scipy.stats.

PermutationMethod#

class scipy.stats.PermutationMethod(n_resamples=9999, batch=None, rng=None)[source]#

Configuration information for a permutation hypothesis test.

Instances of this class can be passed into the method parameter of some hypothesis test functions to perform a permutation version of the hypothesis tests.

Attributes:

n_resamplesint, optional

The number of resamples to perform. Default is 9999.

batchint, optional

The number of resamples to process in each vectorized call to the statistic. Batch sizes >>1 tend to be faster when the statistic is vectorized, but memory usage scales linearly with the batch size. Default is None, which processes all resamples in a single batch.

rngnumpy.random.Generator, optional

Pseudorandom number generator used to perform resampling.

If rng is passed by keyword to the initializer or the rng attribute is used directly, types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator. If rng is already a Generator instance, then the provided instance is used. Specify rng for repeatable behavior.

If this argument is passed by position, if random_state is passed by keyword into the initializer, or if the random_state attribute is used directly, legacy behavior for random_state applies:

If random_state is None (or numpy.random), the numpy.random.RandomState singleton is used.
If random_state is an int, a new RandomState instance is used, seeded with random_state.
If random_state is already a Generator or RandomState instance then that instance is used.

Changed in version 1.15.0: As part of the SPEC-007 transition from use of numpy.random.RandomState to numpy.random.Generator, this attribute name was changed from random_state to rng. For an interim period, both names will continue to work, although only one may be specified at a time. After the interim period, uses of random_state will emit warnings. The behavior of both random_state and rng are outlined above, but only rng should be used in new code.