability that we would observe this or an even more imbalanced ratio
|
|
by chance is about 3.5%. A commonly used significance level is 5%--if we
|
|
adopt that, we can therefore conclude that our observed imbalance is
|
|
statistically significant; whales prefer the Atlantic while sharks prefer
|
|
the Indian ocean.
|
|
|
|
"""
|
|
hypergeom = distributions.hypergeom
|
|
c = np.asarray(table, dtype=np.int64) # int32 is not enough for the algorithm
|
|
if not c.shape == (2, 2):
|
|
raise ValueError("The input `table` must be of shape (2, 2).")
|
|
|
|
if np.any(c < 0):
|
|
raise ValueError("All values in `table` must be nonnegative.")
|
|
|
|
if 0 in c.sum(axis=0) or 0 in c.sum(axis=1):
|
|
# If both values in a row or column are zero, the p-value is 1 and
|
|
# the odds ratio is NaN.
|
|
return np.nan, 1.0
|
|
|
|
if c[1, 0] > 0 and c[0, 1] > 0:
|
|
oddsratio = c[0, 0] * c[1, 1] / (c[1, 0] * c[0, 1])
|
|
else:
|
|
oddsratio = np.inf
|
|
|
|
n1 = c[0, 0] + c[0, 1]
|
|
n2 = c[1, 0] + c[1, 1]
|
|
n = c[0, 0] + c[1, 0]
|
|
|
|
def binary_search(n, n1, n2, side):
|
|
"""Binary search for where to begin halves in two-sided test."""
|
|
if side == "upper":
|
|
minval = mode
|
|
maxval = n
|
|
else:
|
|
minval = 0
|
|
maxval = mode
|
|
guess = -1
|
|
while maxval - minval > 1:
|
|
if maxval == minval + 1 and guess == minval:
|
|
guess = maxval
|
|
else:
|
|
guess = (maxval + minval) // 2
|
|
pguess = hypergeom.pmf(guess, n1 + n2, n1, n)
|
|
if side == "upper":
|
|
ng = guess - 1
|
|
else:
|
|
ng = guess + 1
|
|
if pguess <= pexact < hypergeom.pmf(ng, n1 + n2, n1, n):
|
|
break
|
|
elif pguess < pexact:
|
|
maxval = guess
|
|
else:
|
|
minval = guess
|
|
if guess == -1:
|
|
guess = minval
|
|
if side == "upper":
|
|
while guess > 0 and hypergeom.pmf(guess, n1 + n2, n1, n) < pexact * epsilon:
|
|
guess -= 1
|
|
while hypergeom.pmf(guess, n1 + n2, n1, n) > pexact / epsilon:
|
|
guess += 1
|
|
else:
|
|
while hypergeom.pmf(guess, n1 + n2, n1, n) < pexact * epsilon:
|
|
guess += 1
|
|
while guess > 0 and hypergeom.pmf(guess, n1 + n2, n1, n) > pexact / epsilon:
|
|
guess -= 1
|
|
return guess
|
|
|
|
if alternative == 'less':
|
|
pvalue = hypergeom.cdf(c[0, 0], n1 + n2, n1, n)
|
|
elif alternative == 'greater':
|
|
# Same formula as the 'less' case, but with the second column.
|
|
pvalue = hypergeom.cdf(c[0, 1], n1 + n2, n1, c[0, 1] + c[1, 1])
|
|
elif alternative == 'two-sided':
|
|
mode = int((n + 1) * (n1 + 1) / (n1 + n2 + 2))
|
|
pexact = hypergeom.pmf(c[0, 0], n1 + n2, n1, n)
|
|
pmode = hypergeom.pmf(mode, n1 + n2, n1, n)
|
|
|
|
epsilon = 1 - 1e-4
|
|
if np.abs(pexact - pmode) / np.maximum(pexact, pmode) <= 1 - epsilon:
|
|
return oddsratio, 1.
|
|
|
|
elif c[0, 0] < mode:
|
|
plower = hypergeom.cdf(c[0, 0], n1 + n2, n1, n)
|
|
if hypergeom.pmf(n, n1 + n2, n1, n) > pexact / epsilon:
|
|
return oddsratio, plower
|
|
|
|
guess = binary_search(n, n1, n2, "upper")
|
|
pvalue = plower + hypergeom.sf(guess - 1, n1 + n2, n1, n)
|
|
else:
|
|
pupper = hypergeom.sf(c[0, 0] - 1, n1 + n2, n1, n)
|
|
if hypergeom.pmf(0, n1 + n2, n1, n) > pexact / epsilon:
|
|
return oddsratio, pupper
|
|
|
|
guess = binary_search(n, n1, n2, "lower")
|
|
pvalue = pupper + hypergeom.cdf(guess, n1 + n2, n1, n)
|
|
else:
|
|
msg = "`alternative` should be one of {'two-sided', 'less', 'greater'}"
|
|
raise ValueError(msg)
|
|
|
|
pvalue = min(pvalue, 1.0)
|
|
|
|
return oddsratio, pvalue
|
|
|
|
|
|
class SpearmanRConstantInputWarning(RuntimeWarning):
|
|
"""Warning generated by `spearmanr` when an input is constant."""
|
|
|
|
def __init__(self, msg=None):
|
|
if msg is None:
|
|
msg = ("An input array is constant; the correlation coefficent "
|
|
"is not defined.")
|
|
self.args = (msg,)
|
|
|
|
|
|
SpearmanrResult = namedtuple('SpearmanrResult', ('correlation', 'pvalue'))
|
|
|
|
|
|
def spearmanr(a, b=None, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Calculate a Spearman correlation coefficient with associated p-value.
|
|
|
|
The Spearman rank-order correlation coefficient is a nonparametric measure
|
|
of the monotonicity of the relationship between two datasets. Unlike the
|
|
Pearson correlation, the Spearman correlation does not assume that both
|
|
datasets are normally distributed. Like other correlation coefficients,
|
|
this one varies between -1 and +1 with 0 implying no correlation.
|
|
Correlations of -1 or +1 imply an exact monotonic relationship. Positive
|
|
correlations imply that as x increases, so does y. Negative correlations
|
|
imply that as x increases, y decreases.
|
|
|
|
The p-value roughly indicates the probability of an uncorrelated system
|
|
producing datasets that have a Spearman correlation at least as extreme
|
|
as the one computed from these datasets. The p-values are not entirely
|
|
reliable but are probably reasonable for datasets larger than 500 or so.
|
|
|
|
Parameters
|
|
----------
|
|
a, b : 1D or 2D array_like, b is optional
|
|
One or two 1-D or 2-D arrays containing multiple variables and
|
|
observations. When these are 1-D, each represents a vector of
|
|
observations of a single variable. For the behavior in the 2-D case,
|
|
see under ``axis``, below.
|
|
Both arrays need to have the same length in the ``axis`` dimension.
|
|
axis : int or None, optional
|
|
If axis=0 (default), then each column represents a variable, with
|
|
observations in the rows. If axis=1, the relationship is transposed:
|
|
each row represents a variable, while the columns contain observations.
|
|
If axis=None, then both arrays will be raveled.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
correlation : float or ndarray (2-D square)
|
|
Spearman correlation matrix or correlation coefficient (if only 2
|
|
variables are given as parameters. Correlation matrix is square with
|
|
length equal to total number of variables (columns or rows) in ``a``
|
|
and ``b`` combined.
|
|
pvalue : float
|
|
The two-sided p-value for a hypothesis test whose null hypothesis is
|
|
that two sets of data are uncorrelated, has same dimension as rho.
|
|
|
|
References
|
|
----------
|
|
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
|
|
Probability and Statistics Tables and Formulae. Chapman & Hall: New
|
|
York. 2000.
|
|
Section 14.7
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> stats.spearmanr([1,2,3,4,5], [5,6,7,8,7])
|
|
(0.82078268166812329, 0.088587005313543798)
|
|
>>> np.random.seed(1234321)
|
|
>>> x2n = np.random.randn(100, 2)
|
|
>>> y2n = np.random.randn(100, 2)
|
|
>>> stats.spearmanr(x2n)
|
|
(0.059969996999699973, 0.55338590803773591)
|
|
>>> stats.spearmanr(x2n[:,0], x2n[:,1])
|
|
(0.059969996999699973, 0.55338590803773591)
|
|
>>> rho, pval = stats.spearmanr(x2n, y2n)
|
|
>>> rho
|
|
array([[ 1. , 0.05997 , 0.18569457, 0.06258626],
|
|
[ 0.05997 , 1. , 0.110003 , 0.02534653],
|
|
[ 0.18569457, 0.110003 , 1. , 0.03488749],
|
|
[ 0.06258626, 0.02534653, 0.03488749, 1. ]])
|
|
>>> pval
|
|
array([[ 0. , 0.55338591, 0.06435364, 0.53617935],
|
|
[ 0.55338591, 0. , 0.27592895, 0.80234077],
|
|
[ 0.06435364, 0.27592895, 0. , 0.73039992],
|
|
[ 0.53617935, 0.80234077, 0.73039992, 0. ]])
|
|
>>> rho, pval = stats.spearmanr(x2n.T, y2n.T, axis=1)
|
|
>>> rho
|
|
array([[ 1. , 0.05997 , 0.18569457, 0.06258626],
|
|
[ 0.05997 , 1. , 0.110003 , 0.02534653],
|
|
[ 0.18569457, 0.110003 , 1. , 0.03488749],
|
|
[ 0.06258626, 0.02534653, 0.03488749, 1. ]])
|
|
>>> stats.spearmanr(x2n, y2n, axis=None)
|
|
(0.10816770419260482, 0.1273562188027364)
|
|
>>> stats.spearmanr(x2n.ravel(), y2n.ravel())
|
|
(0.10816770419260482, 0.1273562188027364)
|
|
|
|
>>> xint = np.random.randint(10, size=(100, 2))
|
|
>>> stats.spearmanr(xint)
|
|
(0.052760927029710199, 0.60213045837062351)
|
|
|
|
"""
|
|
if axis is not None and axis > 1:
|
|
raise ValueError("spearmanr only handles 1-D or 2-D arrays, supplied axis argument {}, please use only values 0, 1 or None for axis".format(axis))
|
|
|
|
a, axisout = _chk_asarray(a, axis)
|
|
if a.ndim > 2:
|
|
raise ValueError("spearmanr only handles 1-D or 2-D arrays")
|
|
|
|
if b is None:
|
|
if a.ndim < 2:
|
|
raise ValueError("`spearmanr` needs at least 2 variables to compare")
|
|
else:
|
|
# Concatenate a and b, so that we now only have to handle the case
|
|
# of a 2-D `a`.
|
|
b, _ = _chk_asarray(b, axis)
|
|
if axisout == 0:
|
|
a = np.column_stack((a, b))
|
|
else:
|
|
a = np.row_stack((a, b))
|
|
|
|
n_vars = a.shape[1 - axisout]
|
|
n_obs = a.shape[axisout]
|
|
if n_obs <= 1:
|
|
# Handle empty arrays or single observations.
|
|
return SpearmanrResult(np.nan, np.nan)
|
|
|
|
if axisout == 0:
|
|
if (a[:, 0][0] == a[:, 0]).all() or (a[:, 1][0] == a[:, 1]).all():
|
|
# If an input is constant, the correlation coefficient is not defined.
|
|
warnings.warn(SpearmanRConstantInputWarning())
|
|
return SpearmanrResult(np.nan, np.nan)
|
|
else: # case when axisout == 1 b/c a is 2 dim only
|
|
if (a[0, :][0] == a[0, :]).all() or (a[1, :][0] == a[1, :]).all():
|
|
# If an input is constant, the correlation coefficient is not defined.
|
|
warnings.warn(SpearmanRConstantInputWarning())
|
|
return SpearmanrResult(np.nan, np.nan)
|
|
|
|
a_contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
variable_has_nan = np.zeros(n_vars, dtype=bool)
|
|
if a_contains_nan:
|
|
if nan_policy == 'omit':
|
|
return mstats_basic.spearmanr(a, axis=axis, nan_policy=nan_policy)
|
|
elif nan_policy == 'propagate':
|
|
if a.ndim == 1 or n_vars <= 2:
|
|
return SpearmanrResult(np.nan, np.nan)
|
|
else:
|
|
# Keep track of variables with NaNs, set the outputs to NaN
|
|
# only for those variables
|
|
variable_has_nan = np.isnan(a).sum(axis=axisout)
|
|
|
|
a_ranked = np.apply_along_axis(rankdata, axisout, a)
|
|
rs = np.corrcoef(a_ranked, rowvar=axisout)
|
|
dof = n_obs - 2 # degrees of freedom
|
|
|
|
# rs can have elements equal to 1, so avoid zero division warnings
|
|
with np.errstate(divide='ignore'):
|
|
# clip the small negative values possibly caused by rounding
|
|
# errors before taking the square root
|
|
t = rs * np.sqrt((dof/((rs+1.0)*(1.0-rs))).clip(0))
|
|
|
|
prob = 2 * distributions.t.sf(np.abs(t), dof)
|
|
|
|
# For backwards compatibility, return scalars when comparing 2 columns
|
|
if rs.shape == (2, 2):
|
|
return SpearmanrResult(rs[1, 0], prob[1, 0])
|
|
else:
|
|
rs[variable_has_nan, :] = np.nan
|
|
rs[:, variable_has_nan] = np.nan
|
|
return SpearmanrResult(rs, prob)
|
|
|
|
|
|
PointbiserialrResult = namedtuple('PointbiserialrResult',
|
|
('correlation', 'pvalue'))
|
|
|
|
|
|
def pointbiserialr(x, y):
|
|
r"""
|
|
Calculate a point biserial correlation coefficient and its p-value.
|
|
|
|
The point biserial correlation is used to measure the relationship
|
|
between a binary variable, x, and a continuous variable, y. Like other
|
|
correlation coefficients, this one varies between -1 and +1 with 0
|
|
implying no correlation. Correlations of -1 or +1 imply a determinative
|
|
relationship.
|
|
|
|
This function uses a shortcut formula but produces the same result as
|
|
`pearsonr`.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like of bools
|
|
Input array.
|
|
y : array_like
|
|
Input array.
|
|
|
|
Returns
|
|
-------
|
|
correlation : float
|
|
R value.
|
|
pvalue : float
|
|
Two-sided p-value.
|
|
|
|
Notes
|
|
-----
|
|
`pointbiserialr` uses a t-test with ``n-1`` degrees of freedom.
|
|
It is equivalent to `pearsonr.`
|
|
|
|
The value of the point-biserial correlation can be calculated from:
|
|
|
|
.. math::
|
|
|
|
r_{pb} = \frac{\overline{Y_{1}} -
|
|
\overline{Y_{0}}}{s_{y}}\sqrt{\frac{N_{1} N_{2}}{N (N - 1))}}
|
|
|
|
Where :math:`Y_{0}` and :math:`Y_{1}` are means of the metric
|
|
observations coded 0 and 1 respectively; :math:`N_{0}` and :math:`N_{1}`
|
|
are number of observations coded 0 and 1 respectively; :math:`N` is the
|
|
total number of observations and :math:`s_{y}` is the standard
|
|
deviation of all the metric observations.
|
|
|
|
A value of :math:`r_{pb}` that is significantly different from zero is
|
|
completely equivalent to a significant difference in means between the two
|
|
groups. Thus, an independent groups t Test with :math:`N-2` degrees of
|
|
freedom may be used to test whether :math:`r_{pb}` is nonzero. The
|
|
relation between the t-statistic for comparing two independent groups and
|
|
:math:`r_{pb}` is given by:
|
|
|
|
.. math::
|
|
|
|
t = \sqrt{N - 2}\frac{r_{pb}}{\sqrt{1 - r^{2}_{pb}}}
|
|
|
|
References
|
|
----------
|
|
.. [1] J. Lev, "The Point Biserial Coefficient of Correlation", Ann. Math.
|
|
Statist., Vol. 20, no.1, pp. 125-126, 1949.
|
|
|
|
.. [2] R.F. Tate, "Correlation Between a Discrete and a Continuous
|
|
Variable. Point-Biserial Correlation.", Ann. Math. Statist., Vol. 25,
|
|
np. 3, pp. 603-607, 1954.
|
|
|
|
.. [3] D. Kornbrot "Point Biserial Correlation", In Wiley StatsRef:
|
|
Statistics Reference Online (eds N. Balakrishnan, et al.), 2014.
|
|
https://doi.org/10.1002/9781118445112.stat06227
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> a = np.array([0, 0, 0, 1, 1, 1, 1])
|
|
>>> b = np.arange(7)
|
|
>>> stats.pointbiserialr(a, b)
|
|
(0.8660254037844386, 0.011724811003954652)
|
|
>>> stats.pearsonr(a, b)
|
|
(0.86602540378443871, 0.011724811003954626)
|
|
>>> np.corrcoef(a, b)
|
|
array([[ 1. , 0.8660254],
|
|
[ 0.8660254, 1. ]])
|
|
|
|
"""
|
|
rpb, prob = pearsonr(x, y)
|
|
return PointbiserialrResult(rpb, prob)
|
|
|
|
|
|
KendalltauResult = namedtuple('KendalltauResult', ('correlation', 'pvalue'))
|
|
|
|
|
|
def kendalltau(x, y, initial_lexsort=None, nan_policy='propagate', method='auto'):
|
|
"""
|
|
Calculate Kendall's tau, a correlation measure for ordinal data.
|
|
|
|
Kendall's tau is a measure of the correspondence between two rankings.
|
|
Values close to 1 indicate strong agreement, values close to -1 indicate
|
|
strong disagreement. This is the 1945 "tau-b" version of Kendall's
|
|
tau [2]_, which can account for ties and which reduces to the 1938 "tau-a"
|
|
version [1]_ in absence of ties.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
Arrays of rankings, of the same shape. If arrays are not 1-D, they will
|
|
be flattened to 1-D.
|
|
initial_lexsort : bool, optional
|
|
Unused (deprecated).
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
method : {'auto', 'asymptotic', 'exact'}, optional
|
|
Defines which method is used to calculate the p-value [5]_.
|
|
The following options are available (default is 'auto'):
|
|
|
|
* 'auto': selects the appropriate method based on a trade-off between
|
|
speed and accuracy
|
|
* 'asymptotic': uses a normal approximation valid for large samples
|
|
* 'exact': computes the exact p-value, but can only be used if no ties
|
|
are present
|
|
|
|
Returns
|
|
-------
|
|
correlation : float
|
|
The tau statistic.
|
|
pvalue : float
|
|
The two-sided p-value for a hypothesis test whose null hypothesis is
|
|
an absence of association, tau = 0.
|
|
|
|
See Also
|
|
--------
|
|
spearmanr : Calculates a Spearman rank-order correlation coefficient.
|
|
theilslopes : Computes the Theil-Sen estimator for a set of points (x, y).
|
|
weightedtau : Computes a weighted version of Kendall's tau.
|
|
|
|
Notes
|
|
-----
|
|
The definition of Kendall's tau that is used is [2]_::
|
|
|
|
tau = (P - Q) / sqrt((P + Q + T) * (P + Q + U))
|
|
|
|
where P is the number of concordant pairs, Q the number of discordant
|
|
pairs, T the number of ties only in `x`, and U the number of ties only in
|
|
`y`. If a tie occurs for the same pair in both `x` and `y`, it is not
|
|
added to either T or U.
|
|
|
|
References
|
|
----------
|
|
.. [1] Maurice G. Kendall, "A New Measure of Rank Correlation", Biometrika
|
|
Vol. 30, No. 1/2, pp. 81-93, 1938.
|
|
.. [2] Maurice G. Kendall, "The treatment of ties in ranking problems",
|
|
Biometrika Vol. 33, No. 3, pp. 239-251. 1945.
|
|
.. [3] Gottfried E. Noether, "Elements of Nonparametric Statistics", John
|
|
Wiley & Sons, 1967.
|
|
.. [4] Peter M. Fenwick, "A new data structure for cumulative frequency
|
|
tables", Software: Practice and Experience, Vol. 24, No. 3,
|
|
pp. 327-336, 1994.
|
|
.. [5] Maurice G. Kendall, "Rank Correlation Methods" (4th Edition),
|
|
Charles Griffin & Co., 1970.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x1 = [12, 2, 1, 12, 2]
|
|
>>> x2 = [1, 4, 7, 1, 0]
|
|
>>> tau, p_value = stats.kendalltau(x1, x2)
|
|
>>> tau
|
|
-0.47140452079103173
|
|
>>> p_value
|
|
0.2827454599327748
|
|
|
|
"""
|
|
x = np.asarray(x).ravel()
|
|
y = np.asarray(y).ravel()
|
|
|
|
if x.size != y.size:
|
|
raise ValueError("All inputs to `kendalltau` must be of the same size, "
|
|
"found x-size %s and y-size %s" % (x.size, y.size))
|
|
elif not x.size or not y.size:
|
|
return KendalltauResult(np.nan, np.nan) # Return NaN if arrays are empty
|
|
|
|
# check both x and y
|
|
cnx, npx = _contains_nan(x, nan_policy)
|
|
cny, npy = _contains_nan(y, nan_policy)
|
|
contains_nan = cnx or cny
|
|
if npx == 'omit' or npy == 'omit':
|
|
nan_policy = 'omit'
|
|
|
|
if contains_nan and nan_policy == 'propagate':
|
|
return KendalltauResult(np.nan, np.nan)
|
|
|
|
elif contains_nan and nan_policy == 'omit':
|
|
x = ma.masked_invalid(x)
|
|
y = ma.masked_invalid(y)
|
|
return mstats_basic.kendalltau(x, y, method=method)
|
|
|
|
if initial_lexsort is not None: # deprecate to drop!
|
|
warnings.warn('"initial_lexsort" is gone!')
|
|
|
|
def count_rank_tie(ranks):
|
|
cnt = np.bincount(ranks).astype('int64', copy=False)
|
|
cnt = cnt[cnt > 1]
|
|
return ((cnt * (cnt - 1) // 2).sum(),
|
|
(cnt * (cnt - 1.) * (cnt - 2)).sum(),
|
|
(cnt * (cnt - 1.) * (2*cnt + 5)).sum())
|
|
|
|
size = x.size
|
|
perm = np.argsort(y) # sort on y and convert y to dense ranks
|
|
x, y = x[perm], y[perm]
|
|
y = np.r_[True, y[1:] != y[:-1]].cumsum(dtype=np.intp)
|
|
|
|
# stable sort on x and convert x to dense ranks
|
|
perm = np.argsort(x, kind='mergesort')
|
|
x, y = x[perm], y[perm]
|
|
x = np.r_[True, x[1:] != x[:-1]].cumsum(dtype=np.intp)
|
|
|
|
dis = _kendall_dis(x, y) # discordant pairs
|
|
|
|
obs = np.r_[True, (x[1:] != x[:-1]) | (y[1:] != y[:-1]), True]
|
|
cnt = np.diff(np.nonzero(obs)[0]).astype('int64', copy=False)
|
|
|
|
ntie = (cnt * (cnt - 1) // 2).sum() # joint ties
|
|
xtie, x0, x1 = count_rank_tie(x) # ties in x, stats
|
|
ytie, y0, y1 = count_rank_tie(y) # ties in y, stats
|
|
|
|
tot = (size * (size - 1)) // 2
|
|
|
|
if xtie == tot or ytie == tot:
|
|
return KendalltauResult(np.nan, np.nan)
|
|
|
|
# Note that tot = con + dis + (xtie - ntie) + (ytie - ntie) + ntie
|
|
# = con + dis + xtie + ytie - ntie
|
|
con_minus_dis = tot - xtie - ytie + ntie - 2 * dis
|
|
tau = con_minus_dis / np.sqrt(tot - xtie) / np.sqrt(tot - ytie)
|
|
# Limit range to fix computational errors
|
|
tau = min(1., max(-1., tau))
|
|
|
|
if method == 'exact' and (xtie != 0 or ytie != 0):
|
|
raise ValueError("Ties found, exact method cannot be used.")
|
|
|
|
if method == 'auto':
|
|
if (xtie == 0 and ytie == 0) and (size <= 33 or min(dis, tot-dis) <= 1):
|
|
method = 'exact'
|
|
else:
|
|
method = 'asymptotic'
|
|
|
|
if xtie == 0 and ytie == 0 and method == 'exact':
|
|
# Exact p-value, see p. 68 of Maurice G. Kendall, "Rank Correlation Methods" (4th Edition), Charles Griffin & Co., 1970.
|
|
c = min(dis, tot-dis)
|
|
if size <= 0:
|
|
raise ValueError
|
|
elif c < 0 or 2*c > size*(size-1):
|
|
raise ValueError
|
|
elif size == 1:
|
|
pvalue = 1.0
|
|
elif size == 2:
|
|
pvalue = 1.0
|
|
elif c == 0:
|
|
pvalue = 2.0/float_factorial(size)
|
|
elif c == 1:
|
|
pvalue = 2.0/float_factorial(size-1)
|
|
elif 2*c == tot:
|
|
pvalue = 1.0
|
|
else:
|
|
new = [0.0]*(c+1)
|
|
new[0] = 1.0
|
|
new[1] = 1.0
|
|
for j in range(3,size+1):
|
|
old = new[:]
|
|
for k in range(1,min(j,c+1)):
|
|
new[k] += new[k-1]
|
|
for k in range(j,c+1):
|
|
new[k] += new[k-1] - old[k-j]
|
|
|
|
pvalue = 2.0*sum(new)/float_factorial(size)
|
|
|
|
elif method == 'asymptotic':
|
|
# con_minus_dis is approx normally distributed with this variance [3]_
|
|
var = (size * (size - 1) * (2.*size + 5) - x1 - y1) / 18. + (
|
|
2. * xtie * ytie) / (size * (size - 1)) + x0 * y0 / (9. *
|
|
size * (size - 1) * (size - 2))
|
|
pvalue = special.erfc(np.abs(con_minus_dis) / np.sqrt(var) / np.sqrt(2))
|
|
else:
|
|
raise ValueError("Unknown method "+str(method)+" specified, please use auto, exact or asymptotic.")
|
|
|
|
return KendalltauResult(tau, pvalue)
|
|
|
|
|
|
WeightedTauResult = namedtuple('WeightedTauResult', ('correlation', 'pvalue'))
|
|
|
|
|
|
def weightedtau(x, y, rank=True, weigher=None, additive=True):
|
|
r"""
|
|
Compute a weighted version of Kendall's :math:`\tau`.
|
|
|
|
The weighted :math:`\tau` is a weighted version of Kendall's
|
|
:math:`\tau` in which exchanges of high weight are more influential than
|
|
exchanges of low weight. The default parameters compute the additive
|
|
hyperbolic version of the index, :math:`\tau_\mathrm h`, which has
|
|
been shown to provide the best balance between important and
|
|
unimportant elements [1]_.
|
|
|
|
The weighting is defined by means of a rank array, which assigns a
|
|
nonnegative rank to each element, and a weigher function, which
|
|
assigns a weight based from the rank to each element. The weight of an
|
|
exchange is then the sum or the product of the weights of the ranks of
|
|
the exchanged elements. The default parameters compute
|
|
:math:`\tau_\mathrm h`: an exchange between elements with rank
|
|
:math:`r` and :math:`s` (starting from zero) has weight
|
|
:math:`1/(r+1) + 1/(s+1)`.
|
|
|
|
Specifying a rank array is meaningful only if you have in mind an
|
|
external criterion of importance. If, as it usually happens, you do
|
|
not have in mind a specific rank, the weighted :math:`\tau` is
|
|
defined by averaging the values obtained using the decreasing
|
|
lexicographical rank by (`x`, `y`) and by (`y`, `x`). This is the
|
|
behavior with default parameters.
|
|
|
|
Note that if you are computing the weighted :math:`\tau` on arrays of
|
|
ranks, rather than of scores (i.e., a larger value implies a lower
|
|
rank) you must negate the ranks, so that elements of higher rank are
|
|
associated with a larger value.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
Arrays of scores, of the same shape. If arrays are not 1-D, they will
|
|
be flattened to 1-D.
|
|
rank : array_like of ints or bool, optional
|
|
A nonnegative rank assigned to each element. If it is None, the
|
|
decreasing lexicographical rank by (`x`, `y`) will be used: elements of
|
|
higher rank will be those with larger `x`-values, using `y`-values to
|
|
break ties (in particular, swapping `x` and `y` will give a different
|
|
result). If it is False, the element indices will be used
|
|
directly as ranks. The default is True, in which case this
|
|
function returns the average of the values obtained using the
|
|
decreasing lexicographical rank by (`x`, `y`) and by (`y`, `x`).
|
|
weigher : callable, optional
|
|
The weigher function. Must map nonnegative integers (zero
|
|
representing the most important element) to a nonnegative weight.
|
|
The default, None, provides hyperbolic weighing, that is,
|
|
rank :math:`r` is mapped to weight :math:`1/(r+1)`.
|
|
additive : bool, optional
|
|
If True, the weight of an exchange is computed by adding the
|
|
weights of the ranks of the exchanged elements; otherwise, the weights
|
|
are multiplied. The default is True.
|
|
|
|
Returns
|
|
-------
|
|
correlation : float
|
|
The weighted :math:`\tau` correlation index.
|
|
pvalue : float
|
|
Presently ``np.nan``, as the null statistics is unknown (even in the
|
|
additive hyperbolic case).
|
|
|
|
See Also
|
|
--------
|
|
kendalltau : Calculates Kendall's tau.
|
|
spearmanr : Calculates a Spearman rank-order correlation coefficient.
|
|
theilslopes : Computes the Theil-Sen estimator for a set of points (x, y).
|
|
|
|
Notes
|
|
-----
|
|
This function uses an :math:`O(n \log n)`, mergesort-based algorithm
|
|
[1]_ that is a weighted extension of Knight's algorithm for Kendall's
|
|
:math:`\tau` [2]_. It can compute Shieh's weighted :math:`\tau` [3]_
|
|
between rankings without ties (i.e., permutations) by setting
|
|
`additive` and `rank` to False, as the definition given in [1]_ is a
|
|
generalization of Shieh's.
|
|
|
|
NaNs are considered the smallest possible score.
|
|
|
|
.. versionadded:: 0.19.0
|
|
|
|
References
|
|
----------
|
|
.. [1] Sebastiano Vigna, "A weighted correlation index for rankings with
|
|
ties", Proceedings of the 24th international conference on World
|
|
Wide Web, pp. 1166-1176, ACM, 2015.
|
|
.. [2] W.R. Knight, "A Computer Method for Calculating Kendall's Tau with
|
|
Ungrouped Data", Journal of the American Statistical Association,
|
|
Vol. 61, No. 314, Part 1, pp. 436-439, 1966.
|
|
.. [3] Grace S. Shieh. "A weighted Kendall's tau statistic", Statistics &
|
|
Probability Letters, Vol. 39, No. 1, pp. 17-24, 1998.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = [12, 2, 1, 12, 2]
|
|
>>> y = [1, 4, 7, 1, 0]
|
|
>>> tau, p_value = stats.weightedtau(x, y)
|
|
>>> tau
|
|
-0.56694968153682723
|
|
>>> p_value
|
|
nan
|
|
>>> tau, p_value = stats.weightedtau(x, y, additive=False)
|
|
>>> tau
|
|
-0.62205716951801038
|
|
|
|
NaNs are considered the smallest possible score:
|
|
|
|
>>> x = [12, 2, 1, 12, 2]
|
|
>>> y = [1, 4, 7, 1, np.nan]
|
|
>>> tau, _ = stats.weightedtau(x, y)
|
|
>>> tau
|
|
-0.56694968153682723
|
|
|
|
This is exactly Kendall's tau:
|
|
|
|
>>> x = [12, 2, 1, 12, 2]
|
|
>>> y = [1, 4, 7, 1, 0]
|
|
>>> tau, _ = stats.weightedtau(x, y, weigher=lambda x: 1)
|
|
>>> tau
|
|
-0.47140452079103173
|
|
|
|
>>> x = [12, 2, 1, 12, 2]
|
|
>>> y = [1, 4, 7, 1, 0]
|
|
>>> stats.weightedtau(x, y, rank=None)
|
|
WeightedTauResult(correlation=-0.4157652301037516, pvalue=nan)
|
|
>>> stats.weightedtau(y, x, rank=None)
|
|
WeightedTauResult(correlation=-0.7181341329699028, pvalue=nan)
|
|
|
|
"""
|
|
x = np.asarray(x).ravel()
|
|
y = np.asarray(y).ravel()
|
|
|
|
if x.size != y.size:
|
|
raise ValueError("All inputs to `weightedtau` must be of the same size, "
|
|
"found x-size %s and y-size %s" % (x.size, y.size))
|
|
if not x.size:
|
|
return WeightedTauResult(np.nan, np.nan) # Return NaN if arrays are empty
|
|
|
|
# If there are NaNs we apply _toint64()
|
|
if np.isnan(np.sum(x)):
|
|
x = _toint64(x)
|
|
if np.isnan(np.sum(x)):
|
|
y = _toint64(y)
|
|
|
|
# Reduce to ranks unsupported types
|
|
if x.dtype != y.dtype:
|
|
if x.dtype != np.int64:
|
|
x = _toint64(x)
|
|
if y.dtype != np.int64:
|
|
y = _toint64(y)
|
|
else:
|
|
if x.dtype not in (np.int32, np.int64, np.float32, np.float64):
|
|
x = _toint64(x)
|
|
y = _toint64(y)
|
|
|
|
if rank is True:
|
|
return WeightedTauResult((
|
|
_weightedrankedtau(x, y, None, weigher, additive) +
|
|
_weightedrankedtau(y, x, None, weigher, additive)
|
|
) / 2, np.nan)
|
|
|
|
if rank is False:
|
|
rank = np.arange(x.size, dtype=np.intp)
|
|
elif rank is not None:
|
|
rank = np.asarray(rank).ravel()
|
|
if rank.size != x.size:
|
|
raise ValueError("All inputs to `weightedtau` must be of the same size, "
|
|
"found x-size %s and rank-size %s" % (x.size, rank.size))
|
|
|
|
return WeightedTauResult(_weightedrankedtau(x, y, rank, weigher, additive), np.nan)
|
|
|
|
|
|
# FROM MGCPY: https://github.com/neurodata/mgcpy
|
|
|
|
class _ParallelP(object):
|
|
"""
|
|
Helper function to calculate parallel p-value.
|
|
"""
|
|
def __init__(self, x, y, random_states):
|
|
self.x = x
|
|
self.y = y
|
|
self.random_states = random_states
|
|
|
|
def __call__(self, index):
|
|
order = self.random_states[index].permutation(self.y.shape[0])
|
|
permy = self.y[order][:, order]
|
|
|
|
# calculate permuted stats, store in null distribution
|
|
perm_stat = _mgc_stat(self.x, permy)[0]
|
|
|
|
return perm_stat
|
|
|
|
|
|
def _perm_test(x, y, stat, reps=1000, workers=-1, random_state=None):
|
|
r"""
|
|
Helper function that calculates the p-value. See below for uses.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : ndarray
|
|
`x` and `y` have shapes `(n, p)` and `(n, q)`.
|
|
stat : float
|
|
The sample test statistic.
|
|
reps : int, optional
|
|
The number of replications used to estimate the null when using the
|
|
permutation test. The default is 1000 replications.
|
|
workers : int or map-like callable, optional
|
|
If `workers` is an int the population is subdivided into `workers`
|
|
sections and evaluated in parallel (uses
|
|
`multiprocessing.Pool <multiprocessing>`). Supply `-1` to use all cores
|
|
available to the Process. Alternatively supply a map-like callable,
|
|
such as `multiprocessing.Pool.map` for evaluating the population in
|
|
parallel. This evaluation is carried out as `workers(func, iterable)`.
|
|
Requires that `func` be pickleable.
|
|
random_state : int or np.random.RandomState instance, optional
|
|
If already a RandomState instance, use it.
|
|
If seed is an int, return a new RandomState instance seeded with seed.
|
|
If None, use np.random.RandomState. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
pvalue : float
|
|
The sample test p-value.
|
|
null_dist : list
|
|
The approximated null distribution.
|
|
"""
|
|
# generate seeds for each rep (change to new parallel random number
|
|
# capabilities in numpy >= 1.17+)
|
|
random_state = check_random_state(random_state)
|
|
random_states = [np.random.RandomState(rng_integers(random_state, 1 << 32,
|
|
size=4, dtype=np.uint32)) for _ in range(reps)]
|
|
|
|
# parallelizes with specified workers over number of reps and set seeds
|
|
mapwrapper = MapWrapper(workers)
|
|
parallelp = _ParallelP(x=x, y=y, random_states=random_states)
|
|
null_dist = np.array(list(mapwrapper(parallelp, range(reps))))
|
|
|
|
# calculate p-value and significant permutation map through list
|
|
pvalue = (null_dist >= stat).sum() / reps
|
|
|
|
# correct for a p-value of 0. This is because, with bootstrapping
|
|
# permutations, a p-value of 0 is incorrect
|
|
if pvalue == 0:
|
|
pvalue = 1 / reps
|
|
|
|
return pvalue, null_dist
|
|
|
|
|
|
def _euclidean_dist(x):
|
|
return cdist(x, x)
|
|
|
|
|
|
MGCResult = namedtuple('MGCResult', ('stat', 'pvalue', 'mgc_dict'))
|
|
|
|
|
|
def multiscale_graphcorr(x, y, compute_distance=_euclidean_dist, reps=1000,
|
|
workers=1, is_twosamp=False, random_state=None):
|
|
r"""
|
|
Computes the Multiscale Graph Correlation (MGC) test statistic.
|
|
|
|
Specifically, for each point, MGC finds the :math:`k`-nearest neighbors for
|
|
one property (e.g. cloud density), and the :math:`l`-nearest neighbors for
|
|
the other property (e.g. grass wetness) [1]_. This pair :math:`(k, l)` is
|
|
called the "scale". A priori, however, it is not know which scales will be
|
|
most informative. So, MGC computes all distance pairs, and then efficiently
|
|
computes the distance correlations for all scales. The local correlations
|
|
illustrate which scales are relatively informative about the relationship.
|
|
The key, therefore, to successfully discover and decipher relationships
|
|
between disparate data modalities is to adaptively determine which scales
|
|
are the most informative, and the geometric implication for the most
|
|
informative scales. Doing so not only provides an estimate of whether the
|
|
modalities are related, but also provides insight into how the
|
|
determination was made. This is especially important in high-dimensional
|
|
data, where simple visualizations do not reveal relationships to the
|
|
unaided human eye. Characterizations of this implementation in particular
|
|
have been derived from and benchmarked within in [2]_.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : ndarray
|
|
If ``x`` and ``y`` have shapes ``(n, p)`` and ``(n, q)`` where `n` is
|
|
the number of samples and `p` and `q` are the number of dimensions,
|
|
then the MGC independence test will be run. Alternatively, ``x`` and
|
|
``y`` can have shapes ``(n, n)`` if they are distance or similarity
|
|
matrices, and ``compute_distance`` must be sent to ``None``. If ``x``
|
|
and ``y`` have shapes ``(n, p)`` and ``(m, p)``, an unpaired
|
|
two-sample MGC test will be run.
|
|
compute_distance : callable, optional
|
|
A function that computes the distance or similarity among the samples
|
|
within each data matrix. Set to ``None`` if ``x`` and ``y`` are
|
|
already distance matrices. The default uses the euclidean norm metric.
|
|
If you are calling a custom function, either create the distance
|
|
matrix before-hand or create a function of the form
|
|
``compute_distance(x)`` where `x` is the data matrix for which
|
|
pairwise distances are calculated.
|
|
reps : int, optional
|
|
The number of replications used to estimate the null when using the
|
|
permutation test. The default is ``1000``.
|
|
workers : int or map-like callable, optional
|
|
If ``workers`` is an int the population is subdivided into ``workers``
|
|
sections and evaluated in parallel (uses ``multiprocessing.Pool
|
|
<multiprocessing>``). Supply ``-1`` to use all cores available to the
|
|
Process. Alternatively supply a map-like callable, such as
|
|
``multiprocessing.Pool.map`` for evaluating the p-value in parallel.
|
|
This evaluation is carried out as ``workers(func, iterable)``.
|
|
Requires that `func` be pickleable. The default is ``1``.
|
|
is_twosamp : bool, optional
|
|
If `True`, a two sample test will be run. If ``x`` and ``y`` have
|
|
shapes ``(n, p)`` and ``(m, p)``, this optional will be overriden and
|
|
set to ``True``. Set to ``True`` if ``x`` and ``y`` both have shapes
|
|
``(n, p)`` and a two sample test is desired. The default is ``False``.
|
|
Note that this will not run if inputs are distance matrices.
|
|
random_state : int or np.random.RandomState instance, optional
|
|
If already a RandomState instance, use it.
|
|
If seed is an int, return a new RandomState instance seeded with seed.
|
|
If None, use np.random.RandomState. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
stat : float
|
|
The sample MGC test statistic within `[-1, 1]`.
|
|
pvalue : float
|
|
The p-value obtained via permutation.
|
|
mgc_dict : dict
|
|
Contains additional useful additional returns containing the following
|
|
keys:
|
|
|
|
- mgc_map : ndarray
|
|
A 2D representation of the latent geometry of the relationship.
|
|
of the relationship.
|
|
- opt_scale : (int, int)
|
|
The estimated optimal scale as a `(x, y)` pair.
|
|
- null_dist : list
|
|
The null distribution derived from the permuted matrices
|
|
|
|
See Also
|
|
--------
|
|
pearsonr : Pearson correlation coefficient and p-value for testing
|
|
non-correlation.
|
|
kendalltau : Calculates Kendall's tau.
|
|
spearmanr : Calculates a Spearman rank-order correlation coefficient.
|
|
|
|
Notes
|
|
-----
|
|
A description of the process of MGC and applications on neuroscience data
|
|
can be found in [1]_. It is performed using the following steps:
|
|
|
|
#. Two distance matrices :math:`D^X` and :math:`D^Y` are computed and
|
|
modified to be mean zero columnwise. This results in two
|
|
:math:`n \times n` distance matrices :math:`A` and :math:`B` (the
|
|
centering and unbiased modification) [3]_.
|
|
|
|
#. For all values :math:`k` and :math:`l` from :math:`1, ..., n`,
|
|
|
|
* The :math:`k`-nearest neighbor and :math:`l`-nearest neighbor graphs
|
|
are calculated for each property. Here, :math:`G_k (i, j)` indicates
|
|
the :math:`k`-smallest values of the :math:`i`-th row of :math:`A`
|
|
and :math:`H_l (i, j)` indicates the :math:`l` smallested values of
|
|
the :math:`i`-th row of :math:`B`
|
|
|
|
* Let :math:`\circ` denotes the entry-wise matrix product, then local
|
|
correlations are summed and normalized using the following statistic:
|
|
|
|
.. math::
|
|
|
|
c^{kl} = \frac{\sum_{ij} A G_k B H_l}
|
|
{\sqrt{\sum_{ij} A^2 G_k \times \sum_{ij} B^2 H_l}}
|
|
|
|
#. The MGC test statistic is the smoothed optimal local correlation of
|
|
:math:`\{ c^{kl} \}`. Denote the smoothing operation as :math:`R(\cdot)`
|
|
(which essentially set all isolated large correlations) as 0 and
|
|
connected large correlations the same as before, see [3]_.) MGC is,
|
|
|
|
.. math::
|
|
|
|
MGC_n (x, y) = \max_{(k, l)} R \left(c^{kl} \left( x_n, y_n \right)
|
|
\right)
|
|
|
|
The test statistic returns a value between :math:`(-1, 1)` since it is
|
|
normalized.
|
|
|
|
The p-value returned is calculated using a permutation test. This process
|
|
is completed by first randomly permuting :math:`y` to estimate the null
|
|
distribution and then calculating the probability of observing a test
|
|
statistic, under the null, at least as extreme as the observed test
|
|
statistic.
|
|
|
|
MGC requires at least 5 samples to run with reliable results. It can also
|
|
handle high-dimensional data sets.
|
|
In addition, by manipulating the input data matrices, the two-sample
|
|
testing problem can be reduced to the independence testing problem [4]_.
|
|
Given sample data :math:`U` and :math:`V` of sizes :math:`p \times n`
|
|
:math:`p \times m`, data matrix :math:`X` and :math:`Y` can be created as
|
|
follows:
|
|
|
|
.. math::
|
|
|
|
X = [U | V] \in \mathcal{R}^{p \times (n + m)}
|
|
Y = [0_{1 \times n} | 1_{1 \times m}] \in \mathcal{R}^{(n + m)}
|
|
|
|
Then, the MGC statistic can be calculated as normal. This methodology can
|
|
be extended to similar tests such as distance correlation [4]_.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
References
|
|
----------
|
|
.. [1] Vogelstein, J. T., Bridgeford, E. W., Wang, Q., Priebe, C. E.,
|
|
Maggioni, M., & Shen, C. (2019). Discovering and deciphering
|
|
relationships across disparate data modalities. ELife.
|
|
.. [2] Panda, S., Palaniappan, S., Xiong, J., Swaminathan, A.,
|
|
Ramachandran, S., Bridgeford, E. W., ... Vogelstein, J. T. (2019).
|
|
mgcpy: A Comprehensive High Dimensional Independence Testing Python
|
|
Package. ArXiv:1907.02088 [Cs, Stat].
|
|
.. [3] Shen, C., Priebe, C.E., & Vogelstein, J. T. (2019). From distance
|
|
correlation to multiscale graph correlation. Journal of the American
|
|
Statistical Association.
|
|
.. [4] Shen, C. & Vogelstein, J. T. (2018). The Exact Equivalence of
|
|
Distance and Kernel Methods for Hypothesis Testing. ArXiv:1806.05514
|
|
[Cs, Stat].
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import multiscale_graphcorr
|
|
>>> x = np.arange(100)
|
|
>>> y = x
|
|
>>> stat, pvalue, _ = multiscale_graphcorr(x, y, workers=-1)
|
|
>>> '%.1f, %.3f' % (stat, pvalue)
|
|
'1.0, 0.001'
|
|
|
|
Alternatively,
|
|
|
|
>>> x = np.arange(100)
|
|
>>> y = x
|
|
>>> mgc = multiscale_graphcorr(x, y)
|
|
>>> '%.1f, %.3f' % (mgc.stat, mgc.pvalue)
|
|
'1.0, 0.001'
|
|
|
|
To run an unpaired two-sample test,
|
|
|
|
>>> x = np.arange(100)
|
|
>>> y = np.arange(79)
|
|
>>> mgc = multiscale_graphcorr(x, y, random_state=1)
|
|
>>> '%.3f, %.2f' % (mgc.stat, mgc.pvalue)
|
|
'0.033, 0.02'
|
|
|
|
or, if shape of the inputs are the same,
|
|
|
|
>>> x = np.arange(100)
|
|
>>> y = x
|
|
>>> mgc = multiscale_graphcorr(x, y, is_twosamp=True)
|
|
>>> '%.3f, %.1f' % (mgc.stat, mgc.pvalue)
|
|
'-0.008, 1.0'
|
|
"""
|
|
if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray):
|
|
raise ValueError("x and y must be ndarrays")
|
|
|
|
# convert arrays of type (n,) to (n, 1)
|
|
if x.ndim == 1:
|
|
x = x[:, np.newaxis]
|
|
elif x.ndim != 2:
|
|
raise ValueError("Expected a 2-D array `x`, found shape "
|
|
"{}".format(x.shape))
|
|
if y.ndim == 1:
|
|
y = y[:, np.newaxis]
|
|
elif y.ndim != 2:
|
|
raise ValueError("Expected a 2-D array `y`, found shape "
|
|
"{}".format(y.shape))
|
|
|
|
nx, px = x.shape
|
|
ny, py = y.shape
|
|
|
|
# check for NaNs
|
|
_contains_nan(x, nan_policy='raise')
|
|
_contains_nan(y, nan_policy='raise')
|
|
|
|
# check for positive or negative infinity and raise error
|
|
if np.sum(np.isinf(x)) > 0 or np.sum(np.isinf(y)) > 0:
|
|
raise ValueError("Inputs contain infinities")
|
|
|
|
if nx != ny:
|
|
if px == py:
|
|
# reshape x and y for two sample testing
|
|
is_twosamp = True
|
|
else:
|
|
raise ValueError("Shape mismatch, x and y must have shape [n, p] "
|
|
"and [n, q] or have shape [n, p] and [m, p].")
|
|
|
|
if nx < 5 or ny < 5:
|
|
raise ValueError("MGC requires at least 5 samples to give reasonable "
|
|
"results.")
|
|
|
|
# convert x and y to float
|
|
x = x.astype(np.float64)
|
|
y = y.astype(np.float64)
|
|
|
|
# check if compute_distance_matrix if a callable()
|
|
if not callable(compute_distance) and compute_distance is not None:
|
|
raise ValueError("Compute_distance must be a function.")
|
|
|
|
# check if number of reps exists, integer, or > 0 (if under 1000 raises
|
|
# warning)
|
|
if not isinstance(reps, int) or reps < 0:
|
|
raise ValueError("Number of reps must be an integer greater than 0.")
|
|
elif reps < 1000:
|
|
msg = ("The number of replications is low (under 1000), and p-value "
|
|
"calculations may be unreliable. Use the p-value result, with "
|
|
"caution!")
|
|
warnings.warn(msg, RuntimeWarning)
|
|
|
|
if is_twosamp:
|
|
if compute_distance is None:
|
|
raise ValueError("Cannot run if inputs are distance matrices")
|
|
x, y = _two_sample_transform(x, y)
|
|
|
|
if compute_distance is not None:
|
|
# compute distance matrices for x and y
|
|
x = compute_distance(x)
|
|
y = compute_distance(y)
|
|
|
|
# calculate MGC stat
|
|
stat, stat_dict = _mgc_stat(x, y)
|
|
stat_mgc_map = stat_dict["stat_mgc_map"]
|
|
opt_scale = stat_dict["opt_scale"]
|
|
|
|
# calculate permutation MGC p-value
|
|
pvalue, null_dist = _perm_test(x, y, stat, reps=reps, workers=workers,
|
|
random_state=random_state)
|
|
|
|
# save all stats (other than stat/p-value) in dictionary
|
|
mgc_dict = {"mgc_map": stat_mgc_map,
|
|
"opt_scale": opt_scale,
|
|
"null_dist": null_dist}
|
|
|
|
return MGCResult(stat, pvalue, mgc_dict)
|
|
|
|
|
|
def _mgc_stat(distx, disty):
|
|
r"""
|
|
Helper function that calculates the MGC stat. See above for use.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : ndarray
|
|
`x` and `y` have shapes `(n, p)` and `(n, q)` or `(n, n)` and `(n, n)`
|
|
if distance matrices.
|
|
|
|
Returns
|
|
-------
|
|
stat : float
|
|
The sample MGC test statistic within `[-1, 1]`.
|
|
stat_dict : dict
|
|
Contains additional useful additional returns containing the following
|
|
keys:
|
|
|
|
- stat_mgc_map : ndarray
|
|
MGC-map of the statistics.
|
|
- opt_scale : (float, float)
|
|
The estimated optimal scale as a `(x, y)` pair.
|
|
"""
|
|
# calculate MGC map and optimal scale
|
|
stat_mgc_map = _local_correlations(distx, disty, global_corr='mgc')
|
|
|
|
n, m = stat_mgc_map.shape
|
|
if m == 1 or n == 1:
|
|
# the global scale at is the statistic calculated at maximial nearest
|
|
# neighbors. There is not enough local scale to search over, so
|
|
# default to global scale
|
|
stat = stat_mgc_map[m - 1][n - 1]
|
|
opt_scale = m * n
|
|
else:
|
|
samp_size = len(distx) - 1
|
|
|
|
# threshold to find connected region of significant local correlations
|
|
sig_connect = _threshold_mgc_map(stat_mgc_map, samp_size)
|
|
|
|
# maximum within the significant region
|
|
stat, opt_scale = _smooth_mgc_map(sig_connect, stat_mgc_map)
|
|
|
|
stat_dict = {"stat_mgc_map": stat_mgc_map,
|
|
"opt_scale": opt_scale}
|
|
|
|
return stat, stat_dict
|
|
|
|
|
|
def _threshold_mgc_map(stat_mgc_map, samp_size):
|
|
r"""
|
|
Finds a connected region of significance in the MGC-map by thresholding.
|
|
|
|
Parameters
|
|
----------
|
|
stat_mgc_map : ndarray
|
|
All local correlations within `[-1,1]`.
|
|
samp_size : int
|
|
The sample size of original data.
|
|
|
|
Returns
|
|
-------
|
|
sig_connect : ndarray
|
|
A binary matrix with 1's indicating the significant region.
|
|
"""
|
|
m, n = stat_mgc_map.shape
|
|
|
|
# 0.02 is simply an empirical threshold, this can be set to 0.01 or 0.05
|
|
# with varying levels of performance. Threshold is based on a beta
|
|
# approximation.
|
|
per_sig = 1 - (0.02 / samp_size) # Percentile to consider as significant
|
|
threshold = samp_size * (samp_size - 3)/4 - 1/2 # Beta approximation
|
|
threshold = distributions.beta.ppf(per_sig, threshold, threshold) * 2 - 1
|
|
|
|
# the global scale at is the statistic calculated at maximial nearest
|
|
# neighbors. Threshold is the maximium on the global and local scales
|
|
threshold = max(threshold, stat_mgc_map[m - 1][n - 1])
|
|
|
|
# find the largest connected component of significant correlations
|
|
sig_connect = stat_mgc_map > threshold
|
|
if np.sum(sig_connect) > 0:
|
|
sig_connect, _ = measurements.label(sig_connect)
|
|
_, label_counts = np.unique(sig_connect, return_counts=True)
|
|
|
|
# skip the first element in label_counts, as it is count(zeros)
|
|
max_label = np.argmax(label_counts[1:]) + 1
|
|
sig_connect = sig_connect == max_label
|
|
else:
|
|
sig_connect = np.array([[False]])
|
|
|
|
return sig_connect
|
|
|
|
|
|
def _smooth_mgc_map(sig_connect, stat_mgc_map):
|
|
"""
|
|
Finds the smoothed maximal within the significant region R.
|
|
If area of R is too small it returns the last local correlation. Otherwise,
|
|
returns the maximum within significant_connected_region.
|
|
|
|
Parameters
|
|
----------
|
|
sig_connect: ndarray
|
|
A binary matrix with 1's indicating the significant region.
|
|
stat_mgc_map: ndarray
|
|
All local correlations within `[-1, 1]`.
|
|
|
|
Returns
|
|
-------
|
|
stat : float
|
|
The sample MGC statistic within `[-1, 1]`.
|
|
opt_scale: (float, float)
|
|
The estimated optimal scale as an `(x, y)` pair.
|
|
"""
|
|
|
|
m, n = stat_mgc_map.shape
|
|
|
|
# the global scale at is the statistic calculated at maximial nearest
|
|
# neighbors. By default, statistic and optimal scale are global.
|
|
stat = stat_mgc_map[m - 1][n - 1]
|
|
opt_scale = [m, n]
|
|
|
|
if np.linalg.norm(sig_connect) != 0:
|
|
# proceed only when the connected region's area is sufficiently large
|
|
# 0.02 is simply an empirical threshold, this can be set to 0.01 or 0.05
|
|
# with varying levels of performance
|
|
if np.sum(sig_connect) >= np.ceil(0.02 * max(m, n)) * min(m, n):
|
|
max_corr = max(stat_mgc_map[sig_connect])
|
|
|
|
# find all scales within significant_connected_region that maximize
|
|
# the local correlation
|
|
max_corr_index = np.where((stat_mgc_map >= max_corr) & sig_connect)
|
|
|
|
if max_corr >= stat:
|
|
stat = max_corr
|
|
|
|
k, l = max_corr_index
|
|
one_d_indices = k * n + l # 2D to 1D indexing
|
|
k = np.max(one_d_indices) // n
|
|
l = np.max(one_d_indices) % n
|
|
opt_scale = [k+1, l+1] # adding 1s to match R indexing
|
|
|
|
return stat, opt_scale
|
|
|
|
|
|
def _two_sample_transform(u, v):
|
|
"""
|
|
Helper function that concatenates x and y for two sample MGC stat. See
|
|
above for use.
|
|
|
|
Parameters
|
|
----------
|
|
u, v : ndarray
|
|
`u` and `v` have shapes `(n, p)` and `(m, p)`.
|
|
|
|
Returns
|
|
-------
|
|
x : ndarray
|
|
Concatenate `u` and `v` along the `axis = 0`. `x` thus has shape
|
|
`(2n, p)`.
|
|
y : ndarray
|
|
Label matrix for `x` where 0 refers to samples that comes from `u` and
|
|
1 refers to samples that come from `v`. `y` thus has shape `(2n, 1)`.
|
|
"""
|
|
nx = u.shape[0]
|
|
ny = v.shape[0]
|
|
x = np.concatenate([u, v], axis=0)
|
|
y = np.concatenate([np.zeros(nx), np.ones(ny)], axis=0).reshape(-1, 1)
|
|
return x, y
|
|
|
|
|
|
#####################################
|
|
# INFERENTIAL STATISTICS #
|
|
#####################################
|
|
|
|
Ttest_1sampResult = namedtuple('Ttest_1sampResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def ttest_1samp(a, popmean, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Calculate the T-test for the mean of ONE group of scores.
|
|
|
|
This is a two-sided test for the null hypothesis that the expected value
|
|
(mean) of a sample of independent observations `a` is equal to the given
|
|
population mean, `popmean`.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Sample observation.
|
|
popmean : float or array_like
|
|
Expected value in null hypothesis. If array_like, then it must have the
|
|
same shape as `a` excluding the axis dimension.
|
|
axis : int or None, optional
|
|
Axis along which to compute test. If None, compute over the whole
|
|
array `a`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float or array
|
|
t-statistic.
|
|
pvalue : float or array
|
|
Two-sided p-value.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
|
|
>>> np.random.seed(7654567) # fix seed to get the same result
|
|
>>> rvs = stats.norm.rvs(loc=5, scale=10, size=(50,2))
|
|
|
|
Test if mean of random sample is equal to true mean, and different mean.
|
|
We reject the null hypothesis in the second case and don't reject it in
|
|
the first case.
|
|
|
|
>>> stats.ttest_1samp(rvs,5.0)
|
|
(array([-0.68014479, -0.04323899]), array([ 0.49961383, 0.96568674]))
|
|
>>> stats.ttest_1samp(rvs,0.0)
|
|
(array([ 2.77025808, 4.11038784]), array([ 0.00789095, 0.00014999]))
|
|
|
|
Examples using axis and non-scalar dimension for population mean.
|
|
|
|
>>> stats.ttest_1samp(rvs,[5.0,0.0])
|
|
(array([-0.68014479, 4.11038784]), array([ 4.99613833e-01, 1.49986458e-04]))
|
|
>>> stats.ttest_1samp(rvs.T,[5.0,0.0],axis=1)
|
|
(array([-0.68014479, 4.11038784]), array([ 4.99613833e-01, 1.49986458e-04]))
|
|
>>> stats.ttest_1samp(rvs,[[5.0],[0.0]])
|
|
(array([[-0.68014479, -0.04323899],
|
|
[ 2.77025808, 4.11038784]]), array([[ 4.99613833e-01, 9.65686743e-01],
|
|
[ 7.89094663e-03, 1.49986458e-04]]))
|
|
|
|
"""
|
|
a, axis = _chk_asarray(a, axis)
|
|
|
|
contains_nan, nan_policy = _contains_nan(a, nan_policy)
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.ttest_1samp(a, popmean, axis)
|
|
|
|
n = a.shape[axis]
|
|
df = n - 1
|
|
|
|
d = np.mean(a, axis) - popmean
|
|
v = np.var(a, axis, ddof=1)
|
|
denom = np.sqrt(v / n)
|
|
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|
t = np.divide(d, denom)
|
|
t, prob = _ttest_finish(df, t)
|
|
|
|
return Ttest_1sampResult(t, prob)
|
|
|
|
|
|
def _ttest_finish(df, t):
|
|
"""Common code between all 3 t-test functions."""
|
|
prob = distributions.t.sf(np.abs(t), df) * 2 # use np.abs to get upper tail
|
|
if t.ndim == 0:
|
|
t = t[()]
|
|
|
|
return t, prob
|
|
|
|
|
|
def _ttest_ind_from_stats(mean1, mean2, denom, df):
|
|
|
|
d = mean1 - mean2
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|
t = np.divide(d, denom)
|
|
t, prob = _ttest_finish(df, t)
|
|
|
|
return (t, prob)
|
|
|
|
|
|
def _unequal_var_ttest_denom(v1, n1, v2, n2):
|
|
vn1 = v1 / n1
|
|
vn2 = v2 / n2
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|
df = (vn1 + vn2)**2 / (vn1**2 / (n1 - 1) + vn2**2 / (n2 - 1))
|
|
|
|
# If df is undefined, variances are zero (assumes n1 > 0 & n2 > 0).
|
|
# Hence it doesn't matter what df is as long as it's not NaN.
|
|
df = np.where(np.isnan(df), 1, df)
|
|
denom = np.sqrt(vn1 + vn2)
|
|
return df, denom
|
|
|
|
|
|
def _equal_var_ttest_denom(v1, n1, v2, n2):
|
|
df = n1 + n2 - 2.0
|
|
svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
|
|
denom = np.sqrt(svar * (1.0 / n1 + 1.0 / n2))
|
|
return df, denom
|
|
|
|
|
|
Ttest_indResult = namedtuple('Ttest_indResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2,
|
|
equal_var=True):
|
|
r"""
|
|
T-test for means of two independent samples from descriptive statistics.
|
|
|
|
This is a two-sided test for the null hypothesis that two independent
|
|
samples have identical average (expected) values.
|
|
|
|
Parameters
|
|
----------
|
|
mean1 : array_like
|
|
The mean(s) of sample 1.
|
|
std1 : array_like
|
|
The standard deviation(s) of sample 1.
|
|
nobs1 : array_like
|
|
The number(s) of observations of sample 1.
|
|
mean2 : array_like
|
|
The mean(s) of sample 2.
|
|
std2 : array_like
|
|
The standard deviations(s) of sample 2.
|
|
nobs2 : array_like
|
|
The number(s) of observations of sample 2.
|
|
equal_var : bool, optional
|
|
If True (default), perform a standard independent 2 sample test
|
|
that assumes equal population variances [1]_.
|
|
If False, perform Welch's t-test, which does not assume equal
|
|
population variance [2]_.
|
|
|
|
Returns
|
|
-------
|
|
statistic : float or array
|
|
The calculated t-statistics.
|
|
pvalue : float or array
|
|
The two-tailed p-value.
|
|
|
|
See Also
|
|
--------
|
|
scipy.stats.ttest_ind
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 0.16.0
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test
|
|
|
|
.. [2] https://en.wikipedia.org/wiki/Welch%27s_t-test
|
|
|
|
Examples
|
|
--------
|
|
Suppose we have the summary data for two samples, as follows::
|
|
|
|
Sample Sample
|
|
Size Mean Variance
|
|
Sample 1 13 15.0 87.5
|
|
Sample 2 11 12.0 39.0
|
|
|
|
Apply the t-test to this data (with the assumption that the population
|
|
variances are equal):
|
|
|
|
>>> from scipy.stats import ttest_ind_from_stats
|
|
>>> ttest_ind_from_stats(mean1=15.0, std1=np.sqrt(87.5), nobs1=13,
|
|
... mean2=12.0, std2=np.sqrt(39.0), nobs2=11)
|
|
Ttest_indResult(statistic=0.9051358093310269, pvalue=0.3751996797581487)
|
|
|
|
For comparison, here is the data from which those summary statistics
|
|
were taken. With this data, we can compute the same result using
|
|
`scipy.stats.ttest_ind`:
|
|
|
|
>>> a = np.array([1, 3, 4, 6, 11, 13, 15, 19, 22, 24, 25, 26, 26])
|
|
>>> b = np.array([2, 4, 6, 9, 11, 13, 14, 15, 18, 19, 21])
|
|
>>> from scipy.stats import ttest_ind
|
|
>>> ttest_ind(a, b)
|
|
Ttest_indResult(statistic=0.905135809331027, pvalue=0.3751996797581486)
|
|
|
|
Suppose we instead have binary data and would like to apply a t-test to
|
|
compare the proportion of 1s in two independent groups::
|
|
|
|
Number of Sample Sample
|
|
Size ones Mean Variance
|
|
Sample 1 150 30 0.2 0.16
|
|
Sample 2 200 45 0.225 0.174375
|
|
|
|
The sample mean :math:`\hat{p}` is the proportion of ones in the sample
|
|
and the variance for a binary observation is estimated by
|
|
:math:`\hat{p}(1-\hat{p})`.
|
|
|
|
>>> ttest_ind_from_stats(mean1=0.2, std1=np.sqrt(0.16), nobs1=150,
|
|
... mean2=0.225, std2=np.sqrt(0.17437), nobs2=200)
|
|
Ttest_indResult(statistic=-0.564327545549774, pvalue=0.5728947691244874)
|
|
|
|
For comparison, we could compute the t statistic and p-value using
|
|
arrays of 0s and 1s and `scipy.stat.ttest_ind`, as above.
|
|
|
|
>>> group1 = np.array([1]*30 + [0]*(150-30))
|
|
>>> group2 = np.array([1]*45 + [0]*(200-45))
|
|
>>> ttest_ind(group1, group2)
|
|
Ttest_indResult(statistic=-0.5627179589855622, pvalue=0.573989277115258)
|
|
|
|
"""
|
|
if equal_var:
|
|
df, denom = _equal_var_ttest_denom(std1**2, nobs1, std2**2, nobs2)
|
|
else:
|
|
df, denom = _unequal_var_ttest_denom(std1**2, nobs1,
|
|
std2**2, nobs2)
|
|
|
|
res = _ttest_ind_from_stats(mean1, mean2, denom, df)
|
|
return Ttest_indResult(*res)
|
|
|
|
|
|
def _ttest_nans(a, b, axis, namedtuple_type):
|
|
"""
|
|
Generate an array of `nan`, with shape determined by `a`, `b` and `axis`.
|
|
|
|
This function is used by ttest_ind and ttest_rel to create the return
|
|
value when one of the inputs has size 0.
|
|
|
|
The shapes of the arrays are determined by dropping `axis` from the
|
|
shapes of `a` and `b` and broadcasting what is left.
|
|
|
|
The return value is a named tuple of the type given in `namedtuple_type`.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.zeros((9, 2))
|
|
>>> b = np.zeros((5, 1))
|
|
>>> _ttest_nans(a, b, 0, Ttest_indResult)
|
|
Ttest_indResult(statistic=array([nan, nan]), pvalue=array([nan, nan]))
|
|
|
|
>>> a = np.zeros((3, 0, 9))
|
|
>>> b = np.zeros((1, 10))
|
|
>>> stat, p = _ttest_nans(a, b, -1, Ttest_indResult)
|
|
>>> stat
|
|
array([], shape=(3, 0), dtype=float64)
|
|
>>> p
|
|
array([], shape=(3, 0), dtype=float64)
|
|
|
|
>>> a = np.zeros(10)
|
|
>>> b = np.zeros(7)
|
|
>>> _ttest_nans(a, b, 0, Ttest_indResult)
|
|
Ttest_indResult(statistic=nan, pvalue=nan)
|
|
"""
|
|
shp = _broadcast_shapes_with_dropped_axis(a, b, axis)
|
|
if len(shp) == 0:
|
|
t = np.nan
|
|
p = np.nan
|
|
else:
|
|
t = np.full(shp, fill_value=np.nan)
|
|
p = t.copy()
|
|
return namedtuple_type(t, p)
|
|
|
|
|
|
def ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate'):
|
|
"""
|
|
Calculate the T-test for the means of *two independent* samples of scores.
|
|
|
|
This is a two-sided test for the null hypothesis that 2 independent samples
|
|
have identical average (expected) values. This test assumes that the
|
|
populations have identical variances by default.
|
|
|
|
Parameters
|
|
----------
|
|
a, b : array_like
|
|
The arrays must have the same shape, except in the dimension
|
|
corresponding to `axis` (the first, by default).
|
|
axis : int or None, optional
|
|
Axis along which to compute test. If None, compute over the whole
|
|
arrays, `a`, and `b`.
|
|
equal_var : bool, optional
|
|
If True (default), perform a standard independent 2 sample test
|
|
that assumes equal population variances [1]_.
|
|
If False, perform Welch's t-test, which does not assume equal
|
|
population variance [2]_.
|
|
|
|
.. versionadded:: 0.11.0
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float or array
|
|
The calculated t-statistic.
|
|
pvalue : float or array
|
|
The two-tailed p-value.
|
|
|
|
Notes
|
|
-----
|
|
We can use this test, if we observe two independent samples from
|
|
the same or different population, e.g. exam scores of boys and
|
|
girls or of two ethnic groups. The test measures whether the
|
|
average (expected) value differs significantly across samples. If
|
|
we observe a large p-value, for example larger than 0.05 or 0.1,
|
|
then we cannot reject the null hypothesis of identical average scores.
|
|
If the p-value is smaller than the threshold, e.g. 1%, 5% or 10%,
|
|
then we reject the null hypothesis of equal averages.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test
|
|
|
|
.. [2] https://en.wikipedia.org/wiki/Welch%27s_t-test
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> np.random.seed(12345678)
|
|
|
|
Test with sample with identical means:
|
|
|
|
>>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
|
|
>>> rvs2 = stats.norm.rvs(loc=5,scale=10,size=500)
|
|
>>> stats.ttest_ind(rvs1,rvs2)
|
|
(0.26833823296239279, 0.78849443369564776)
|
|
>>> stats.ttest_ind(rvs1,rvs2, equal_var = False)
|
|
(0.26833823296239279, 0.78849452749500748)
|
|
|
|
`ttest_ind` underestimates p for unequal variances:
|
|
|
|
>>> rvs3 = stats.norm.rvs(loc=5, scale=20, size=500)
|
|
>>> stats.ttest_ind(rvs1, rvs3)
|
|
(-0.46580283298287162, 0.64145827413436174)
|
|
>>> stats.ttest_ind(rvs1, rvs3, equal_var = False)
|
|
(-0.46580283298287162, 0.64149646246569292)
|
|
|
|
When n1 != n2, the equal variance t-statistic is no longer equal to the
|
|
unequal variance t-statistic:
|
|
|
|
>>> rvs4 = stats.norm.rvs(loc=5, scale=20, size=100)
|
|
>>> stats.ttest_ind(rvs1, rvs4)
|
|
(-0.99882539442782481, 0.3182832709103896)
|
|
>>> stats.ttest_ind(rvs1, rvs4, equal_var = False)
|
|
(-0.69712570584654099, 0.48716927725402048)
|
|
|
|
T-test with different means, variance, and n:
|
|
|
|
>>> rvs5 = stats.norm.rvs(loc=8, scale=20, size=100)
|
|
>>> stats.ttest_ind(rvs1, rvs5)
|
|
(-1.4679669854490653, 0.14263895620529152)
|
|
>>> stats.ttest_ind(rvs1, rvs5, equal_var = False)
|
|
(-0.94365973617132992, 0.34744170334794122)
|
|
|
|
"""
|
|
a, b, axis = _chk2_asarray(a, b, axis)
|
|
|
|
# check both a and b
|
|
cna, npa = _contains_nan(a, nan_policy)
|
|
cnb, npb = _contains_nan(b, nan_policy)
|
|
contains_nan = cna or cnb
|
|
if npa == 'omit' or npb == 'omit':
|
|
nan_policy = 'omit'
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
b = ma.masked_invalid(b)
|
|
return mstats_basic.ttest_ind(a, b, axis, equal_var)
|
|
|
|
if a.size == 0 or b.size == 0:
|
|
return _ttest_nans(a, b, axis, Ttest_indResult)
|
|
|
|
v1 = np.var(a, axis, ddof=1)
|
|
v2 = np.var(b, axis, ddof=1)
|
|
n1 = a.shape[axis]
|
|
n2 = b.shape[axis]
|
|
|
|
if equal_var:
|
|
df, denom = _equal_var_ttest_denom(v1, n1, v2, n2)
|
|
else:
|
|
df, denom = _unequal_var_ttest_denom(v1, n1, v2, n2)
|
|
|
|
res = _ttest_ind_from_stats(np.mean(a, axis), np.mean(b, axis), denom, df)
|
|
|
|
return Ttest_indResult(*res)
|
|
|
|
|
|
def _get_len(a, axis, msg):
|
|
try:
|
|
n = a.shape[axis]
|
|
except IndexError:
|
|
raise np.AxisError(axis, a.ndim, msg) from None
|
|
return n
|
|
|
|
|
|
Ttest_relResult = namedtuple('Ttest_relResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def ttest_rel(a, b, axis=0, nan_policy='propagate'):
|
|
"""
|
|
Calculate the t-test on TWO RELATED samples of scores, a and b.
|
|
|
|
This is a two-sided test for the null hypothesis that 2 related or
|
|
repeated samples have identical average (expected) values.
|
|
|
|
Parameters
|
|
----------
|
|
a, b : array_like
|
|
The arrays must have the same shape.
|
|
axis : int or None, optional
|
|
Axis along which to compute test. If None, compute over the whole
|
|
arrays, `a`, and `b`.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float or array
|
|
t-statistic.
|
|
pvalue : float or array
|
|
Two-sided p-value.
|
|
|
|
Notes
|
|
-----
|
|
Examples for use are scores of the same set of student in
|
|
different exams, or repeated sampling from the same units. The
|
|
test measures whether the average score differs significantly
|
|
across samples (e.g. exams). If we observe a large p-value, for
|
|
example greater than 0.05 or 0.1 then we cannot reject the null
|
|
hypothesis of identical average scores. If the p-value is smaller
|
|
than the threshold, e.g. 1%, 5% or 10%, then we reject the null
|
|
hypothesis of equal averages. Small p-values are associated with
|
|
large t-statistics.
|
|
|
|
References
|
|
----------
|
|
https://en.wikipedia.org/wiki/T-test#Dependent_t-test_for_paired_samples
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> np.random.seed(12345678) # fix random seed to get same numbers
|
|
|
|
>>> rvs1 = stats.norm.rvs(loc=5,scale=10,size=500)
|
|
>>> rvs2 = (stats.norm.rvs(loc=5,scale=10,size=500) +
|
|
... stats.norm.rvs(scale=0.2,size=500))
|
|
>>> stats.ttest_rel(rvs1,rvs2)
|
|
(0.24101764965300962, 0.80964043445811562)
|
|
>>> rvs3 = (stats.norm.rvs(loc=8,scale=10,size=500) +
|
|
... stats.norm.rvs(scale=0.2,size=500))
|
|
>>> stats.ttest_rel(rvs1,rvs3)
|
|
(-3.9995108708727933, 7.3082402191726459e-005)
|
|
|
|
"""
|
|
a, b, axis = _chk2_asarray(a, b, axis)
|
|
|
|
cna, npa = _contains_nan(a, nan_policy)
|
|
cnb, npb = _contains_nan(b, nan_policy)
|
|
contains_nan = cna or cnb
|
|
if npa == 'omit' or npb == 'omit':
|
|
nan_policy = 'omit'
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
a = ma.masked_invalid(a)
|
|
b = ma.masked_invalid(b)
|
|
m = ma.mask_or(ma.getmask(a), ma.getmask(b))
|
|
aa = ma.array(a, mask=m, copy=True)
|
|
bb = ma.array(b, mask=m, copy=True)
|
|
return mstats_basic.ttest_rel(aa, bb, axis)
|
|
|
|
na = _get_len(a, axis, "first argument")
|
|
nb = _get_len(b, axis, "second argument")
|
|
if na != nb:
|
|
raise ValueError('unequal length arrays')
|
|
|
|
if na == 0:
|
|
return _ttest_nans(a, b, axis, Ttest_relResult)
|
|
|
|
n = a.shape[axis]
|
|
df = n - 1
|
|
|
|
d = (a - b).astype(np.float64)
|
|
v = np.var(d, axis, ddof=1)
|
|
dm = np.mean(d, axis)
|
|
denom = np.sqrt(v / n)
|
|
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|
t = np.divide(dm, denom)
|
|
t, prob = _ttest_finish(df, t)
|
|
|
|
return Ttest_relResult(t, prob)
|
|
|
|
|
|
# Map from names to lambda_ values used in power_divergence().
|
|
_power_div_lambda_names = {
|
|
"pearson": 1,
|
|
"log-likelihood": 0,
|
|
"freeman-tukey": -0.5,
|
|
"mod-log-likelihood": -1,
|
|
"neyman": -2,
|
|
"cressie-read": 2/3,
|
|
}
|
|
|
|
|
|
def _count(a, axis=None):
|
|
"""
|
|
Count the number of non-masked elements of an array.
|
|
|
|
This function behaves like np.ma.count(), but is much faster
|
|
for ndarrays.
|
|
"""
|
|
if hasattr(a, 'count'):
|
|
num = a.count(axis=axis)
|
|
if isinstance(num, np.ndarray) and num.ndim == 0:
|
|
# In some cases, the `count` method returns a scalar array (e.g.
|
|
# np.array(3)), but we want a plain integer.
|
|
num = int(num)
|
|
else:
|
|
if axis is None:
|
|
num = a.size
|
|
else:
|
|
num = a.shape[axis]
|
|
return num
|
|
|
|
|
|
Power_divergenceResult = namedtuple('Power_divergenceResult',
|
|
('statistic', 'pvalue'))
|
|
|
|
|
|
def power_divergence(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None):
|
|
"""
|
|
Cressie-Read power divergence statistic and goodness of fit test.
|
|
|
|
This function tests the null hypothesis that the categorical data
|
|
has the given frequencies, using the Cressie-Read power divergence
|
|
statistic.
|
|
|
|
Parameters
|
|
----------
|
|
f_obs : array_like
|
|
Observed frequencies in each category.
|
|
f_exp : array_like, optional
|
|
Expected frequencies in each category. By default the categories are
|
|
assumed to be equally likely.
|
|
ddof : int, optional
|
|
"Delta degrees of freedom": adjustment to the degrees of freedom
|
|
for the p-value. The p-value is computed using a chi-squared
|
|
distribution with ``k - 1 - ddof`` degrees of freedom, where `k`
|
|
is the number of observed frequencies. The default value of `ddof`
|
|
is 0.
|
|
axis : int or None, optional
|
|
The axis of the broadcast result of `f_obs` and `f_exp` along which to
|
|
apply the test. If axis is None, all values in `f_obs` are treated
|
|
as a single data set. Default is 0.
|
|
lambda_ : float or str, optional
|
|
The power in the Cressie-Read power divergence statistic. The default
|
|
is 1. For convenience, `lambda_` may be assigned one of the following
|
|
strings, in which case the corresponding numerical value is used::
|
|
|
|
String Value Description
|
|
"pearson" 1 Pearson's chi-squared statistic.
|
|
In this case, the function is
|
|
equivalent to `stats.chisquare`.
|
|
"log-likelihood" 0 Log-likelihood ratio. Also known as
|
|
the G-test [3]_.
|
|
"freeman-tukey" -1/2 Freeman-Tukey statistic.
|
|
"mod-log-likelihood" -1 Modified log-likelihood ratio.
|
|
"neyman" -2 Neyman's statistic.
|
|
"cressie-read" 2/3 The power recommended in [5]_.
|
|
|
|
Returns
|
|
-------
|
|
statistic : float or ndarray
|
|
The Cressie-Read power divergence test statistic. The value is
|
|
a float if `axis` is None or if` `f_obs` and `f_exp` are 1-D.
|
|
pvalue : float or ndarray
|
|
The p-value of the test. The value is a float if `ddof` and the
|
|
return value `stat` are scalars.
|
|
|
|
See Also
|
|
--------
|
|
chisquare
|
|
|
|
Notes
|
|
-----
|
|
This test is invalid when the observed or expected frequencies in each
|
|
category are too small. A typical rule is that all of the observed
|
|
and expected frequencies should be at least 5.
|
|
|
|
When `lambda_` is less than zero, the formula for the statistic involves
|
|
dividing by `f_obs`, so a warning or error may be generated if any value
|
|
in `f_obs` is 0.
|
|
|
|
Similarly, a warning or error may be generated if any value in `f_exp` is
|
|
zero when `lambda_` >= 0.
|
|
|
|
The default degrees of freedom, k-1, are for the case when no parameters
|
|
of the distribution are estimated. If p parameters are estimated by
|
|
efficient maximum likelihood then the correct degrees of freedom are
|
|
k-1-p. If the parameters are estimated in a different way, then the
|
|
dof can be between k-1-p and k-1. However, it is also possible that
|
|
the asymptotic distribution is not a chisquare, in which case this
|
|
test is not appropriate.
|
|
|
|
This function handles masked arrays. If an element of `f_obs` or `f_exp`
|
|
is masked, then data at that position is ignored, and does not count
|
|
towards the size of the data set.
|
|
|
|
.. versionadded:: 0.13.0
|
|
|
|
References
|
|
----------
|
|
.. [1] Lowry, Richard. "Concepts and Applications of Inferential
|
|
Statistics". Chapter 8.
|
|
https://web.archive.org/web/20171015035606/http://faculty.vassar.edu/lowry/ch8pt1.html
|
|
.. [2] "Chi-squared test", https://en.wikipedia.org/wiki/Chi-squared_test
|
|
.. [3] "G-test", https://en.wikipedia.org/wiki/G-test
|
|
.. [4] Sokal, R. R. and Rohlf, F. J. "Biometry: the principles and
|
|
practice of statistics in biological research", New York: Freeman
|
|
(1981)
|
|
.. [5] Cressie, N. and Read, T. R. C., "Multinomial Goodness-of-Fit
|
|
Tests", J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984),
|
|
pp. 440-464.
|
|
|
|
Examples
|
|
--------
|
|
(See `chisquare` for more examples.)
|
|
|
|
When just `f_obs` is given, it is assumed that the expected frequencies
|
|
are uniform and given by the mean of the observed frequencies. Here we
|
|
perform a G-test (i.e. use the log-likelihood ratio statistic):
|
|
|
|
>>> from scipy.stats import power_divergence
|
|
>>> power_divergence([16, 18, 16, 14, 12, 12], lambda_='log-likelihood')
|
|
(2.006573162632538, 0.84823476779463769)
|
|
|
|
The expected frequencies can be given with the `f_exp` argument:
|
|
|
|
>>> power_divergence([16, 18, 16, 14, 12, 12],
|
|
... f_exp=[16, 16, 16, 16, 16, 8],
|
|
... lambda_='log-likelihood')
|
|
(3.3281031458963746, 0.6495419288047497)
|
|
|
|
When `f_obs` is 2-D, by default the test is applied to each column.
|
|
|
|
>>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
|
|
>>> obs.shape
|
|
(6, 2)
|
|
>>> power_divergence(obs, lambda_="log-likelihood")
|
|
(array([ 2.00657316, 6.77634498]), array([ 0.84823477, 0.23781225]))
|
|
|
|
By setting ``axis=None``, the test is applied to all data in the array,
|
|
which is equivalent to applying the test to the flattened array.
|
|
|
|
>>> power_divergence(obs, axis=None)
|
|
(23.31034482758621, 0.015975692534127565)
|
|
>>> power_divergence(obs.ravel())
|
|
(23.31034482758621, 0.015975692534127565)
|
|
|
|
`ddof` is the change to make to the default degrees of freedom.
|
|
|
|
>>> power_divergence([16, 18, 16, 14, 12, 12], ddof=1)
|
|
(2.0, 0.73575888234288467)
|
|
|
|
The calculation of the p-values is done by broadcasting the
|
|
test statistic with `ddof`.
|
|
|
|
>>> power_divergence([16, 18, 16, 14, 12, 12], ddof=[0,1,2])
|
|
(2.0, array([ 0.84914504, 0.73575888, 0.5724067 ]))
|
|
|
|
`f_obs` and `f_exp` are also broadcast. In the following, `f_obs` has
|
|
shape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting
|
|
`f_obs` and `f_exp` has shape (2, 6). To compute the desired chi-squared
|
|
statistics, we must use ``axis=1``:
|
|
|
|
>>> power_divergence([16, 18, 16, 14, 12, 12],
|
|
... f_exp=[[16, 16, 16, 16, 16, 8],
|
|
... [8, 20, 20, 16, 12, 12]],
|
|
... axis=1)
|
|
(array([ 3.5 , 9.25]), array([ 0.62338763, 0.09949846]))
|
|
|
|
"""
|
|
# Convert the input argument `lambda_` to a numerical value.
|
|
if isinstance(lambda_, str):
|
|
if lambda_ not in _power_div_lambda_names:
|
|
names = repr(list(_power_div_lambda_names.keys()))[1:-1]
|
|
raise ValueError("invalid string for lambda_: {0!r}. Valid strings "
|
|
"are {1}".format(lambda_, names))
|
|
lambda_ = _power_div_lambda_names[lambda_]
|
|
elif lambda_ is None:
|
|
lambda_ = 1
|
|
|
|
f_obs = np.asanyarray(f_obs)
|
|
|
|
if f_exp is not None:
|
|
f_exp = np.asanyarray(f_exp)
|
|
else:
|
|
# Ignore 'invalid' errors so the edge case of a data set with length 0
|
|
# is handled without spurious warnings.
|
|
with np.errstate(invalid='ignore'):
|
|
f_exp = f_obs.mean(axis=axis, keepdims=True)
|
|
|
|
# `terms` is the array of terms that are summed along `axis` to create
|
|
# the test statistic. We use some specialized code for a few special
|
|
# cases of lambda_.
|
|
if lambda_ == 1:
|
|
# Pearson's chi-squared statistic
|
|
terms = (f_obs.astype(np.float64) - f_exp)**2 / f_exp
|
|
elif lambda_ == 0:
|
|
# Log-likelihood ratio (i.e. G-test)
|
|
terms = 2.0 * special.xlogy(f_obs, f_obs / f_exp)
|
|
elif lambda_ == -1:
|
|
# Modified log-likelihood ratio
|
|
terms = 2.0 * special.xlogy(f_exp, f_exp / f_obs)
|
|
else:
|
|
# General Cressie-Read power divergence.
|
|
terms = f_obs * ((f_obs / f_exp)**lambda_ - 1)
|
|
terms /= 0.5 * lambda_ * (lambda_ + 1)
|
|
|
|
stat = terms.sum(axis=axis)
|
|
|
|
num_obs = _count(terms, axis=axis)
|
|
ddof = asarray(ddof)
|
|
p = distributions.chi2.sf(stat, num_obs - 1 - ddof)
|
|
|
|
return Power_divergenceResult(stat, p)
|
|
|
|
|
|
def chisquare(f_obs, f_exp=None, ddof=0, axis=0):
|
|
"""
|
|
Calculate a one-way chi-square test.
|
|
|
|
The chi-square test tests the null hypothesis that the categorical data
|
|
has the given frequencies.
|
|
|
|
Parameters
|
|
----------
|
|
f_obs : array_like
|
|
Observed frequencies in each category.
|
|
f_exp : array_like, optional
|
|
Expected frequencies in each category. By default the categories are
|
|
assumed to be equally likely.
|
|
ddof : int, optional
|
|
"Delta degrees of freedom": adjustment to the degrees of freedom
|
|
for the p-value. The p-value is computed using a chi-squared
|
|
distribution with ``k - 1 - ddof`` degrees of freedom, where `k`
|
|
is the number of observed frequencies. The default value of `ddof`
|
|
is 0.
|
|
axis : int or None, optional
|
|
The axis of the broadcast result of `f_obs` and `f_exp` along which to
|
|
apply the test. If axis is None, all values in `f_obs` are treated
|
|
as a single data set. Default is 0.
|
|
|
|
Returns
|
|
-------
|
|
chisq : float or ndarray
|
|
The chi-squared test statistic. The value is a float if `axis` is
|
|
None or `f_obs` and `f_exp` are 1-D.
|
|
p : float or ndarray
|
|
The p-value of the test. The value is a float if `ddof` and the
|
|
return value `chisq` are scalars.
|
|
|
|
See Also
|
|
--------
|
|
scipy.stats.power_divergence
|
|
|
|
Notes
|
|
-----
|
|
This test is invalid when the observed or expected frequencies in each
|
|
category are too small. A typical rule is that all of the observed
|
|
and expected frequencies should be at least 5.
|
|
|
|
The default degrees of freedom, k-1, are for the case when no parameters
|
|
of the distribution are estimated. If p parameters are estimated by
|
|
efficient maximum likelihood then the correct degrees of freedom are
|
|
k-1-p. If the parameters are estimated in a different way, then the
|
|
dof can be between k-1-p and k-1. However, it is also possible that
|
|
the asymptotic distribution is not chi-square, in which case this test
|
|
is not appropriate.
|
|
|
|
References
|
|
----------
|
|
.. [1] Lowry, Richard. "Concepts and Applications of Inferential
|
|
Statistics". Chapter 8.
|
|
https://web.archive.org/web/20171022032306/http://vassarstats.net:80/textbook/ch8pt1.html
|
|
.. [2] "Chi-squared test", https://en.wikipedia.org/wiki/Chi-squared_test
|
|
|
|
Examples
|
|
--------
|
|
When just `f_obs` is given, it is assumed that the expected frequencies
|
|
are uniform and given by the mean of the observed frequencies.
|
|
|
|
>>> from scipy.stats import chisquare
|
|
>>> chisquare([16, 18, 16, 14, 12, 12])
|
|
(2.0, 0.84914503608460956)
|
|
|
|
With `f_exp` the expected frequencies can be given.
|
|
|
|
>>> chisquare([16, 18, 16, 14, 12, 12], f_exp=[16, 16, 16, 16, 16, 8])
|
|
(3.5, 0.62338762774958223)
|
|
|
|
When `f_obs` is 2-D, by default the test is applied to each column.
|
|
|
|
>>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
|
|
>>> obs.shape
|
|
(6, 2)
|
|
>>> chisquare(obs)
|
|
(array([ 2. , 6.66666667]), array([ 0.84914504, 0.24663415]))
|
|
|
|
By setting ``axis=None``, the test is applied to all data in the array,
|
|
which is equivalent to applying the test to the flattened array.
|
|
|
|
>>> chisquare(obs, axis=None)
|
|
(23.31034482758621, 0.015975692534127565)
|
|
>>> chisquare(obs.ravel())
|
|
(23.31034482758621, 0.015975692534127565)
|
|
|
|
`ddof` is the change to make to the default degrees of freedom.
|
|
|
|
>>> chisquare([16, 18, 16, 14, 12, 12], ddof=1)
|
|
(2.0, 0.73575888234288467)
|
|
|
|
The calculation of the p-values is done by broadcasting the
|
|
chi-squared statistic with `ddof`.
|
|
|
|
>>> chisquare([16, 18, 16, 14, 12, 12], ddof=[0,1,2])
|
|
(2.0, array([ 0.84914504, 0.73575888, 0.5724067 ]))
|
|
|
|
`f_obs` and `f_exp` are also broadcast. In the following, `f_obs` has
|
|
shape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting
|
|
`f_obs` and `f_exp` has shape (2, 6). To compute the desired chi-squared
|
|
statistics, we use ``axis=1``:
|
|
|
|
>>> chisquare([16, 18, 16, 14, 12, 12],
|
|
... f_exp=[[16, 16, 16, 16, 16, 8], [8, 20, 20, 16, 12, 12]],
|
|
... axis=1)
|
|
(array([ 3.5 , 9.25]), array([ 0.62338763, 0.09949846]))
|
|
|
|
"""
|
|
return power_divergence(f_obs, f_exp=f_exp, ddof=ddof, axis=axis,
|
|
lambda_="pearson")
|
|
|
|
|
|
KstestResult = namedtuple('KstestResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def _compute_dplus(cdfvals):
|
|
"""Computes D+ as used in the Kolmogorov-Smirnov test.
|
|
|
|
Parameters
|
|
----------
|
|
cdfvals: array_like
|
|
Sorted array of CDF values between 0 and 1
|
|
|
|
Returns
|
|
-------
|
|
Maximum distance of the CDF values below Uniform(0, 1)
|
|
"""
|
|
n = len(cdfvals)
|
|
return (np.arange(1.0, n + 1) / n - cdfvals).max()
|
|
|
|
|
|
def _compute_dminus(cdfvals):
|
|
"""Computes D- as used in the Kolmogorov-Smirnov test.
|
|
|
|
Parameters
|
|
----------
|
|
cdfvals: array_like
|
|
Sorted array of CDF values between 0 and 1
|
|
|
|
Returns
|
|
-------
|
|
Maximum distance of the CDF values above Uniform(0, 1)
|
|
"""
|
|
n = len(cdfvals)
|
|
return (cdfvals - np.arange(0.0, n)/n).max()
|
|
|
|
|
|
def ks_1samp(x, cdf, args=(), alternative='two-sided', mode='auto'):
|
|
"""
|
|
Performs the Kolmogorov-Smirnov test for goodness of fit.
|
|
|
|
This performs a test of the distribution F(x) of an observed
|
|
random variable against a given distribution G(x). Under the null
|
|
hypothesis, the two distributions are identical, F(x)=G(x). The
|
|
alternative hypothesis can be either 'two-sided' (default), 'less'
|
|
or 'greater'. The KS test is only valid for continuous distributions.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
a 1-D array of observations of iid random variables.
|
|
cdf : callable
|
|
callable used to calculate the cdf.
|
|
args : tuple, sequence, optional
|
|
Distribution parameters, used with `cdf`.
|
|
alternative : {'two-sided', 'less', 'greater'}, optional
|
|
Defines the alternative hypothesis.
|
|
The following options are available (default is 'two-sided'):
|
|
|
|
* 'two-sided'
|
|
* 'less': one-sided, see explanation in Notes
|
|
* 'greater': one-sided, see explanation in Notes
|
|
mode : {'auto', 'exact', 'approx', 'asymp'}, optional
|
|
Defines the distribution used for calculating the p-value.
|
|
The following options are available (default is 'auto'):
|
|
|
|
* 'auto' : selects one of the other options.
|
|
* 'exact' : uses the exact distribution of test statistic.
|
|
* 'approx' : approximates the two-sided probability with twice the one-sided probability
|
|
* 'asymp': uses asymptotic distribution of test statistic
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
KS test statistic, either D, D+ or D- (depending on the value of 'alternative')
|
|
pvalue : float
|
|
One-tailed or two-tailed p-value.
|
|
|
|
See Also
|
|
--------
|
|
ks_2samp, kstest
|
|
|
|
Notes
|
|
-----
|
|
In the one-sided test, the alternative is that the empirical
|
|
cumulative distribution function of the random variable is "less"
|
|
or "greater" than the cumulative distribution function G(x) of the
|
|
hypothesis, ``F(x)<=G(x)``, resp. ``F(x)>=G(x)``.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
|
|
>>> x = np.linspace(-15, 15, 9)
|
|
>>> stats.ks_1samp(x, stats.norm.cdf)
|
|
(0.44435602715924361, 0.038850142705171065)
|
|
|
|
>>> np.random.seed(987654321) # set random seed to get the same result
|
|
>>> stats.ks_1samp(stats.norm.rvs(size=100), stats.norm.cdf)
|
|
(0.058352892479417884, 0.8653960860778898)
|
|
|
|
*Test against one-sided alternative hypothesis*
|
|
|
|
Shift distribution to larger values, so that `` CDF(x) < norm.cdf(x)``:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> x = stats.norm.rvs(loc=0.2, size=100)
|
|
>>> stats.ks_1samp(x, stats.norm.cdf, alternative='less')
|
|
(0.12464329735846891, 0.040989164077641749)
|
|
|
|
Reject equal distribution against alternative hypothesis: less
|
|
|
|
>>> stats.ks_1samp(x, stats.norm.cdf, alternative='greater')
|
|
(0.0072115233216311081, 0.98531158590396395)
|
|
|
|
Don't reject equal distribution against alternative hypothesis: greater
|
|
|
|
>>> stats.ks_1samp(x, stats.norm.cdf)
|
|
(0.12464329735846891, 0.08197335233541582)
|
|
|
|
Don't reject equal distribution against alternative hypothesis: two-sided
|
|
|
|
*Testing t distributed random variables against normal distribution*
|
|
|
|
With 100 degrees of freedom the t distribution looks close to the normal
|
|
distribution, and the K-S test does not reject the hypothesis that the
|
|
sample came from the normal distribution:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> stats.ks_1samp(stats.t.rvs(100,size=100), stats.norm.cdf)
|
|
(0.072018929165471257, 0.6505883498379312)
|
|
|
|
With 3 degrees of freedom the t distribution looks sufficiently different
|
|
from the normal distribution, that we can reject the hypothesis that the
|
|
sample came from the normal distribution at the 10% level:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> stats.ks_1samp(stats.t.rvs(3,size=100), stats.norm.cdf)
|
|
(0.131016895759829, 0.058826222555312224)
|
|
|
|
"""
|
|
alternative = {'t': 'two-sided', 'g': 'greater', 'l': 'less'}.get(
|
|
alternative.lower()[0], alternative)
|
|
if alternative not in ['two-sided', 'greater', 'less']:
|
|
raise ValueError("Unexpected alternative %s" % alternative)
|
|
if np.ma.is_masked(x):
|
|
x = x.compressed()
|
|
|
|
N = len(x)
|
|
x = np.sort(x)
|
|
cdfvals = cdf(x, *args)
|
|
|
|
if alternative == 'greater':
|
|
Dplus = _compute_dplus(cdfvals)
|
|
return KstestResult(Dplus, distributions.ksone.sf(Dplus, N))
|
|
|
|
if alternative == 'less':
|
|
Dminus = _compute_dminus(cdfvals)
|
|
return KstestResult(Dminus, distributions.ksone.sf(Dminus, N))
|
|
|
|
# alternative == 'two-sided':
|
|
Dplus = _compute_dplus(cdfvals)
|
|
Dminus = _compute_dminus(cdfvals)
|
|
D = np.max([Dplus, Dminus])
|
|
if mode == 'auto': # Always select exact
|
|
mode = 'exact'
|
|
if mode == 'exact':
|
|
prob = distributions.kstwo.sf(D, N)
|
|
elif mode == 'asymp':
|
|
prob = distributions.kstwobign.sf(D * np.sqrt(N))
|
|
else:
|
|
# mode == 'approx'
|
|
prob = 2 * distributions.ksone.sf(D, N)
|
|
prob = np.clip(prob, 0, 1)
|
|
return KstestResult(D, prob)
|
|
|
|
|
|
Ks_2sampResult = KstestResult
|
|
|
|
|
|
def _compute_prob_inside_method(m, n, g, h):
|
|
"""
|
|
Count the proportion of paths that stay strictly inside two diagonal lines.
|
|
|
|
Parameters
|
|
----------
|
|
m : integer
|
|
m > 0
|
|
n : integer
|
|
n > 0
|
|
g : integer
|
|
g is greatest common divisor of m and n
|
|
h : integer
|
|
0 <= h <= lcm(m,n)
|
|
|
|
Returns
|
|
-------
|
|
p : float
|
|
The proportion of paths that stay inside the two lines.
|
|
|
|
|
|
Count the integer lattice paths from (0, 0) to (m, n) which satisfy
|
|
|x/m - y/n| < h / lcm(m, n).
|
|
The paths make steps of size +1 in either positive x or positive y directions.
|
|
|
|
We generally follow Hodges' treatment of Drion/Gnedenko/Korolyuk.
|
|
Hodges, J.L. Jr.,
|
|
"The Significance Probability of the Smirnov Two-Sample Test,"
|
|
Arkiv fiur Matematik, 3, No. 43 (1958), 469-86.
|
|
|
|
"""
|
|
# Probability is symmetrical in m, n. Computation below uses m >= n.
|
|
if m < n:
|
|
m, n = n, m
|
|
mg = m // g
|
|
ng = n // g
|
|
|
|
# Count the integer lattice paths from (0, 0) to (m, n) which satisfy
|
|
# |nx/g - my/g| < h.
|
|
# Compute matrix A such that:
|
|
# A(x, 0) = A(0, y) = 1
|
|
# A(x, y) = A(x, y-1) + A(x-1, y), for x,y>=1, except that
|
|
# A(x, y) = 0 if |x/m - y/n|>= h
|
|
# Probability is A(m, n)/binom(m+n, n)
|
|
# Optimizations exist for m==n, m==n*p.
|
|
# Only need to preserve a single column of A, and only a sliding window of it.
|
|
# minj keeps track of the slide.
|
|
minj, maxj = 0, min(int(np.ceil(h / mg)), n + 1)
|
|
curlen = maxj - minj
|
|
# Make a vector long enough to hold maximum window needed.
|
|
lenA = min(2 * maxj + 2, n + 1)
|
|
# This is an integer calculation, but the entries are essentially
|
|
# binomial coefficients, hence grow quickly.
|
|
# Scaling after each column is computed avoids dividing by a
|
|
# large binomial coefficent at the end, but is not sufficient to avoid
|
|
# the large dyanamic range which appears during the calculation.
|
|
# Instead we rescale based on the magnitude of the right most term in
|
|
# the column and keep track of an exponent separately and apply
|
|
# it at the end of the calculation. Similarly when multiplying by
|
|
# the binomial coefficint
|
|
dtype = np.float64
|
|
A = np.zeros(lenA, dtype=dtype)
|
|
# Initialize the first column
|
|
A[minj:maxj] = 1
|
|
expnt = 0
|
|
for i in range(1, m + 1):
|
|
# Generate the next column.
|
|
# First calculate the sliding window
|
|
lastminj, lastlen = minj, curlen
|
|
minj = max(int(np.floor((ng * i - h) / mg)) + 1, 0)
|
|
minj = min(minj, n)
|
|
maxj = min(int(np.ceil((ng * i + h) / mg)), n + 1)
|
|
if maxj <= minj:
|
|
return 0
|
|
# Now fill in the values
|
|
A[0:maxj - minj] = np.cumsum(A[minj - lastminj:maxj - lastminj])
|
|
curlen = maxj - minj
|
|
if lastlen > curlen:
|
|
# Set some carried-over elements to 0
|
|
A[maxj - minj:maxj - minj + (lastlen - curlen)] = 0
|
|
# Rescale if the right most value is over 2**900
|
|
val = A[maxj - minj - 1]
|
|
_, valexpt = math.frexp(val)
|
|
if valexpt > 900:
|
|
# Scaling to bring down to about 2**800 appears
|
|
# sufficient for sizes under 10000.
|
|
valexpt -= 800
|
|
A = np.ldexp(A, -valexpt)
|
|
expnt += valexpt
|
|
|
|
val = A[maxj - minj - 1]
|
|
# Now divide by the binomial (m+n)!/m!/n!
|
|
for i in range(1, n + 1):
|
|
val = (val * i) / (m + i)
|
|
_, valexpt = math.frexp(val)
|
|
if valexpt < -128:
|
|
val = np.ldexp(val, -valexpt)
|
|
expnt += valexpt
|
|
# Finally scale if needed.
|
|
return np.ldexp(val, expnt)
|
|
|
|
|
|
def _compute_prob_outside_square(n, h):
|
|
"""
|
|
Compute the proportion of paths that pass outside the two diagonal lines.
|
|
|
|
Parameters
|
|
----------
|
|
n : integer
|
|
n > 0
|
|
h : integer
|
|
0 <= h <= n
|
|
|
|
Returns
|
|
-------
|
|
p : float
|
|
The proportion of paths that pass outside the lines x-y = +/-h.
|
|
|
|
"""
|
|
# Compute Pr(D_{n,n} >= h/n)
|
|
# Prob = 2 * ( binom(2n, n-h) - binom(2n, n-2a) + binom(2n, n-3a) - ... ) / binom(2n, n)
|
|
# This formulation exhibits subtractive cancellation.
|
|
# Instead divide each term by binom(2n, n), then factor common terms
|
|
# and use a Horner-like algorithm
|
|
# P = 2 * A0 * (1 - A1*(1 - A2*(1 - A3*(1 - A4*(...)))))
|
|
|
|
P = 0.0
|
|
k = int(np.floor(n / h))
|
|
while k >= 0:
|
|
p1 = 1.0
|
|
# Each of the Ai terms has numerator and denominator with h simple terms.
|
|
for j in range(h):
|
|
p1 = (n - k * h - j) * p1 / (n + k * h + j + 1)
|
|
P = p1 * (1.0 - P)
|
|
k -= 1
|
|
return 2 * P
|
|
|
|
|
|
def _count_paths_outside_method(m, n, g, h):
|
|
"""
|
|
Count the number of paths that pass outside the specified diagonal.
|
|
|
|
Parameters
|
|
----------
|
|
m : integer
|
|
m > 0
|
|
n : integer
|
|
n > 0
|
|
g : integer
|
|
g is greatest common divisor of m and n
|
|
h : integer
|
|
0 <= h <= lcm(m,n)
|
|
|
|
Returns
|
|
-------
|
|
p : float
|
|
The number of paths that go low.
|
|
The calculation may overflow - check for a finite answer.
|
|
|
|
Exceptions
|
|
----------
|
|
FloatingPointError: Raised if the intermediate computation goes outside
|
|
the range of a float.
|
|
|
|
Notes
|
|
-----
|
|
Count the integer lattice paths from (0, 0) to (m, n), which at some
|
|
point (x, y) along the path, satisfy:
|
|
m*y <= n*x - h*g
|
|
The paths make steps of size +1 in either positive x or positive y directions.
|
|
|
|
We generally follow Hodges' treatment of Drion/Gnedenko/Korolyuk.
|
|
Hodges, J.L. Jr.,
|
|
"The Significance Probability of the Smirnov Two-Sample Test,"
|
|
Arkiv fiur Matematik, 3, No. 43 (1958), 469-86.
|
|
|
|
"""
|
|
# Compute #paths which stay lower than x/m-y/n = h/lcm(m,n)
|
|
# B(x, y) = #{paths from (0,0) to (x,y) without previously crossing the boundary}
|
|
# = binom(x, y) - #{paths which already reached the boundary}
|
|
# Multiply by the number of path extensions going from (x, y) to (m, n)
|
|
# Sum.
|
|
|
|
# Probability is symmetrical in m, n. Computation below assumes m >= n.
|
|
if m < n:
|
|
m, n = n, m
|
|
mg = m // g
|
|
ng = n // g
|
|
|
|
# Not every x needs to be considered.
|
|
# xj holds the list of x values to be checked.
|
|
# Wherever n*x/m + ng*h crosses an integer
|
|
lxj = n + (mg-h)//mg
|
|
xj = [(h + mg * j + ng-1)//ng for j in range(lxj)]
|
|
# B is an array just holding a few values of B(x,y), the ones needed.
|
|
# B[j] == B(x_j, j)
|
|
if lxj == 0:
|
|
return np.round(special.binom(m + n, n))
|
|
B = np.zeros(lxj)
|
|
B[0] = 1
|
|
# Compute the B(x, y) terms
|
|
# The binomial coefficient is an integer, but special.binom() may return a float.
|
|
# Round it to the nearest integer.
|
|
for j in range(1, lxj):
|
|
Bj = np.round(special.binom(xj[j] + j, j))
|
|
if not np.isfinite(Bj):
|
|
raise FloatingPointError()
|
|
for i in range(j):
|
|
bin = np.round(special.binom(xj[j] - xj[i] + j - i, j-i))
|
|
Bj -= bin * B[i]
|
|
B[j] = Bj
|
|
if not np.isfinite(Bj):
|
|
raise FloatingPointError()
|
|
# Compute the number of path extensions...
|
|
num_paths = 0
|
|
for j in range(lxj):
|
|
bin = np.round(special.binom((m-xj[j]) + (n - j), n-j))
|
|
term = B[j] * bin
|
|
if not np.isfinite(term):
|
|
raise FloatingPointError()
|
|
num_paths += term
|
|
return np.round(num_paths)
|
|
|
|
|
|
def _attempt_exact_2kssamp(n1, n2, g, d, alternative):
|
|
"""Attempts to compute the exact 2sample probability.
|
|
|
|
n1, n2 are the sample sizes
|
|
g is the gcd(n1, n2)
|
|
d is the computed max difference in ECDFs
|
|
|
|
Returns (success, d, probability)
|
|
"""
|
|
lcm = (n1 // g) * n2
|
|
h = int(np.round(d * lcm))
|
|
d = h * 1.0 / lcm
|
|
if h == 0:
|
|
return True, d, 1.0
|
|
saw_fp_error, prob = False, np.nan
|
|
try:
|
|
if alternative == 'two-sided':
|
|
if n1 == n2:
|
|
prob = _compute_prob_outside_square(n1, h)
|
|
else:
|
|
prob = 1 - _compute_prob_inside_method(n1, n2, g, h)
|
|
else:
|
|
if n1 == n2:
|
|
# prob = binom(2n, n-h) / binom(2n, n)
|
|
# Evaluating in that form incurs roundoff errors
|
|
# from special.binom. Instead calculate directly
|
|
jrange = np.arange(h)
|
|
prob = np.prod((n1 - jrange) / (n1 + jrange + 1.0))
|
|
else:
|
|
num_paths = _count_paths_outside_method(n1, n2, g, h)
|
|
bin = special.binom(n1 + n2, n1)
|
|
if not np.isfinite(bin) or not np.isfinite(num_paths) or num_paths > bin:
|
|
saw_fp_error = True
|
|
else:
|
|
prob = num_paths / bin
|
|
|
|
except FloatingPointError:
|
|
saw_fp_error = True
|
|
|
|
if saw_fp_error:
|
|
return False, d, np.nan
|
|
if not (0 <= prob <= 1):
|
|
return False, d, prob
|
|
return True, d, prob
|
|
|
|
|
|
def ks_2samp(data1, data2, alternative='two-sided', mode='auto'):
|
|
"""
|
|
Compute the Kolmogorov-Smirnov statistic on 2 samples.
|
|
|
|
This is a two-sided test for the null hypothesis that 2 independent samples
|
|
are drawn from the same continuous distribution. The alternative hypothesis
|
|
can be either 'two-sided' (default), 'less' or 'greater'.
|
|
|
|
Parameters
|
|
----------
|
|
data1, data2 : array_like, 1-Dimensional
|
|
Two arrays of sample observations assumed to be drawn from a continuous
|
|
distribution, sample sizes can be different.
|
|
alternative : {'two-sided', 'less', 'greater'}, optional
|
|
Defines the alternative hypothesis.
|
|
The following options are available (default is 'two-sided'):
|
|
|
|
* 'two-sided'
|
|
* 'less': one-sided, see explanation in Notes
|
|
* 'greater': one-sided, see explanation in Notes
|
|
mode : {'auto', 'exact', 'asymp'}, optional
|
|
Defines the method used for calculating the p-value.
|
|
The following options are available (default is 'auto'):
|
|
|
|
* 'auto' : use 'exact' for small size arrays, 'asymp' for large
|
|
* 'exact' : use exact distribution of test statistic
|
|
* 'asymp' : use asymptotic distribution of test statistic
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
KS statistic.
|
|
pvalue : float
|
|
Two-tailed p-value.
|
|
|
|
See Also
|
|
--------
|
|
kstest, ks_1samp, epps_singleton_2samp, anderson_ksamp
|
|
|
|
Notes
|
|
-----
|
|
This tests whether 2 samples are drawn from the same distribution. Note
|
|
that, like in the case of the one-sample KS test, the distribution is
|
|
assumed to be continuous.
|
|
|
|
In the one-sided test, the alternative is that the empirical
|
|
cumulative distribution function F(x) of the data1 variable is "less"
|
|
or "greater" than the empirical cumulative distribution function G(x)
|
|
of the data2 variable, ``F(x)<=G(x)``, resp. ``F(x)>=G(x)``.
|
|
|
|
If the KS statistic is small or the p-value is high, then we cannot
|
|
reject the hypothesis that the distributions of the two samples
|
|
are the same.
|
|
|
|
If the mode is 'auto', the computation is exact if the sample sizes are
|
|
less than 10000. For larger sizes, the computation uses the
|
|
Kolmogorov-Smirnov distributions to compute an approximate value.
|
|
|
|
The 'two-sided' 'exact' computation computes the complementary probability
|
|
and then subtracts from 1. As such, the minimum probability it can return
|
|
is about 1e-16. While the algorithm itself is exact, numerical
|
|
errors may accumulate for large sample sizes. It is most suited to
|
|
situations in which one of the sample sizes is only a few thousand.
|
|
|
|
We generally follow Hodges' treatment of Drion/Gnedenko/Korolyuk [1]_.
|
|
|
|
References
|
|
----------
|
|
.. [1] Hodges, J.L. Jr., "The Significance Probability of the Smirnov
|
|
Two-Sample Test," Arkiv fiur Matematik, 3, No. 43 (1958), 469-86.
|
|
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> np.random.seed(12345678) #fix random seed to get the same result
|
|
>>> n1 = 200 # size of first sample
|
|
>>> n2 = 300 # size of second sample
|
|
|
|
For a different distribution, we can reject the null hypothesis since the
|
|
pvalue is below 1%:
|
|
|
|
>>> rvs1 = stats.norm.rvs(size=n1, loc=0., scale=1)
|
|
>>> rvs2 = stats.norm.rvs(size=n2, loc=0.5, scale=1.5)
|
|
>>> stats.ks_2samp(rvs1, rvs2)
|
|
(0.20833333333333334, 5.129279597781977e-05)
|
|
|
|
For a slightly different distribution, we cannot reject the null hypothesis
|
|
at a 10% or lower alpha since the p-value at 0.144 is higher than 10%
|
|
|
|
>>> rvs3 = stats.norm.rvs(size=n2, loc=0.01, scale=1.0)
|
|
>>> stats.ks_2samp(rvs1, rvs3)
|
|
(0.10333333333333333, 0.14691437867433876)
|
|
|
|
For an identical distribution, we cannot reject the null hypothesis since
|
|
the p-value is high, 41%:
|
|
|
|
>>> rvs4 = stats.norm.rvs(size=n2, loc=0.0, scale=1.0)
|
|
>>> stats.ks_2samp(rvs1, rvs4)
|
|
(0.07999999999999996, 0.41126949729859719)
|
|
|
|
"""
|
|
if mode not in ['auto', 'exact', 'asymp']:
|
|
raise ValueError(f'Invalid value for mode: {mode}')
|
|
alternative = {'t': 'two-sided', 'g': 'greater', 'l': 'less'}.get(
|
|
alternative.lower()[0], alternative)
|
|
if alternative not in ['two-sided', 'less', 'greater']:
|
|
raise ValueError(f'Invalid value for alternative: {alternative}')
|
|
MAX_AUTO_N = 10000 # 'auto' will attempt to be exact if n1,n2 <= MAX_AUTO_N
|
|
if np.ma.is_masked(data1):
|
|
data1 = data1.compressed()
|
|
if np.ma.is_masked(data2):
|
|
data2 = data2.compressed()
|
|
data1 = np.sort(data1)
|
|
data2 = np.sort(data2)
|
|
n1 = data1.shape[0]
|
|
n2 = data2.shape[0]
|
|
if min(n1, n2) == 0:
|
|
raise ValueError('Data passed to ks_2samp must not be empty')
|
|
|
|
data_all = np.concatenate([data1, data2])
|
|
# using searchsorted solves equal data problem
|
|
cdf1 = np.searchsorted(data1, data_all, side='right') / n1
|
|
cdf2 = np.searchsorted(data2, data_all, side='right') / n2
|
|
cddiffs = cdf1 - cdf2
|
|
minS = np.clip(-np.min(cddiffs), 0, 1) # Ensure sign of minS is not negative.
|
|
maxS = np.max(cddiffs)
|
|
alt2Dvalue = {'less': minS, 'greater': maxS, 'two-sided': max(minS, maxS)}
|
|
d = alt2Dvalue[alternative]
|
|
g = gcd(n1, n2)
|
|
n1g = n1 // g
|
|
n2g = n2 // g
|
|
prob = -np.inf
|
|
original_mode = mode
|
|
if mode == 'auto':
|
|
mode = 'exact' if max(n1, n2) <= MAX_AUTO_N else 'asymp'
|
|
elif mode == 'exact':
|
|
# If lcm(n1, n2) is too big, switch from exact to asymp
|
|
if n1g >= np.iinfo(np.int_).max / n2g:
|
|
mode = 'asymp'
|
|
warnings.warn(
|
|
f"Exact ks_2samp calculation not possible with samples sizes "
|
|
f"{n1} and {n2}. Switching to 'asymp'.", RuntimeWarning)
|
|
|
|
if mode == 'exact':
|
|
success, d, prob = _attempt_exact_2kssamp(n1, n2, g, d, alternative)
|
|
if not success:
|
|
mode = 'asymp'
|
|
if original_mode == 'exact':
|
|
warnings.warn(f"ks_2samp: Exact calculation unsuccessful. "
|
|
f"Switching to mode={mode}.", RuntimeWarning)
|
|
|
|
if mode == 'asymp':
|
|
# The product n1*n2 is large. Use Smirnov's asymptoptic formula.
|
|
# Ensure float to avoid overflow in multiplication
|
|
# sorted because the one-sided formula is not symmetric in n1, n2
|
|
m, n = sorted([float(n1), float(n2)], reverse=True)
|
|
en = m * n / (m + n)
|
|
if alternative == 'two-sided':
|
|
prob = distributions.kstwo.sf(d, np.round(en))
|
|
else:
|
|
z = np.sqrt(en) * d
|
|
# Use Hodges' suggested approximation Eqn 5.3
|
|
# Requires m to be the larger of (n1, n2)
|
|
expt = -2 * z**2 - 2 * z * (m + 2*n)/np.sqrt(m*n*(m+n))/3.0
|
|
prob = np.exp(expt)
|
|
|
|
prob = np.clip(prob, 0, 1)
|
|
return KstestResult(d, prob)
|
|
|
|
|
|
def _parse_kstest_args(data1, data2, args, N):
|
|
# kstest allows many different variations of arguments.
|
|
# Pull out the parsing into a separate function
|
|
# (xvals, yvals, ) # 2sample
|
|
# (xvals, cdf function,..)
|
|
# (xvals, name of distribution, ...)
|
|
# (name of distribution, name of distribution, ...)
|
|
|
|
# Returns xvals, yvals, cdf
|
|
# where cdf is a cdf function, or None
|
|
# and yvals is either an array_like of values, or None
|
|
# and xvals is array_like.
|
|
rvsfunc, cdf = None, None
|
|
if isinstance(data1, str):
|
|
rvsfunc = getattr(distributions, data1).rvs
|
|
elif callable(data1):
|
|
rvsfunc = data1
|
|
|
|
if isinstance(data2, str):
|
|
cdf = getattr(distributions, data2).cdf
|
|
data2 = None
|
|
elif callable(data2):
|
|
cdf = data2
|
|
data2 = None
|
|
|
|
data1 = np.sort(rvsfunc(*args, size=N) if rvsfunc else data1)
|
|
return data1, data2, cdf
|
|
|
|
|
|
def kstest(rvs, cdf, args=(), N=20, alternative='two-sided', mode='auto'):
|
|
"""
|
|
Performs the (one sample or two samples) Kolmogorov-Smirnov test for goodness of fit.
|
|
|
|
The one-sample test performs a test of the distribution F(x) of an observed
|
|
random variable against a given distribution G(x). Under the null
|
|
hypothesis, the two distributions are identical, F(x)=G(x). The
|
|
alternative hypothesis can be either 'two-sided' (default), 'less'
|
|
or 'greater'. The KS test is only valid for continuous distributions.
|
|
The two-sample test tests whether the two independent samples are drawn
|
|
from the same continuous distribution.
|
|
|
|
Parameters
|
|
----------
|
|
rvs : str, array_like, or callable
|
|
If an array, it should be a 1-D array of observations of random
|
|
variables.
|
|
If a callable, it should be a function to generate random variables;
|
|
it is required to have a keyword argument `size`.
|
|
If a string, it should be the name of a distribution in `scipy.stats`,
|
|
which will be used to generate random variables.
|
|
cdf : str, array_like or callable
|
|
If array_like, it should be a 1-D array of observations of random
|
|
variables, and the two-sample test is performed (and rvs must be array_like)
|
|
If a callable, that callable is used to calculate the cdf.
|
|
If a string, it should be the name of a distribution in `scipy.stats`,
|
|
which will be used as the cdf function.
|
|
args : tuple, sequence, optional
|
|
Distribution parameters, used if `rvs` or `cdf` are strings or callables.
|
|
N : int, optional
|
|
Sample size if `rvs` is string or callable. Default is 20.
|
|
alternative : {'two-sided', 'less', 'greater'}, optional
|
|
Defines the alternative hypothesis.
|
|
The following options are available (default is 'two-sided'):
|
|
|
|
* 'two-sided'
|
|
* 'less': one-sided, see explanation in Notes
|
|
* 'greater': one-sided, see explanation in Notes
|
|
mode : {'auto', 'exact', 'approx', 'asymp'}, optional
|
|
Defines the distribution used for calculating the p-value.
|
|
The following options are available (default is 'auto'):
|
|
|
|
* 'auto' : selects one of the other options.
|
|
* 'exact' : uses the exact distribution of test statistic.
|
|
* 'approx' : approximates the two-sided probability with twice the one-sided probability
|
|
* 'asymp': uses asymptotic distribution of test statistic
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
KS test statistic, either D, D+ or D-.
|
|
pvalue : float
|
|
One-tailed or two-tailed p-value.
|
|
|
|
See Also
|
|
--------
|
|
ks_2samp
|
|
|
|
Notes
|
|
-----
|
|
In the one-sided test, the alternative is that the empirical
|
|
cumulative distribution function of the random variable is "less"
|
|
or "greater" than the cumulative distribution function G(x) of the
|
|
hypothesis, ``F(x)<=G(x)``, resp. ``F(x)>=G(x)``.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
|
|
>>> x = np.linspace(-15, 15, 9)
|
|
>>> stats.kstest(x, 'norm')
|
|
(0.44435602715924361, 0.038850142705171065)
|
|
|
|
>>> np.random.seed(987654321) # set random seed to get the same result
|
|
>>> stats.kstest(stats.norm.rvs(size=100), stats.norm.cdf)
|
|
(0.058352892479417884, 0.8653960860778898)
|
|
|
|
The above lines are equivalent to:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> stats.kstest(stats.norm.rvs, 'norm', N=100)
|
|
(0.058352892479417884, 0.8653960860778898)
|
|
|
|
*Test against one-sided alternative hypothesis*
|
|
|
|
Shift distribution to larger values, so that ``CDF(x) < norm.cdf(x)``:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> x = stats.norm.rvs(loc=0.2, size=100)
|
|
>>> stats.kstest(x, 'norm', alternative='less')
|
|
(0.12464329735846891, 0.040989164077641749)
|
|
|
|
Reject equal distribution against alternative hypothesis: less
|
|
|
|
>>> stats.kstest(x, 'norm', alternative='greater')
|
|
(0.0072115233216311081, 0.98531158590396395)
|
|
|
|
Don't reject equal distribution against alternative hypothesis: greater
|
|
|
|
>>> stats.kstest(x, 'norm')
|
|
(0.12464329735846891, 0.08197335233541582)
|
|
|
|
*Testing t distributed random variables against normal distribution*
|
|
|
|
With 100 degrees of freedom the t distribution looks close to the normal
|
|
distribution, and the K-S test does not reject the hypothesis that the
|
|
sample came from the normal distribution:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> stats.kstest(stats.t.rvs(100, size=100), 'norm')
|
|
(0.072018929165471257, 0.6505883498379312)
|
|
|
|
With 3 degrees of freedom the t distribution looks sufficiently different
|
|
from the normal distribution, that we can reject the hypothesis that the
|
|
sample came from the normal distribution at the 10% level:
|
|
|
|
>>> np.random.seed(987654321)
|
|
>>> stats.kstest(stats.t.rvs(3, size=100), 'norm')
|
|
(0.131016895759829, 0.058826222555312224)
|
|
|
|
"""
|
|
# to not break compatibility with existing code
|
|
if alternative == 'two_sided':
|
|
alternative = 'two-sided'
|
|
if alternative not in ['two-sided', 'greater', 'less']:
|
|
raise ValueError("Unexpected alternative %s" % alternative)
|
|
xvals, yvals, cdf = _parse_kstest_args(rvs, cdf, args, N)
|
|
if cdf:
|
|
return ks_1samp(xvals, cdf, args=args, alternative=alternative, mode=mode)
|
|
return ks_2samp(xvals, yvals, alternative=alternative, mode=mode)
|
|
|
|
|
|
def tiecorrect(rankvals):
|
|
"""
|
|
Tie correction factor for Mann-Whitney U and Kruskal-Wallis H tests.
|
|
|
|
Parameters
|
|
----------
|
|
rankvals : array_like
|
|
A 1-D sequence of ranks. Typically this will be the array
|
|
returned by `~scipy.stats.rankdata`.
|
|
|
|
Returns
|
|
-------
|
|
factor : float
|
|
Correction factor for U or H.
|
|
|
|
See Also
|
|
--------
|
|
rankdata : Assign ranks to the data
|
|
mannwhitneyu : Mann-Whitney rank test
|
|
kruskal : Kruskal-Wallis H test
|
|
|
|
References
|
|
----------
|
|
.. [1] Siegel, S. (1956) Nonparametric Statistics for the Behavioral
|
|
Sciences. New York: McGraw-Hill.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import tiecorrect, rankdata
|
|
>>> tiecorrect([1, 2.5, 2.5, 4])
|
|
0.9
|
|
>>> ranks = rankdata([1, 3, 2, 4, 5, 7, 2, 8, 4])
|
|
>>> ranks
|
|
array([ 1. , 4. , 2.5, 5.5, 7. , 8. , 2.5, 9. , 5.5])
|
|
>>> tiecorrect(ranks)
|
|
0.9833333333333333
|
|
|
|
"""
|
|
arr = np.sort(rankvals)
|
|
idx = np.nonzero(np.r_[True, arr[1:] != arr[:-1], True])[0]
|
|
cnt = np.diff(idx).astype(np.float64)
|
|
|
|
size = np.float64(arr.size)
|
|
return 1.0 if size < 2 else 1.0 - (cnt**3 - cnt).sum() / (size**3 - size)
|
|
|
|
|
|
MannwhitneyuResult = namedtuple('MannwhitneyuResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def mannwhitneyu(x, y, use_continuity=True, alternative=None):
|
|
"""
|
|
Compute the Mann-Whitney rank test on samples x and y.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
Array of samples, should be one-dimensional.
|
|
use_continuity : bool, optional
|
|
Whether a continuity correction (1/2.) should be taken into
|
|
account. Default is True.
|
|
alternative : {None, 'two-sided', 'less', 'greater'}, optional
|
|
Defines the alternative hypothesis.
|
|
The following options are available (default is None):
|
|
|
|
* None: computes p-value half the size of the 'two-sided' p-value and
|
|
a different U statistic. The default behavior is not the same as
|
|
using 'less' or 'greater'; it only exists for backward compatibility
|
|
and is deprecated.
|
|
* 'two-sided'
|
|
* 'less': one-sided
|
|
* 'greater': one-sided
|
|
|
|
Use of the None option is deprecated.
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The Mann-Whitney U statistic, equal to min(U for x, U for y) if
|
|
`alternative` is equal to None (deprecated; exists for backward
|
|
compatibility), and U for y otherwise.
|
|
pvalue : float
|
|
p-value assuming an asymptotic normal distribution. One-sided or
|
|
two-sided, depending on the choice of `alternative`.
|
|
|
|
Notes
|
|
-----
|
|
Use only when the number of observation in each sample is > 20 and
|
|
you have 2 independent samples of ranks. Mann-Whitney U is
|
|
significant if the u-obtained is LESS THAN or equal to the critical
|
|
value of U.
|
|
|
|
This test corrects for ties and by default uses a continuity correction.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Mann-Whitney_U_test
|
|
|
|
.. [2] H.B. Mann and D.R. Whitney, "On a Test of Whether one of Two Random
|
|
Variables is Stochastically Larger than the Other," The Annals of
|
|
Mathematical Statistics, vol. 18, no. 1, pp. 50-60, 1947.
|
|
|
|
"""
|
|
if alternative is None:
|
|
warnings.warn("Calling `mannwhitneyu` without specifying "
|
|
"`alternative` is deprecated.", DeprecationWarning)
|
|
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
n1 = len(x)
|
|
n2 = len(y)
|
|
ranked = rankdata(np.concatenate((x, y)))
|
|
rankx = ranked[0:n1] # get the x-ranks
|
|
u1 = n1*n2 + (n1*(n1+1))/2.0 - np.sum(rankx, axis=0) # calc U for x
|
|
u2 = n1*n2 - u1 # remainder is U for y
|
|
T = tiecorrect(ranked)
|
|
if T == 0:
|
|
raise ValueError('All numbers are identical in mannwhitneyu')
|
|
sd = np.sqrt(T * n1 * n2 * (n1+n2+1) / 12.0)
|
|
|
|
meanrank = n1*n2/2.0 + 0.5 * use_continuity
|
|
if alternative is None or alternative == 'two-sided':
|
|
bigu = max(u1, u2)
|
|
elif alternative == 'less':
|
|
bigu = u1
|
|
elif alternative == 'greater':
|
|
bigu = u2
|
|
else:
|
|
raise ValueError("alternative should be None, 'less', 'greater' "
|
|
"or 'two-sided'")
|
|
|
|
z = (bigu - meanrank) / sd
|
|
if alternative is None:
|
|
# This behavior, equal to half the size of the two-sided
|
|
# p-value, is deprecated.
|
|
p = distributions.norm.sf(abs(z))
|
|
elif alternative == 'two-sided':
|
|
p = 2 * distributions.norm.sf(abs(z))
|
|
else:
|
|
p = distributions.norm.sf(z)
|
|
|
|
u = u2
|
|
# This behavior is deprecated.
|
|
if alternative is None:
|
|
u = min(u1, u2)
|
|
return MannwhitneyuResult(u, p)
|
|
|
|
|
|
RanksumsResult = namedtuple('RanksumsResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def ranksums(x, y):
|
|
"""
|
|
Compute the Wilcoxon rank-sum statistic for two samples.
|
|
|
|
The Wilcoxon rank-sum test tests the null hypothesis that two sets
|
|
of measurements are drawn from the same distribution. The alternative
|
|
hypothesis is that values in one sample are more likely to be
|
|
larger than the values in the other sample.
|
|
|
|
This test should be used to compare two samples from continuous
|
|
distributions. It does not handle ties between measurements
|
|
in x and y. For tie-handling and an optional continuity correction
|
|
see `scipy.stats.mannwhitneyu`.
|
|
|
|
Parameters
|
|
----------
|
|
x,y : array_like
|
|
The data from the two samples.
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The test statistic under the large-sample approximation that the
|
|
rank sum statistic is normally distributed.
|
|
pvalue : float
|
|
The two-sided p-value of the test.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Wilcoxon_rank-sum_test
|
|
|
|
Examples
|
|
--------
|
|
We can test the hypothesis that two independent unequal-sized samples are
|
|
drawn from the same distribution with computing the Wilcoxon rank-sum
|
|
statistic.
|
|
|
|
>>> from scipy.stats import ranksums
|
|
>>> sample1 = np.random.uniform(-1, 1, 200)
|
|
>>> sample2 = np.random.uniform(-0.5, 1.5, 300) # a shifted distribution
|
|
>>> ranksums(sample1, sample2)
|
|
RanksumsResult(statistic=-7.887059, pvalue=3.09390448e-15) # may vary
|
|
|
|
The p-value of less than ``0.05`` indicates that this test rejects the
|
|
hypothesis at the 5% significance level.
|
|
|
|
"""
|
|
x, y = map(np.asarray, (x, y))
|
|
n1 = len(x)
|
|
n2 = len(y)
|
|
alldata = np.concatenate((x, y))
|
|
ranked = rankdata(alldata)
|
|
x = ranked[:n1]
|
|
s = np.sum(x, axis=0)
|
|
expected = n1 * (n1+n2+1) / 2.0
|
|
z = (s - expected) / np.sqrt(n1*n2*(n1+n2+1)/12.0)
|
|
prob = 2 * distributions.norm.sf(abs(z))
|
|
|
|
return RanksumsResult(z, prob)
|
|
|
|
|
|
KruskalResult = namedtuple('KruskalResult', ('statistic', 'pvalue'))
|
|
|
|
|
|
def kruskal(*args, **kwargs):
|
|
"""
|
|
Compute the Kruskal-Wallis H-test for independent samples.
|
|
|
|
The Kruskal-Wallis H-test tests the null hypothesis that the population
|
|
median of all of the groups are equal. It is a non-parametric version of
|
|
ANOVA. The test works on 2 or more independent samples, which may have
|
|
different sizes. Note that rejecting the null hypothesis does not
|
|
indicate which of the groups differs. Post hoc comparisons between
|
|
groups are required to determine which groups are different.
|
|
|
|
Parameters
|
|
----------
|
|
sample1, sample2, ... : array_like
|
|
Two or more arrays with the sample measurements can be given as
|
|
arguments.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The Kruskal-Wallis H statistic, corrected for ties.
|
|
pvalue : float
|
|
The p-value for the test using the assumption that H has a chi
|
|
square distribution.
|
|
|
|
See Also
|
|
--------
|
|
f_oneway : 1-way ANOVA.
|
|
mannwhitneyu : Mann-Whitney rank test on two samples.
|
|
friedmanchisquare : Friedman test for repeated measurements.
|
|
|
|
Notes
|
|
-----
|
|
Due to the assumption that H has a chi square distribution, the number
|
|
of samples in each group must not be too small. A typical rule is
|
|
that each sample must have at least 5 measurements.
|
|
|
|
References
|
|
----------
|
|
.. [1] W. H. Kruskal & W. W. Wallis, "Use of Ranks in
|
|
One-Criterion Variance Analysis", Journal of the American Statistical
|
|
Association, Vol. 47, Issue 260, pp. 583-621, 1952.
|
|
.. [2] https://en.wikipedia.org/wiki/Kruskal-Wallis_one-way_analysis_of_variance
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x = [1, 3, 5, 7, 9]
|
|
>>> y = [2, 4, 6, 8, 10]
|
|
>>> stats.kruskal(x, y)
|
|
KruskalResult(statistic=0.2727272727272734, pvalue=0.6015081344405895)
|
|
|
|
>>> x = [1, 1, 1]
|
|
>>> y = [2, 2, 2]
|
|
>>> z = [2, 2]
|
|
>>> stats.kruskal(x, y, z)
|
|
KruskalResult(statistic=7.0, pvalue=0.0301973834223185)
|
|
|
|
"""
|
|
args = list(map(np.asarray, args))
|
|
num_groups = len(args)
|
|
if num_groups < 2:
|
|
raise ValueError("Need at least two groups in stats.kruskal()")
|
|
|
|
for arg in args:
|
|
if arg.size == 0:
|
|
return KruskalResult(np.nan, np.nan)
|
|
n = np.asarray(list(map(len, args)))
|
|
|
|
if 'nan_policy' in kwargs.keys():
|
|
if kwargs['nan_policy'] not in ('propagate', 'raise', 'omit'):
|
|
raise ValueError("nan_policy must be 'propagate', "
|
|
"'raise' or'omit'")
|
|
else:
|
|
nan_policy = kwargs['nan_policy']
|
|
else:
|
|
nan_policy = 'propagate'
|
|
|
|
contains_nan = False
|
|
for arg in args:
|
|
cn = _contains_nan(arg, nan_policy)
|
|
if cn[0]:
|
|
contains_nan = True
|
|
break
|
|
|
|
if contains_nan and nan_policy == 'omit':
|
|
for a in args:
|
|
a = ma.masked_invalid(a)
|
|
return mstats_basic.kruskal(*args)
|
|
|
|
if contains_nan and nan_policy == 'propagate':
|
|
return KruskalResult(np.nan, np.nan)
|
|
|
|
alldata = np.concatenate(args)
|
|
ranked = rankdata(alldata)
|
|
ties = tiecorrect(ranked)
|
|
if ties == 0:
|
|
raise ValueError('All numbers are identical in kruskal')
|
|
|
|
# Compute sum^2/n for each group and sum
|
|
j = np.insert(np.cumsum(n), 0, 0)
|
|
ssbn = 0
|
|
for i in range(num_groups):
|
|
ssbn += _square_of_sums(ranked[j[i]:j[i+1]]) / n[i]
|
|
|
|
totaln = np.sum(n, dtype=float)
|
|
h = 12.0 / (totaln * (totaln + 1)) * ssbn - 3 * (totaln + 1)
|
|
df = num_groups - 1
|
|
h /= ties
|
|
|
|
return KruskalResult(h, distributions.chi2.sf(h, df))
|
|
|
|
|
|
FriedmanchisquareResult = namedtuple('FriedmanchisquareResult',
|
|
('statistic', 'pvalue'))
|
|
|
|
|
|
def friedmanchisquare(*args):
|
|
"""
|
|
Compute the Friedman test for repeated measurements.
|
|
|
|
The Friedman test tests the null hypothesis that repeated measurements of
|
|
the same individuals have the same distribution. It is often used
|
|
to test for consistency among measurements obtained in different ways.
|
|
For example, if two measurement techniques are used on the same set of
|
|
individuals, the Friedman test can be used to determine if the two
|
|
measurement techniques are consistent.
|
|
|
|
Parameters
|
|
----------
|
|
measurements1, measurements2, measurements3... : array_like
|
|
Arrays of measurements. All of the arrays must have the same number
|
|
of elements. At least 3 sets of measurements must be given.
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The test statistic, correcting for ties.
|
|
pvalue : float
|
|
The associated p-value assuming that the test statistic has a chi
|
|
squared distribution.
|
|
|
|
Notes
|
|
-----
|
|
Due to the assumption that the test statistic has a chi squared
|
|
distribution, the p-value is only reliable for n > 10 and more than
|
|
6 repeated measurements.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Friedman_test
|
|
|
|
"""
|
|
k = len(args)
|
|
if k < 3:
|
|
raise ValueError('Less than 3 levels. Friedman test not appropriate.')
|
|
|
|
n = len(args[0])
|
|
for i in range(1, k):
|
|
if len(args[i]) != n:
|
|
raise ValueError('Unequal N in friedmanchisquare. Aborting.')
|
|
|
|
# Rank data
|
|
data = np.vstack(args).T
|
|
data = data.astype(float)
|
|
for i in range(len(data)):
|
|
data[i] = rankdata(data[i])
|
|
|
|
# Handle ties
|
|
ties = 0
|
|
for i in range(len(data)):
|
|
replist, repnum = find_repeats(array(data[i]))
|
|
for t in repnum:
|
|
ties += t * (t*t - 1)
|
|
c = 1 - ties / (k*(k*k - 1)*n)
|
|
|
|
ssbn = np.sum(data.sum(axis=0)**2)
|
|
chisq = (12.0 / (k*n*(k+1)) * ssbn - 3*n*(k+1)) / c
|
|
|
|
return FriedmanchisquareResult(chisq, distributions.chi2.sf(chisq, k - 1))
|
|
|
|
|
|
BrunnerMunzelResult = namedtuple('BrunnerMunzelResult',
|
|
('statistic', 'pvalue'))
|
|
|
|
|
|
def brunnermunzel(x, y, alternative="two-sided", distribution="t",
|
|
nan_policy='propagate'):
|
|
"""
|
|
Compute the Brunner-Munzel test on samples x and y.
|
|
|
|
The Brunner-Munzel test is a nonparametric test of the null hypothesis that
|
|
when values are taken one by one from each group, the probabilities of
|
|
getting large values in both groups are equal.
|
|
Unlike the Wilcoxon-Mann-Whitney's U test, this does not require the
|
|
assumption of equivariance of two groups. Note that this does not assume
|
|
the distributions are same. This test works on two independent samples,
|
|
which may have different sizes.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
Array of samples, should be one-dimensional.
|
|
alternative : {'two-sided', 'less', 'greater'}, optional
|
|
Defines the alternative hypothesis.
|
|
The following options are available (default is 'two-sided'):
|
|
|
|
* 'two-sided'
|
|
* 'less': one-sided
|
|
* 'greater': one-sided
|
|
distribution : {'t', 'normal'}, optional
|
|
Defines how to get the p-value.
|
|
The following options are available (default is 't'):
|
|
|
|
* 't': get the p-value by t-distribution
|
|
* 'normal': get the p-value by standard normal distribution.
|
|
nan_policy : {'propagate', 'raise', 'omit'}, optional
|
|
Defines how to handle when input contains nan.
|
|
The following options are available (default is 'propagate'):
|
|
|
|
* 'propagate': returns nan
|
|
* 'raise': throws an error
|
|
* 'omit': performs the calculations ignoring nan values
|
|
|
|
Returns
|
|
-------
|
|
statistic : float
|
|
The Brunner-Munzer W statistic.
|
|
pvalue : float
|
|
p-value assuming an t distribution. One-sided or
|
|
two-sided, depending on the choice of `alternative` and `distribution`.
|
|
|
|
See Also
|
|
--------
|
|
mannwhitneyu : Mann-Whitney rank test on two samples.
|
|
|
|
Notes
|
|
-----
|
|
Brunner and Munzel recommended to estimate the p-value by t-distribution
|
|
when the size of data is 50 or less. If the size is lower than 10, it would
|
|
be better to use permuted Brunner Munzel test (see [2]_).
|
|
|
|
References
|
|
----------
|
|
.. [1] Brunner, E. and Munzel, U. "The nonparametric Benhrens-Fisher
|
|
problem: Asymptotic theory and a small-sample approximation".
|
|
Biometrical Journal. Vol. 42(2000): 17-25.
|
|
.. [2] Neubert, K. and Brunner, E. "A studentized permutation test for the
|
|
non-parametric Behrens-Fisher problem". Computational Statistics and
|
|
Data Analysis. Vol. 51(2007): 5192-5204.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import stats
|
|
>>> x1 = [1,2,1,1,1,1,1,1,1,1,2,4,1,1]
|
|
>>> x2 = [3,3,4,3,1,2,3,1,1,5,4]
|
|
>>> w, p_value = stats.brunnermunzel(x1, x2)
|
|
>>> w
|
|
3.1374674823029505
|
|
>>> p_value
|
|
0.0057862086661515377
|
|
|
|
"""
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
|
|
# check both x and y
|
|
cnx, npx = _contains_nan(x, nan_policy)
|
|
cny, npy = _contains_nan(y, nan_policy)
|
|
contains_nan = cnx or cny
|
|
if npx == "omit" or npy == "omit":
|
|
nan_policy = "omit"
|
|
|
|
if contains_nan and nan_policy == "propagate":
|
|
return BrunnerMunzelResult(np.nan, np.nan)
|
|
elif contains_nan and nan_policy == "omit":
|
|
x = ma.masked_invalid(x)
|
|
y = ma.masked_invalid(y)
|
|
return mstats_basic.brunnermunzel(x, y, alternative, distribution)
|
|
|
|
nx = len(x)
|
|
ny = len(y)
|
|
if nx == 0 or ny == 0:
|
|
return BrunnerMunzelResult(np.nan, np.nan)
|
|
rankc = rankdata(np.concatenate((x, y)))
|
|
rankcx = rankc[0:nx]
|
|
rankcy = rankc[nx:nx+ny]
|
|
rankcx_mean = np.mean(rankcx)
|
|
rankcy_mean = np.mean(rankcy)
|
|
rankx = rankdata(x)
|
|
ranky = rankdata(y)
|
|
rankx_mean = np.mean(rankx)
|
|
ranky_mean = np.mean(ranky)
|
|
|
|
Sx = np.sum(np.power(rankcx - rankx - rankcx_mean + rankx_mean, 2.0))
|
|
Sx /= nx - 1
|
|
Sy = np.sum(np.power(rankcy - ranky - rankcy_mean + ranky_mean, 2.0))
|
|
Sy /= ny - 1
|
|
|
|
wbfn = nx * ny * (rankcy_mean - rankcx_mean)
|
|
wbfn /= (nx + ny) * np.sqrt(nx * Sx + ny * Sy)
|
|
|
|
if distribution == "t":
|
|
df_numer = np.power(nx * Sx + ny * Sy, 2.0)
|
|
df_denom = np.power(nx * Sx, 2.0) / (nx - 1)
|
|
df_denom += np.power(ny * Sy, 2.0) / (ny - 1)
|
|
df = df_numer / df_denom
|
|
p = distributions.t.cdf(wbfn, df)
|
|
elif distribution == "normal":
|
|
p = distributions.norm.cdf(wbfn)
|
|
else:
|
|
raise ValueError(
|
|
"distribution should be 't' or 'normal'")
|
|
|
|
if alternative == "greater":
|
|
pass
|
|
elif alternative == "less":
|
|
p = 1 - p
|
|
elif alternative == "two-sided":
|
|
p = 2 * np.min([p, 1-p])
|
|
else:
|
|
raise ValueError(
|
|
"alternative should be 'less', 'greater' or 'two-sided'")
|
|
|
|
return BrunnerMunzelResult(wbfn, p)
|
|
|
|
|
|
def combine_pvalues(pvalues, method='fisher', weights=None):
|
|
"""
|
|
Combine p-values from independent tests bearing upon the same hypothesis.
|
|
|
|
Parameters
|
|
----------
|
|
pvalues : array_like, 1-D
|
|
Array of p-values assumed to come from independent tests.
|
|
method : {'fisher', 'pearson', 'tippett', 'stouffer', 'mudholkar_george'}, optional
|
|
Name of method to use to combine p-values.
|
|
The following methods are available (default is 'fisher'):
|
|
|
|
* 'fisher': Fisher's method (Fisher's combined probability test), the
|
|
sum of the logarithm of the p-values
|
|
* 'pearson': Pearson's method (similar to Fisher's but uses sum of the
|
|
complement of the p-values inside the logarithms)
|
|
* 'tippett': Tippett's method (minimum of p-values)
|
|
* 'stouffer': Stouffer's Z-score method
|
|
* 'mudholkar_george': the difference of Fisher's and Pearson's methods
|
|
divided by 2
|
|
weights : array_like, 1-D, optional
|
|
Optional array of weights used only for Stouffer's Z-score method.
|
|
|
|
Returns
|
|
-------
|
|
statistic: float
|
|
The statistic calculated by the specified method.
|
|
pval: float
|
|
The combined p-value.
|
|
|
|
Notes
|
|
-----
|
|
Fisher's method (also known as Fisher's combined probability test) [1]_ uses
|
|
a chi-squared statistic to compute a combined p-value. The closely related
|
|
Stouffer's Z-score method [2]_ uses Z-scores rather than p-values. The
|
|
advantage of Stouffer's method is that it is straightforward to introduce
|
|
weights, which can make Stouffer's method more powerful than Fisher's
|
|
method when the p-values are from studies of different size [6]_ [7]_.
|
|
The Pearson's method uses :math:`log(1-p_i)` inside the sum whereas Fisher's
|
|
method uses :math:`log(p_i)` [4]_. For Fisher's and Pearson's method, the
|
|
sum of the logarithms is multiplied by -2 in the implementation. This
|
|
quantity has a chi-square distribution that determines the p-value. The
|
|
`mudholkar_george` method is the difference of the Fisher's and Pearson's
|
|
test statistics, each of which include the -2 factor [4]_. However, the
|
|
`mudholkar_george` method does not include these -2 factors. The test
|
|
statistic of `mudholkar_george` is the sum of logisitic random variables and
|
|
equation 3.6 in [3]_ is used to approximate the p-value based on Student's
|
|
t-distribution.
|
|
|
|
Fisher's method may be extended to combine p-values from dependent tests
|
|
[5]_. Extensions such as Brown's method and Kost's method are not currently
|
|
implemented.
|
|
|
|
.. versionadded:: 0.15.0
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Fisher%27s_method
|
|
.. [2] https://en.wikipedia.org/wiki/Fisher%27s_method#Relation_to_Stouffer.27s_Z-score_method
|
|
.. [3] George, E. O., and G. S. Mudholkar. "On the convolution of logistic
|
|
random variables." Metrika 30.1 (1983): 1-13.
|
|
.. [4] Heard, N. and Rubin-Delanchey, P. "Choosing between methods of
|
|
combining p-values." Biometrika 105.1 (2018): 239-246.
|
|
.. [5] Whitlock, M. C. "Combining probability from independent tests: the
|
|
weighted Z-method is superior to Fisher's approach." Journal of
|
|
Evolutionary Biology 18, no. 5 (2005): 1368-1373.
|
|
.. [6] Zaykin, Dmitri V. "Optimally weighted Z-test is a powerful method
|
|
for combining probabilities in meta-analysis." Journal of
|
|
Evolutionary Biology 24, no. 8 (2011): 1836-1841.
|
|
.. [7] https://en.wikipedia.org/wiki/Extensions_of_Fisher%27s_method
|
|
|
|
"""
|
|
pvalues = np.asarray(pvalues)
|
|
if pvalues.ndim != 1:
|
|
raise ValueError("pvalues is not 1-D")
|
|
|
|
if method == 'fisher':
|
|
statistic = -2 * np.sum(np.log(pvalues))
|
|
pval = distributions.chi2.sf(statistic, 2 * len(pvalues))
|
|
elif method == 'pearson':
|
|
statistic = -2 * np.sum(np.log1p(-pvalues))
|
|
pval = distributions.chi2.sf(statistic, 2 * len(pvalues))
|
|
elif method == 'mudholkar_george':
|
|
normalizing_factor = np.sqrt(3/len(pvalues))/np.pi
|
|
statistic = -np.sum(np.log(pvalues)) + np.sum(np.log1p(-pvalues))
|
|
nu = 5 * len(pvalues) + 4
|
|
approx_factor = np.sqrt(nu / (nu - 2))
|
|
pval = distributions.t.sf(statistic * normalizing_factor * approx_factor, nu)
|
|
elif method == 'tippett':
|
|
statistic = np.min(pvalues)
|
|
pval = distributions.beta.sf(statistic, 1, len(pvalues))
|
|
elif method == 'stouffer':
|
|
if weights is None:
|
|
weights = np.ones_like(pvalues)
|
|
elif len(weights) != len(pvalues):
|
|
raise ValueError("pvalues and weights must be of the same size.")
|
|
|
|
weights = np.asarray(weights)
|
|
if weights.ndim != 1:
|
|
raise ValueError("weights is not 1-D")
|
|
|
|
Zi = distributions.norm.isf(pvalues)
|
|
statistic = np.dot(weights, Zi) / np.linalg.norm(weights)
|
|
pval = distributions.norm.sf(statistic)
|
|
|
|
else:
|
|
raise ValueError(
|
|
"Invalid method '%s'. Options are 'fisher', 'pearson', \
|
|
'mudholkar_george', 'tippett', 'or 'stouffer'", method)
|
|
|
|
return (statistic, pval)
|
|
|
|
|
|
#####################################
|
|
# STATISTICAL DISTANCES #
|
|
#####################################
|
|
|
|
def wasserstein_distance(u_values, v_values, u_weights=None, v_weights=None):
|
|
r"""
|
|
Compute the first Wasserstein distance between two 1D distributions.
|
|
|
|
This distance is also known as the earth mover's distance, since it can be
|
|
seen as the minimum amount of "work" required to transform :math:`u` into
|
|
:math:`v`, where "work" is measured as the amount of distribution weight
|
|
that must be moved, multiplied by the distance it has to be moved.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
Parameters
|
|
----------
|
|
u_values, v_values : array_like
|
|
Values observed in the (empirical) distribution.
|
|
u_weights, v_weights : array_like, optional
|
|
Weight for each value. If unspecified, each value is assigned the same
|
|
weight.
|
|
`u_weights` (resp. `v_weights`) must have the same length as
|
|
`u_values` (resp. `v_values`). If the weight sum differs from 1, it
|
|
must still be positive and finite so that the weights can be normalized
|
|
to sum to 1.
|
|
|
|
Returns
|
|
-------
|
|
distance : float
|
|
The computed distance between the distributions.
|
|
|
|
Notes
|
|
-----
|
|
The first Wasserstein distance between the distributions :math:`u` and
|
|
:math:`v` is:
|
|
|
|
.. math::
|
|
|
|
l_1 (u, v) = \inf_{\pi \in \Gamma (u, v)} \int_{\mathbb{R} \times
|
|
\mathbb{R}} |x-y| \mathrm{d} \pi (x, y)
|
|
|
|
where :math:`\Gamma (u, v)` is the set of (probability) distributions on
|
|
:math:`\mathbb{R} \times \mathbb{R}` whose marginals are :math:`u` and
|
|
:math:`v` on the first and second factors respectively.
|
|
|
|
If :math:`U` and :math:`V` are the respective CDFs of :math:`u` and
|
|
:math:`v`, this distance also equals to:
|
|
|
|
.. math::
|
|
|
|
l_1(u, v) = \int_{-\infty}^{+\infty} |U-V|
|
|
|
|
See [2]_ for a proof of the equivalence of both definitions.
|
|
|
|
The input distributions can be empirical, therefore coming from samples
|
|
whose values are effectively inputs of the function, or they can be seen as
|
|
generalized functions, in which case they are weighted sums of Dirac delta
|
|
functions located at the specified values.
|
|
|
|
References
|
|
----------
|
|
.. [1] "Wasserstein metric", https://en.wikipedia.org/wiki/Wasserstein_metric
|
|
.. [2] Ramdas, Garcia, Cuturi "On Wasserstein Two Sample Testing and Related
|
|
Families of Nonparametric Tests" (2015). :arXiv:`1509.02237`.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import wasserstein_distance
|
|
>>> wasserstein_distance([0, 1, 3], [5, 6, 8])
|
|
5.0
|
|
>>> wasserstein_distance([0, 1], [0, 1], [3, 1], [2, 2])
|
|
0.25
|
|
>>> wasserstein_distance([3.4, 3.9, 7.5, 7.8], [4.5, 1.4],
|
|
... [1.4, 0.9, 3.1, 7.2], [3.2, 3.5])
|
|
4.0781331438047861
|
|
|
|
"""
|
|
return _cdf_distance(1, u_values, v_values, u_weights, v_weights)
|
|
|
|
|
|
def energy_distance(u_values, v_values, u_weights=None, v_weights=None):
|
|
r"""
|
|
Compute the energy distance between two 1D distributions.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
Parameters
|
|
----------
|
|
u_values, v_values : array_like
|
|
Values observed in the (empirical) distribution.
|
|
u_weights, v_weights : array_like, optional
|
|
Weight for each value. If unspecified, each value is assigned the same
|
|
weight.
|
|
`u_weights` (resp. `v_weights`) must have the same length as
|
|
`u_values` (resp. `v_values`). If the weight sum differs from 1, it
|
|
must still be positive and finite so that the weights can be normalized
|
|
to sum to 1.
|
|
|
|
Returns
|
|
-------
|
|
distance : float
|
|
The computed distance between the distributions.
|
|
|
|
Notes
|
|
-----
|
|
The energy distance between two distributions :math:`u` and :math:`v`, whose
|
|
respective CDFs are :math:`U` and :math:`V`, equals to:
|
|
|
|
.. math::
|
|
|
|
D(u, v) = \left( 2\mathbb E|X - Y| - \mathbb E|X - X'| -
|
|
\mathbb E|Y - Y'| \right)^{1/2}
|
|
|
|
where :math:`X` and :math:`X'` (resp. :math:`Y` and :math:`Y'`) are
|
|
independent random variables whose probability distribution is :math:`u`
|
|
(resp. :math:`v`).
|
|
|
|
As shown in [2]_, for one-dimensional real-valued variables, the energy
|
|
distance is linked to the non-distribution-free version of the Cramer-von
|
|
Mises distance:
|
|
|
|
.. math::
|
|
|
|
D(u, v) = \sqrt{2} l_2(u, v) = \left( 2 \int_{-\infty}^{+\infty} (U-V)^2
|
|
\right)^{1/2}
|
|
|
|
Note that the common Cramer-von Mises criterion uses the distribution-free
|
|
version of the distance. See [2]_ (section 2), for more details about both
|
|
versions of the distance.
|
|
|
|
The input distributions can be empirical, therefore coming from samples
|
|
whose values are effectively inputs of the function, or they can be seen as
|
|
generalized functions, in which case they are weighted sums of Dirac delta
|
|
functions located at the specified values.
|
|
|
|
References
|
|
----------
|
|
.. [1] "Energy distance", https://en.wikipedia.org/wiki/Energy_distance
|
|
.. [2] Szekely "E-statistics: The energy of statistical samples." Bowling
|
|
Green State University, Department of Mathematics and Statistics,
|
|
Technical Report 02-16 (2002).
|
|
.. [3] Rizzo, Szekely "Energy distance." Wiley Interdisciplinary Reviews:
|
|
Computational Statistics, 8(1):27-38 (2015).
|
|
.. [4] Bellemare, Danihelka, Dabney, Mohamed, Lakshminarayanan, Hoyer,
|
|
Munos "The Cramer Distance as a Solution to Biased Wasserstein
|
|
Gradients" (2017). :arXiv:`1705.10743`.
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.stats import energy_distance
|
|
>>> energy_distance([0], [2])
|
|
2.0000000000000004
|
|
>>> energy_distance([0, 8], [0, 8], [3, 1], [2, 2])
|
|
1.0000000000000002
|
|
>>> energy_distance([0.7, 7.4, 2.4, 6.8], [1.4, 8. ],
|
|
... [2.1, 4.2, 7.4, 8. ], [7.6, 8.8])
|
|
0.88003340976158217
|
|
|
|
"""
|
|
return np.sqrt(2) * _cdf_distance(2, u_values, v_values,
|
|
u_weights, v_weights)
|
|
|
|
|
|
def _cdf_distance(p, u_values, v_values, u_weights=None, v_weights=None):
|
|
r"""
|
|
Compute, between two one-dimensional distributions :math:`u` and
|
|
:math:`v`, whose respective CDFs are :math:`U` and :math:`V`, the
|
|
statistical distance that is defined as:
|
|
|
|
.. math::
|
|
|
|
l_p(u, v) = \left( \int_{-\infty}^{+\infty} |U-V|^p \right)^{1/p}
|
|
|
|
p is a positive parameter; p = 1 gives the Wasserstein distance, p = 2
|
|
gives the energy distance.
|
|
|
|
Parameters
|
|
----------
|
|
u_values, v_values : array_like
|
|
Values observed in the (empirical) distribution.
|
|
u_weights, v_weights : array_like, optional
|
|
Weight for each value. If unspecified, each value is assigned the same
|
|
weight.
|
|
`u_weights` (resp. `v_weights`) must have the same length as
|
|
`u_values` (resp. `v_values`). If the weight sum differs from 1, it
|
|
must still be positive and finite so that the weights can be normalized
|
|
to sum to 1.
|
|
|
|
Returns
|
|
-------
|
|
distance : float
|
|
The computed distance between the distributions.
|
|
|
|
Notes
|
|
-----
|
|
The input distributions can be empirical, therefore coming from samples
|
|
whose values are effectively inputs of the function, or they can be seen as
|
|
generalized functions, in which case they are weighted sums of Dirac delta
|
|
functions located at the specified values.
|
|
|
|
References
|
|
----------
|
|
.. [1] Bellemare, Danihelka, Dabney, Mohamed, Lakshminarayanan, Hoyer,
|
|
Munos "The Cramer Distance as a Solution to Biased Wasserstein
|
|
Gradients" (2017). :arXiv:`1705.10743`.
|
|
|
|
"""
|
|
u_values, u_weights = _validate_distribution(u_values, u_weights)
|
|
v_values, v_weights = _validate_distribution(v_values, v_weights)
|
|
|
|
u_sorter = np.argsort(u_values)
|
|
v_sorter = np.argsort(v_values)
|
|
|
|
all_values = np.concatenate((u_values, v_values))
|
|
all_values.sort(kind='mergesort')
|
|
|
|
# Compute the differences between pairs of successive values of u and v.
|
|
deltas = np.diff(all_values)
|
|
|
|
# Get the respective positions of the values of u and v among the values of
|
|
# both distributions.
|
|
u_cdf_indices = u_values[u_sorter].searchsorted(all_values[:-1], 'right')
|
|
v_cdf_indices = v_values[v_sorter].searchsorted(all_values[:-1], 'right')
|
|
|
|
# Calculate the CDFs of u and v using their weights, if specified.
|
|
if u_weights is None:
|
|
u_cdf = u_cdf_indices / u_values.size
|
|
else:
|
|
u_sorted_cumweights = np.concatenate(([0],
|
|
np.cumsum(u_weights[u_sorter])))
|
|
u_cdf = u_sorted_cumweights[u_cdf_indices] / u_sorted_cumweights[-1]
|
|
|
|
if v_weights is None:
|
|
v_cdf = v_cdf_indices / v_values.size
|
|
else:
|
|
v_sorted_cumweights = np.concatenate(([0],
|
|
np.cumsum(v_weights[v_sorter])))
|
|
v_cdf = v_sorted_cumweights[v_cdf_indices] / v_sorted_cumweights[-1]
|
|
|
|
# Compute the value of the integral based on the CDFs.
|
|
# If p = 1 or p = 2, we avoid using np.power, which introduces an overhead
|
|
# of about 15%.
|
|
if p == 1:
|
|
return np.sum(np.multiply(np.abs(u_cdf - v_cdf), deltas))
|
|
if p == 2:
|
|
return np.sqrt(np.sum(np.multiply(np.square(u_cdf - v_cdf), deltas)))
|
|
return np.power(np.sum(np.multiply(np.power(np.abs(u_cdf - v_cdf), p),
|
|
deltas)), 1/p)
|
|
|
|
|
|
def _validate_distribution(values, weights):
|
|
"""
|
|
Validate the values and weights from a distribution input of `cdf_distance`
|
|
and return them as ndarray objects.
|
|
|
|
Parameters
|
|
----------
|
|
values : array_like
|
|
Values observed in the (empirical) distribution.
|
|
weights : array_like
|
|
Weight for each value.
|
|
|
|
Returns
|
|
-------
|
|
values : ndarray
|
|
Values as ndarray.
|
|
weights : ndarray
|
|
Weights as ndarray.
|
|
|
|
"""
|
|
# Validate the value array.
|
|
values = np.asarray(values, dtype=float)
|
|
if len(values) == 0:
|
|
raise ValueError("Distribution can't be empty.")
|
|
|
|
# Validate the weight array, if specified.
|
|
if weights is not None:
|
|
weights = np.asarray(weights, dtype=float)
|
|
if len(weights) != len(values):
|
|
raise ValueError('Value and weight array-likes for the same '
|
|
'empirical distribution must be of the same size.')
|
|
if np.any(weights < 0):
|
|
raise ValueError('All weights must be non-negative.')
|
|
if not 0 < np.sum(weights) < np.inf:
|
|
raise ValueError('Weight array-like sum must be positive and '
|
|
'finite. Set as None for an equal distribution of '
|
|
'weight.')
|
|
|
|
return values, weights
|
|
|
|
return values, None
|
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#####################################
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# SUPPORT FUNCTIONS #
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#####################################
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RepeatedResults = namedtuple('RepeatedResults', ('values', 'counts'))
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def find_repeats(arr):
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"""
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Find repeats and repeat counts.
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Parameters
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----------
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arr : array_like
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Input array. This is cast to float64.
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Returns
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-------
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values : ndarray
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The unique values from the (flattened) input that are repeated.
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counts : ndarray
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Number of times the corresponding 'value' is repeated.
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Notes
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-----
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In numpy >= 1.9 `numpy.unique` provides similar functionality. The main
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difference is that `find_repeats` only returns repeated values.
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Examples
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--------
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>>> from scipy import stats
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>>> stats.find_repeats([2, 1, 2, 3, 2, 2, 5])
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RepeatedResults(values=array([2.]), counts=array([4]))
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>>> stats.find_repeats([[10, 20, 1, 2], [5, 5, 4, 4]])
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RepeatedResults(values=array([4., 5.]), counts=array([2, 2]))
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"""
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# Note: always copies.
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return RepeatedResults(*_find_repeats(np.array(arr, dtype=np.float64)))
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def _sum_of_squares(a, axis=0):
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"""
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Square each element of the input array, and return the sum(s) of that.
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Parameters
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----------
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a : array_like
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Input array.
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axis : int or None, optional
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Axis along which to calculate. Default is 0. If None, compute over
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the whole array `a`.
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Returns
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-------
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sum_of_squares : ndarray
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The sum along the given axis for (a**2).
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See Also
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--------
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_square_of_sums : The square(s) of the sum(s) (the opposite of
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`_sum_of_squares`).
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"""
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a, axis = _chk_asarray(a, axis)
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return np.sum(a*a, axis)
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def _square_of_sums(a, axis=0):
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"""
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Sum elements of the input array, and return the square(s) of that sum.
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Parameters
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----------
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a : array_like
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Input array.
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axis : int or None, optional
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Axis along which to calculate. Default is 0. If None, compute over
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the whole array `a`.
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Returns
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-------
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square_of_sums : float or ndarray
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The square of the sum over `axis`.
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See Also
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--------
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_sum_of_squares : The sum of squares (the opposite of `square_of_sums`).
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"""
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a, axis = _chk_asarray(a, axis)
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s = np.sum(a, axis)
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if not np.isscalar(s):
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return s.astype(float) * s
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else:
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return float(s) * s
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def rankdata(a, method='average', *, axis=None):
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"""
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Assign ranks to data, dealing with ties appropriately.
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By default (``axis=None``), the data array is first flattened, and a flat
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array of ranks is returned. Separately reshape the rank array to the
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shape of the data array if desired (see Examples).
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Ranks begin at 1. The `method` argument controls how ranks are assigned
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to equal values. See [1]_ for further discussion of ranking methods.
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Parameters
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----------
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a : array_like
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The array of values to be ranked.
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method : {'average', 'min', 'max', 'dense', 'ordinal'}, optional
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The method used to assign ranks to tied elements.
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The following methods are available (default is 'average'):
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* 'average': The average of the ranks that would have been assigned to
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all the tied values is assigned to each value.
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* 'min': The minimum of the ranks that would have been assigned to all
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the tied values is assigned to each value. (This is also
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referred to as "competition" ranking.)
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* 'max': The maximum of the ranks that would have been assigned to all
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the tied values is assigned to each value.
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* 'dense': Like 'min', but the rank of the next highest element is
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assigned the rank immediately after those assigned to the tied
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elements.
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* 'ordinal': All values are given a distinct rank, corresponding to
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the order that the values occur in `a`.
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axis : {None, int}, optional
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Axis along which to perform the ranking. If ``None``, the data array
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is first flattened.
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Returns
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-------
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ranks : ndarray
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An array of size equal to the size of `a`, containing rank
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scores.
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References
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----------
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.. [1] "Ranking", https://en.wikipedia.org/wiki/Ranking
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Examples
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--------
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>>> from scipy.stats import rankdata
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>>> rankdata([0, 2, 3, 2])
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array([ 1. , 2.5, 4. , 2.5])
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>>> rankdata([0, 2, 3, 2], method='min')
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array([ 1, 2, 4, 2])
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>>> rankdata([0, 2, 3, 2], method='max')
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array([ 1, 3, 4, 3])
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>>> rankdata([0, 2, 3, 2], method='dense')
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array([ 1, 2, 3, 2])
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>>> rankdata([0, 2, 3, 2], method='ordinal')
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array([ 1, 2, 4, 3])
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>>> rankdata([[0, 2], [3, 2]]).reshape(2,2)
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array([[1. , 2.5],
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[4. , 2.5]])
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>>> rankdata([[0, 2, 2], [3, 2, 5]], axis=1)
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array([[1. , 2.5, 2.5],
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[2. , 1. , 3. ]])
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"""
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if method not in ('average', 'min', 'max', 'dense', 'ordinal'):
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raise ValueError('unknown method "{0}"'.format(method))
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if axis is not None:
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a = np.asarray(a)
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if a.size == 0:
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# The return values of `normalize_axis_index` are ignored. The
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# call validates `axis`, even though we won't use it.
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# use scipy._lib._util._normalize_axis_index when available
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np.core.multiarray.normalize_axis_index(axis, a.ndim)
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dt = np.float64 if method == 'average' else np.int_
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return np.empty(a.shape, dtype=dt)
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return np.apply_along_axis(rankdata, axis, a, method)
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arr = np.ravel(np.asarray(a))
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algo = 'mergesort' if method == 'ordinal' else 'quicksort'
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sorter = np.argsort(arr, kind=algo)
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inv = np.empty(sorter.size, dtype=np.intp)
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inv[sorter] = np.arange(sorter.size, dtype=np.intp)
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if method == 'ordinal':
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return inv + 1
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arr = arr[sorter]
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obs = np.r_[True, arr[1:] != arr[:-1]]
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dense = obs.cumsum()[inv]
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if method == 'dense':
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return dense
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# cumulative counts of each unique value
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count = np.r_[np.nonzero(obs)[0], len(obs)]
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if method == 'max':
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return count[dense]
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if method == 'min':
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return count[dense - 1] + 1
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# average method
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return .5 * (count[dense] + count[dense - 1] + 1)
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