esda.Join_Counts_Local_BV

class esda.Join_Counts_Local_BV(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)

Univariate Local Join Count Statistic

__init__(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)

Initialize a Local_Join_Counts_BV estimator

Parameters:

connectivityscipy.sparse matrix object

the connectivity structure describing the relationships between observed units. Need not be row-standardized.

permutationspython:int

number of random permutations for calculation of pseudo p_values

n_jobspython:int

Number of cores to be used in the conditional randomisation. If -1, all available cores are used.

keep_simulationsBoolean

(default=True) If True, the entire matrix of replications under the null is stored in memory and accessible; otherwise, replications are not saved

seedNone/int

Seed to ensure reproducibility of conditional randomizations. Must be set here, and not outside of the function, since numba does not correctly interpret external seeds nor numpy.random.RandomState instances.

island_weight:

value to use as a weight for the “fake” neighbor for every island. If numpy.nan, will propagate to the final local statistic depending on the stat_func. If 0, then the lag is always zero for islands.

Methods

__init__([connectivity, permutations, ...])	Initialize a Local_Join_Counts_BV estimator
fit(x, z[, case, n_jobs, permutations])	Parameters:
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.