bundles / scipy 1.17.1 / scipy / stats / _page_trend_test / page_trend_test

function

`scipy.stats._page_trend_test:page_trend_test`

source: /scipy/stats/_page_trend_test.py :20

Signature

   def     page_trend_test (    data    ,    ranked    =  False   ,    predicted_ranks    =  None   ,    method    =  auto     )

Summary

Perform Page's Test, a measure of trend in observations between treatments.

Extended Summary

Page's Test (also known as Page's $L$ test) is useful when:

there are $n \geq 3$ treatments,
$m \geq 2$ subjects are observed for each treatment, and
the observations are hypothesized to have a particular order.

Specifically, the test considers the null hypothesis that

m_{1} = m_{2} = m_{3} \dots = m_{n},

where $m_{j}$ is the mean of the observed quantity under treatment $j$ , against the alternative hypothesis that

m_{1} \leq m_{2} \leq m_{3} \leq \dots \leq m_{n},

where at least one inequality is strict.

As noted by ^[4], Page's $L$ test has greater statistical power than the Friedman test against the alternative that there is a difference in trend, as Friedman's test only considers a difference in the means of the observations without considering their order. Whereas Spearman $ρ$ considers the correlation between the ranked observations of two variables (e.g. the airspeed velocity of a swallow vs. the weight of the coconut it carries), Page's $L$ is concerned with a trend in an observation (e.g. the airspeed velocity of a swallow) across several distinct treatments (e.g. carrying each of five coconuts of different weight) even as the observation is repeated with multiple subjects (e.g. one European swallow and one African swallow).

Parameters

data : array-like

A $m \times n$ array; the element in row $i$ and column $j$ is the observation corresponding with subject $i$ and treatment $j$ . By default, the columns are assumed to be arranged in order of increasing predicted mean.

ranked : boolean, optional

By default, data is assumed to be observations rather than ranks; it will be ranked with scipy.stats.rankdata along axis=1. If data is provided in the form of ranks, pass argument True.

predicted_ranks : array-like, optional

The predicted ranks of the column means. If not specified, the columns are assumed to be arranged in order of increasing predicted mean, so the default predicted_ranks are $[1, 2, \dots, n - 1, n]$ .

method : {'auto', 'asymptotic', 'exact'}, optional

Selects the method used to calculate the p-value. The following options are available.

'auto': selects between 'exact' and 'asymptotic' to achieve reasonably accurate results in reasonable time (default)
'asymptotic': compares the standardized test statistic against the normal distribution
'exact': computes the exact p-value by comparing the observed $L$ statistic against those realized by all possible permutations of ranks (under the null hypothesis that each permutation is equally likely)

Returns

res : PageTrendTestResult

An object containing attributes:

statistic: statistic
pvalue: pvalue
method: method

Notes

As noted in ^[1], "the $n$ 'treatments' could just as well represent $n$ objects or events or performances or persons or trials ranked." Similarly, the $m$ 'subjects' could equally stand for $m$ "groupings by ability or some other control variable, or judges doing the ranking, or random replications of some other sort."

The procedure for calculating the $L$ statistic, adapted from ^[1], is:

"Predetermine with careful logic the appropriate hypotheses concerning the predicted ordering of the experimental results. If no reasonable basis for ordering any treatments is known, the $L$ test is not appropriate."
"As in other experiments, determine at what level of confidence you will reject the null hypothesis that there is no agreement of experimental results with the monotonic hypothesis."
"Cast the experimental material into a two-way table of $n$ columns (treatments, objects ranked, conditions) and $m$ rows (subjects, replication groups, levels of control variables)."
"When experimental observations are recorded, rank them across each row", e.g. ranks = scipy.stats.rankdata(data, axis=1).
"Add the ranks in each column", e.g. colsums = np.sum(ranks, axis=0).
"Multiply each sum of ranks by the predicted rank for that same column", e.g. products = predicted_ranks * colsums.
"Sum all such products", e.g. L = products.sum().

^[1] continues by suggesting use of the standardized statistic

χ_{L}^{2} = \frac{[ 12 L - 3 mn ( n + 1 ) ^{2} ] ^{2}}{m n ^{2} ( n ^{2} - 1 ) ( n + 1 )}

"which is distributed approximately as chi-square with 1 degree of freedom. The ordinary use of $χ^{2}$ tables would be equivalent to a two-sided test of agreement. If a one-sided test is desired, as will almost always be the case, the probability discovered in the chi-square table should be halved."

However, this standardized statistic does not distinguish between the observed values being well correlated with the predicted ranks and being _anti_-correlated with the predicted ranks. Instead, we follow ^[2] and calculate the standardized statistic

Λ = \frac{L - E _{0}}{V _{0}},

where $E_{0} = \frac{1}{4} mn (n + 1)^{2}$ and $V_{0} = \frac{1}{144} m n^{2} (n + 1) (n^{2} - 1)$ , "which is asymptotically normal under the null hypothesis".

The p-value for method='exact' is generated by comparing the observed value of $L$ against the $L$ values generated for all $(n!)^{m}$ possible permutations of ranks. The calculation is performed using the recursive method of [5].

The p-values are not adjusted for the possibility of ties. When ties are present, the reported 'exact' p-values may be somewhat larger (i.e. more conservative) than the true p-value ^[2]. The 'asymptotic'` p-values, however, tend to be smaller (i.e. less conservative) than the 'exact' p-values.

Array API Standard Support

page_trend_test has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ⛔                     ⛔                   
JAX                   ⛔                     ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

We use the example from [3]_: 10 students are asked to rate three teaching methods - tutorial, lecture, and seminar - on a scale of 1-5, with 1 being the lowest and 5 being the highest. We have decided that a confidence level of 99% is required to reject the null hypothesis in favor of our alternative: that the seminar will have the highest ratings and the tutorial will have the lowest. Initially, the data have been tabulated with each row representing an individual student's ratings of the three methods in the following order: tutorial, lecture, seminar.

table = [[3, 4, 3],
         [2, 2, 4],
         [3, 3, 5],
         [1, 3, 2],
         [2, 3, 2],
         [2, 4, 5],
         [1, 2, 4],
         [3, 4, 4],
         [2, 4, 5],
         [1, 3, 4]]

✓

Because the tutorial is hypothesized to have the lowest ratings, the column corresponding with tutorial rankings should be first; the seminar is hypothesized to have the highest ratings, so its column should be last. Since the columns are already arranged in this order of increasing predicted mean, we can pass the table directly into `page_trend_test`.

from scipy.stats import page_trend_test
res = page_trend_test(table)

✓

res

✗

This *p*-value indicates that there is a 0.1819% chance that the :math:`L` statistic would reach such an extreme value under the null hypothesis. Because 0.1819% is less than 1%, we have evidence to reject the null hypothesis in favor of our alternative at a 99% confidence level. The value of the :math:`L` statistic is 133.5. To check this manually, we rank the data such that high scores correspond with high ranks, settling ties with an average rank:

from scipy.stats import rankdata
ranks = rankdata(table, axis=1)
ranks

✓

We add the ranks within each column, multiply the sums by the predicted ranks, and sum the products.

import numpy as np
m, n = ranks.shape
predicted_ranks = np.arange(1, n+1)
L = (predicted_ranks * np.sum(ranks, axis=0)).sum()

✓

res.statistic == L

✗

As presented in [3]_, the asymptotic approximation of the *p*-value is the survival function of the normal distribution evaluated at the standardized test statistic:

from scipy.stats import norm
E0 = (m*n*(n+1)**2)/4
V0 = (m*n**2*(n+1)*(n**2-1))/144
Lambda = (L-E0)/np.sqrt(V0)
p = norm.sf(Lambda)

✓

✗

This does not precisely match the *p*-value reported by `page_trend_test` above. The asymptotic distribution is not very accurate, nor conservative, for :math:`m \leq 12` and :math:`n \leq 8`, so `page_trend_test` chose to use ``method='exact'`` based on the dimensions of the table and the recommendations in Page's original paper [1]_. To override `page_trend_test`'s choice, provide the `method` argument.

res = page_trend_test(table, method="asymptotic")

✓

res

✗

If the data are already ranked, we can pass in the ``ranks`` instead of the ``table`` to save computation time.

res = page_trend_test(ranks,             # ranks of data
                      ranked=True,       # data is already ranked
                      )

✓

res

✗

Suppose the raw data had been tabulated in an order different from the order of predicted means, say lecture, seminar, tutorial.

table = np.asarray(table)[:, [1, 2, 0]]

✓

Since the arrangement of this table is not consistent with the assumed ordering, we can either rearrange the table or provide the `predicted_ranks`. Remembering that the lecture is predicted to have the middle rank, the seminar the highest, and tutorial the lowest, we pass:

res = page_trend_test(table,             # data as originally tabulated
                      predicted_ranks=[2, 3, 1],  # our predicted order
                      )

✓

res

✗

Aliases

scipy.stats.page_trend_test

Referenced by

This package

release:1.7.0-notes