477 lines
19 KiB
Python
477 lines
19 KiB
Python
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from itertools import permutations
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import numpy as np
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import math
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from ._continuous_distns import norm
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import scipy.stats
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from dataclasses import make_dataclass
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PageTrendTestResult = make_dataclass("PageTrendTestResult",
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("statistic", "pvalue", "method"))
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def page_trend_test(data, ranked=False, predicted_ranks=None, method='auto'):
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r"""
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Perform Page's Test, a measure of trend in observations between treatments.
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Page's Test (also known as Page's :math:`L` test) is useful when:
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* there are :math:`n \geq 3` treatments,
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* :math:`m \geq 2` subjects are observed for each treatment, and
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* the observations are hypothesized to have a particular order.
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Specifically, the test considers the null hypothesis that
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.. math::
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m_1 = m_2 = m_3 \cdots = m_n,
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where :math:`m_j` is the mean of the observed quantity under treatment
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:math:`j`, against the alternative hypothesis that
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.. math::
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m_1 \leq m_2 \leq m_3 \leq \cdots \leq m_n,
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where at least one inequality is strict.
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As noted by [4]_, Page's :math:`L` test has greater statistical power than
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the Friedman test against the alternative that there is a difference in
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trend, as Friedman's test only considers a difference in the means of the
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observations without considering their order. Whereas Spearman :math:`\rho`
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considers the correlation between the ranked observations of two variables
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(e.g. the airspeed velocity of a swallow vs. the weight of the coconut it
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carries), Page's :math:`L` is concerned with a trend in an observation
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(e.g. the airspeed velocity of a swallow) across several distinct
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treatments (e.g. carrying each of five coconuts of different weight) even
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as the observation is repeated with multiple subjects (e.g. one European
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swallow and one African swallow).
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Parameters
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----------
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data : array-like
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A :math:`m \times n` array; the element in row :math:`i` and
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column :math:`j` is the observation corresponding with subject
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:math:`i` and treatment :math:`j`. By default, the columns are
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assumed to be arranged in order of increasing predicted mean.
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ranked : boolean, optional
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By default, `data` is assumed to be observations rather than ranks;
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it will be ranked with `scipy.stats.rankdata` along ``axis=1``. If
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`data` is provided in the form of ranks, pass argument ``True``.
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predicted_ranks : array-like, optional
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The predicted ranks of the column means. If not specified,
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the columns are assumed to be arranged in order of increasing
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predicted mean, so the default `predicted_ranks` are
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:math:`[1, 2, \dots, n-1, n]`.
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method : {'auto', 'asymptotic', 'exact'}, optional
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Selects the method used to calculate the *p*-value. The following
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options are available.
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* 'auto': selects between 'exact' and 'asymptotic' to
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achieve reasonably accurate results in reasonable time (default)
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* 'asymptotic': compares the standardized test statistic against
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the normal distribution
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* 'exact': computes the exact *p*-value by comparing the observed
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:math:`L` statistic against those realized by all possible
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permutations of ranks (under the null hypothesis that each
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permutation is equally likely)
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Returns
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-------
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res : PageTrendTestResult
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An object containing attributes:
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statistic : float
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Page's :math:`L` test statistic.
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pvalue : float
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The associated *p*-value
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method : {'asymptotic', 'exact'}
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The method used to compute the *p*-value
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See Also
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--------
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rankdata, friedmanchisquare, spearmanr
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Notes
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-----
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As noted in [1]_, "the :math:`n` 'treatments' could just as well represent
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:math:`n` objects or events or performances or persons or trials ranked."
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Similarly, the :math:`m` 'subjects' could equally stand for :math:`m`
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"groupings by ability or some other control variable, or judges doing
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the ranking, or random replications of some other sort."
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The procedure for calculating the :math:`L` statistic, adapted from
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[1]_, is:
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1. "Predetermine with careful logic the appropriate hypotheses
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concerning the predicted ording of the experimental results.
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If no reasonable basis for ordering any treatments is known, the
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:math:`L` test is not appropriate."
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2. "As in other experiments, determine at what level of confidence
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you will reject the null hypothesis that there is no agreement of
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experimental results with the monotonic hypothesis."
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3. "Cast the experimental material into a two-way table of :math:`n`
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columns (treatments, objects ranked, conditions) and :math:`m`
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rows (subjects, replication groups, levels of control variables)."
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4. "When experimental observations are recorded, rank them across each
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row", e.g. ``ranks = scipy.stats.rankdata(data, axis=1)``.
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5. "Add the ranks in each column", e.g.
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``colsums = np.sum(ranks, axis=0)``.
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6. "Multiply each sum of ranks by the predicted rank for that same
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column", e.g. ``products = predicted_ranks * colsums``.
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7. "Sum all such products", e.g. ``L = products.sum()``.
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[1]_ continues by suggesting use of the standardized statistic
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.. math::
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\chi_L^2 = \frac{\left[12L-3mn(n+1)^2\right]^2}{mn^2(n^2-1)(n+1)}
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"which is distributed approximately as chi-square with 1 degree of
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freedom. The ordinary use of :math:`\chi^2` tables would be
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equivalent to a two-sided test of agreement. If a one-sided test
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is desired, *as will almost always be the case*, the probability
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discovered in the chi-square table should be *halved*."
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However, this standardized statistic does not distinguish between the
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observed values being well correlated with the predicted ranks and being
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_anti_-correlated with the predicted ranks. Instead, we follow [2]_
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and calculate the standardized statistic
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.. math::
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\Lambda = \frac{L - E_0}{\sqrt{V_0}},
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where :math:`E_0 = \frac{1}{4} mn(n+1)^2` and
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:math:`V_0 = \frac{1}{144} mn^2(n+1)(n^2-1)`, "which is asymptotically
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normal under the null hypothesis".
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The *p*-value for ``method='exact'`` is generated by comparing the observed
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value of :math:`L` against the :math:`L` values generated for all
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:math:`(n!)^m` possible permutations of ranks. The calculation is performed
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using the recursive method of [5].
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The *p*-values are not adjusted for the possibility of ties. When
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ties are present, the reported ``'exact'`` *p*-values may be somewhat
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larger (i.e. more conservative) than the true *p*-value [2]_. The
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``'asymptotic'``` *p*-values, however, tend to be smaller (i.e. less
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conservative) than the ``'exact'`` *p*-values.
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References
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----------
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.. [1] Ellis Batten Page, "Ordered hypotheses for multiple treatments:
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a significant test for linear ranks", *Journal of the American
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Statistical Association* 58(301), p. 216--230, 1963.
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.. [2] Markus Neuhauser, *Nonparametric Statistical Test: A computational
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approach*, CRC Press, p. 150--152, 2012.
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.. [3] Statext LLC, "Page's L Trend Test - Easy Statistics", *Statext -
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Statistics Study*, https://www.statext.com/practice/PageTrendTest03.php,
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Accessed July 12, 2020.
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.. [4] "Page's Trend Test", *Wikipedia*, WikimediaFoundation,
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https://en.wikipedia.org/wiki/Page%27s_trend_test,
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Accessed July 12, 2020.
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.. [5] Robert E. Odeh, "The exact distribution of Page's L-statistic in
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the two-way layout", *Communications in Statistics - Simulation and
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Computation*, 6(1), p. 49--61, 1977.
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Examples
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--------
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We use the example from [3]_: 10 students are asked to rate three
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teaching methods - tutorial, lecture, and seminar - on a scale of 1-5,
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with 1 being the lowest and 5 being the highest. We have decided that
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a confidence level of 99% is required to reject the null hypothesis in
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favor of our alternative: that the seminar will have the highest ratings
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and the tutorial will have the lowest. Initially, the data have been
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tabulated with each row representing an individual student's ratings of
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the three methods in the following order: tutorial, lecture, seminar.
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>>> table = [[3, 4, 3],
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... [2, 2, 4],
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... [3, 3, 5],
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... [1, 3, 2],
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... [2, 3, 2],
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... [2, 4, 5],
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... [1, 2, 4],
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... [3, 4, 4],
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... [2, 4, 5],
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... [1, 3, 4]]
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Because the tutorial is hypothesized to have the lowest ratings, the
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column corresponding with tutorial rankings should be first; the seminar
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is hypothesized to have the highest ratings, so its column should be last.
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Since the columns are already arranged in this order of increasing
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predicted mean, we can pass the table directly into `page_trend_test`.
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>>> from scipy.stats import page_trend_test
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>>> res = page_trend_test(table)
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>>> res
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PageTrendTestResult(statistic=133.5, pvalue=0.0018191161948127822,
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method='exact')
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This *p*-value indicates that there is a 0.1819% chance that
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the :math:`L` statistic would reach such an extreme value under the null
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hypothesis. Because 0.1819% is less than 1%, we have evidence to reject
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the null hypothesis in favor of our alternative at a 99% confidence level.
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The value of the :math:`L` statistic is 133.5. To check this manually,
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we rank the data such that high scores correspond with high ranks, settling
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ties with an average rank:
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>>> from scipy.stats import rankdata
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>>> ranks = rankdata(table, axis=1)
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>>> ranks
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array([[1.5, 3. , 1.5],
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[1.5, 1.5, 3. ],
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[1.5, 1.5, 3. ],
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[1. , 3. , 2. ],
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[1.5, 3. , 1.5],
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[1. , 2. , 3. ],
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[1. , 2. , 3. ],
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[1. , 2.5, 2.5],
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[1. , 2. , 3. ],
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[1. , 2. , 3. ]])
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We add the ranks within each column, multiply the sums by the
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predicted ranks, and sum the products.
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>>> import numpy as np
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>>> m, n = ranks.shape
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>>> predicted_ranks = np.arange(1, n+1)
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>>> L = (predicted_ranks * np.sum(ranks, axis=0)).sum()
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>>> res.statistic == L
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True
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As presented in [3]_, the asymptotic approximation of the *p*-value is the
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survival function of the normal distribution evaluated at the standardized
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test statistic:
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>>> from scipy.stats import norm
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>>> E0 = (m*n*(n+1)**2)/4
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>>> V0 = (m*n**2*(n+1)*(n**2-1))/144
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>>> Lambda = (L-E0)/np.sqrt(V0)
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>>> p = norm.sf(Lambda)
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>>> p
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0.0012693433690751756
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This does not precisely match the *p*-value reported by `page_trend_test`
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above. The asymptotic distribution is not very accurate, nor conservative,
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for :math:`m \leq 12` and :math:`n \leq 8`, so `page_trend_test` chose to
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use ``method='exact'`` based on the dimensions of the table and the
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recommendations in Page's original paper [1]_. To override
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`page_trend_test`'s choice, provide the `method` argument.
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>>> res = page_trend_test(table, method="asymptotic")
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>>> res
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PageTrendTestResult(statistic=133.5, pvalue=0.0012693433690751756,
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method='asymptotic')
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If the data are already ranked, we can pass in the ``ranks`` instead of
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the ``table`` to save computation time.
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>>> res = page_trend_test(ranks, # ranks of data
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... ranked=True, # data is already ranked
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... )
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>>> res
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PageTrendTestResult(statistic=133.5, pvalue=0.0018191161948127822,
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method='exact')
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Suppose the raw data had been tabulated in an order different from the
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order of predicted means, say lecture, seminar, tutorial.
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>>> table = np.asarray(table)[:, [1, 2, 0]]
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Since the arrangement of this table is not consistent with the assumed
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ordering, we can either rearrange the table or provide the
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`predicted_ranks`. Remembering that the lecture is predicted
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to have the middle rank, the seminar the highest, and tutorial the lowest,
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we pass:
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>>> res = page_trend_test(table, # data as originally tabulated
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... predicted_ranks=[2, 3, 1], # our predicted order
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... )
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>>> res
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PageTrendTestResult(statistic=133.5, pvalue=0.0018191161948127822,
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method='exact')
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"""
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# Possible values of the method parameter and the corresponding function
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# used to evaluate the p value
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methods = {"asymptotic": _l_p_asymptotic,
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"exact": _l_p_exact,
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"auto": None}
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if method not in methods:
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raise ValueError(f"`method` must be in {set(methods)}")
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ranks = np.array(data, copy=False)
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if ranks.ndim != 2: # TODO: relax this to accept 3d arrays?
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raise ValueError("`data` must be a 2d array.")
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m, n = ranks.shape
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if m < 2 or n < 3:
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raise ValueError("Page's L is only appropriate for data with two "
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"or more rows and three or more columns.")
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if np.any(np.isnan(data)):
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raise ValueError("`data` contains NaNs, which cannot be ranked "
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"meaningfully")
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# ensure NumPy array and rank the data if it's not already ranked
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if ranked:
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# Only a basic check on whether data is ranked. Checking that the data
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# is properly ranked could take as much time as ranking it.
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if not (ranks.min() >= 1 and ranks.max() <= ranks.shape[1]):
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raise ValueError("`data` is not properly ranked. Rank the data or "
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"pass `ranked=False`.")
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else:
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ranks = scipy.stats.rankdata(data, axis=-1)
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# generate predicted ranks if not provided, ensure valid NumPy array
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if predicted_ranks is None:
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predicted_ranks = np.arange(1, n+1)
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else:
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predicted_ranks = np.array(predicted_ranks, copy=False)
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if (predicted_ranks.ndim < 1 or
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(set(predicted_ranks) != set(range(1, n+1)) or
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len(predicted_ranks) != n)):
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raise ValueError(f"`predicted_ranks` must include each integer "
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f"from 1 to {n} (the number of columns in "
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f"`data`) exactly once.")
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|
|
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||
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if type(ranked) is not bool:
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|
raise TypeError("`ranked` must be boolean.")
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|
|
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|
# Calculate the L statistic
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|
L = _l_vectorized(ranks, predicted_ranks)
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|
|
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||
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|
# Calculate the p-value
|
||
|
|
if method == "auto":
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|
method = _choose_method(ranks)
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p_fun = methods[method] # get the function corresponding with the method
|
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|
|
p = p_fun(L, m, n)
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||
|
|
|
||
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|
page_result = PageTrendTestResult(statistic=L, pvalue=p, method=method)
|
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|
return page_result
|
||
|
|
|
||
|
|
|
||
|
|
def _choose_method(ranks):
|
||
|
|
'''Choose method for computing p-value automatically'''
|
||
|
|
m, n = ranks.shape
|
||
|
|
if n > 8 or (m > 12 and n > 3) or m > 20: # as in [1], [4]
|
||
|
|
method = "asymptotic"
|
||
|
|
else:
|
||
|
|
method = "exact"
|
||
|
|
return method
|
||
|
|
|
||
|
|
|
||
|
|
def _l_vectorized(ranks, predicted_ranks):
|
||
|
|
'''Calculate's Page's L statistic for each page of a 3d array'''
|
||
|
|
colsums = ranks.sum(axis=-2, keepdims=True)
|
||
|
|
products = predicted_ranks * colsums
|
||
|
|
Ls = products.sum(axis=-1)
|
||
|
|
Ls = Ls[0] if Ls.size == 1 else Ls.ravel()
|
||
|
|
return Ls
|
||
|
|
|
||
|
|
|
||
|
|
def _l_p_asymptotic(L, m, n):
|
||
|
|
'''Calculate the p-value of Page's L from the asymptotic distribution'''
|
||
|
|
# Using [1] as a reference, the asymptotic p-value would be calculated as:
|
||
|
|
# chi_L = (12*L - 3*m*n*(n+1)**2)**2/(m*n**2*(n**2-1)*(n+1))
|
||
|
|
# p = chi2.sf(chi_L, df=1, loc=0, scale=1)/2
|
||
|
|
# but this is insentive to the direction of the hypothesized ranking
|
||
|
|
|
||
|
|
# See [2] page 151
|
||
|
|
E0 = (m*n*(n+1)**2)/4
|
||
|
|
V0 = (m*n**2*(n+1)*(n**2-1))/144
|
||
|
|
Lambda = (L-E0)/np.sqrt(V0)
|
||
|
|
# This is a one-sided "greater" test - calculate the probability that the
|
||
|
|
# L statistic under H0 would be greater than the observed L statistic
|
||
|
|
p = norm.sf(Lambda)
|
||
|
|
return p
|
||
|
|
|
||
|
|
|
||
|
|
def _l_p_exact(L, m, n):
|
||
|
|
'''Calculate the p-value of Page's L exactly'''
|
||
|
|
# [1] uses m, n; [5] uses n, k.
|
||
|
|
# Switch convention here because exact calculation code references [5].
|
||
|
|
L, n, k = int(L), int(m), int(n)
|
||
|
|
_pagel_state.set_k(k)
|
||
|
|
return _pagel_state.sf(L, n)
|
||
|
|
|
||
|
|
|
||
|
|
class _PageL:
|
||
|
|
'''Maintains state between `page_trend_test` executions'''
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
'''Lightweight initialization'''
|
||
|
|
self.all_pmfs = {}
|
||
|
|
|
||
|
|
def set_k(self, k):
|
||
|
|
'''Calculate lower and upper limits of L for single row'''
|
||
|
|
self.k = k
|
||
|
|
# See [5] top of page 52
|
||
|
|
self.a, self.b = (k*(k+1)*(k+2))//6, (k*(k+1)*(2*k+1))//6
|
||
|
|
|
||
|
|
def sf(self, l, n):
|
||
|
|
'''Survival function of Page's L statistic'''
|
||
|
|
ps = [self.pmf(l, n) for l in range(l, n*self.b + 1)]
|
||
|
|
return np.sum(ps)
|
||
|
|
|
||
|
|
def p_l_k_1(self):
|
||
|
|
'''Relative frequency of each L value over all possible single rows'''
|
||
|
|
|
||
|
|
# See [5] Equation (6)
|
||
|
|
ranks = range(1, self.k+1)
|
||
|
|
# generate all possible rows of length k
|
||
|
|
rank_perms = np.array(list(permutations(ranks)))
|
||
|
|
# compute Page's L for all possible rows
|
||
|
|
Ls = (ranks*rank_perms).sum(axis=1)
|
||
|
|
# count occurences of each L value
|
||
|
|
counts = np.histogram(Ls, np.arange(self.a-0.5, self.b+1.5))[0]
|
||
|
|
# factorial(k) is number of possible permutations
|
||
|
|
return counts/math.factorial(self.k)
|
||
|
|
|
||
|
|
def pmf(self, l, n):
|
||
|
|
'''Recursive function to evaluate p(l, k, n); see [5] Equation 1'''
|
||
|
|
|
||
|
|
if n not in self.all_pmfs:
|
||
|
|
self.all_pmfs[n] = {}
|
||
|
|
if self.k not in self.all_pmfs[n]:
|
||
|
|
self.all_pmfs[n][self.k] = {}
|
||
|
|
|
||
|
|
# Cache results to avoid repeating calculation. Initially this was
|
||
|
|
# written with lru_cache, but this seems faster? Also, we could add
|
||
|
|
# an option to save this for future lookup.
|
||
|
|
if l in self.all_pmfs[n][self.k]:
|
||
|
|
return self.all_pmfs[n][self.k][l]
|
||
|
|
|
||
|
|
if n == 1:
|
||
|
|
ps = self.p_l_k_1() # [5] Equation 6
|
||
|
|
ls = range(self.a, self.b+1)
|
||
|
|
# not fast, but we'll only be here once
|
||
|
|
self.all_pmfs[n][self.k] = {l: p for l, p in zip(ls, ps)}
|
||
|
|
return self.all_pmfs[n][self.k][l]
|
||
|
|
|
||
|
|
p = 0
|
||
|
|
low = max(l-(n-1)*self.b, self.a) # [5] Equation 2
|
||
|
|
high = min(l-(n-1)*self.a, self.b)
|
||
|
|
|
||
|
|
# [5] Equation 1
|
||
|
|
for t in range(low, high+1):
|
||
|
|
p1 = self.pmf(l-t, n-1)
|
||
|
|
p2 = self.pmf(t, 1)
|
||
|
|
p += p1*p2
|
||
|
|
self.all_pmfs[n][self.k][l] = p
|
||
|
|
return p
|
||
|
|
|
||
|
|
|
||
|
|
# Maintain state for faster repeat calls to page_trend_test w/ method='exact'
|
||
|
|
_pagel_state = _PageL()
|