scipy.stats.

MonteCarloMethod

class scipy.stats.MonteCarloMethod(n_resamples=9999, batch=None, rvs=None, rng=None)

Configuration information for a Monte Carlo hypothesis test.

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

Attributes:

n_resamplesint, optional

The number of Monte Carlo samples to draw. Default is 9999.

batchint, optional

The number of Monte Carlo samples 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 samples in a single batch.

rvscallable or tuple of callables, optional

A callable or sequence of callables that generates random variates under the null hypothesis. Each element of rvs must be a callable that accepts keyword argument size (e.g. rvs(size=(m, n))) and returns an N-d array sample of that shape. If rvs is a sequence, the number of callables in rvs must match the number of samples passed to the hypothesis test in which the MonteCarloMethod is used. Default is None, in which case the hypothesis test function chooses values to match the standard version of the hypothesis test. For example, the null hypothesis of scipy.stats.pearsonr is typically that the samples are drawn from the standard normal distribution, so rvs = (rng.normal, rng.normal) where rng = np.random.default_rng().

rngnumpy.random.Generator, optional

Pseudorandom number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator.