![]() ![]() That is, whenĬalculating the proportion of the randomized null distribution that isĪs extreme as the observed value of the test statistic, the values in Rather than the unbiased estimator suggested in. Note that p-values for randomized tests are calculated according to theĬonservative (over-estimated) approximation suggested in and 'two-sided' (default) : twice the smaller of the p-values above. Less than or equal to the observed value of the test statistic. 'less' : the percentage of the null distribution that is ![]() Greater than or equal to the observed value of the test statistic. 'greater' : the percentage of the null distribution that is The alternative hypothesis for which the p-value is calculated.įor each alternative, the p-value is defined for exact tests as If vectorized is set True, statistic must also accept a keywordĪrgument axis and be vectorized to compute the statistic along the statistic must be a callable that accepts samplesĪs separate arguments (e.g. Statistic for which the p-value of the hypothesis test is to beĬalculated. Parameters : data iterable of array-likeĬontains the samples, each of which is an array of observations.ĭimensions of sample arrays must be compatible for broadcasting except That the data are paired at random or that the data are assigned to samplesĪt random. Randomly sampled from the same distribution.įor paired sample statistics, two null hypothesis can be tested: Performs a permutation test of a given statistic on provided data.įor independent sample statistics, the null hypothesis is that the data are permutation_test ( data, statistic, *, permutation_type = 'independent', vectorized = None, n_resamples = 9999, batch = None, alternative = 'two-sided', axis = 0, random_state = None ) # Statistical functions for masked arrays ( Again you could sample more matrices than you want and then discard the ones that don't meet this requirement also.K-means clustering and vector quantization ( Your requirement to have different numbers of changes per row, also isn't covered here. Also, you could draw more random matrices than you need and discard ones that don't match all your requirements. binary 0/1 data.Ī couple of things to note, this doesn't guarantee that any column or row has been randomised, but if burnin is long enough there should be a good chance of that having happened.
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