esda.Join_Counts_Local_MV¶
- class esda.Join_Counts_Local_MV(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Multivariate Local Join Count Statistic
- __init__(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Initialize a Local_Join_Counts_MV estimator
- Parameters:
- connectivity
scipy.sparse
matrix
object
the connectivity structure describing the relationships between observed units. Need not be row-standardized.
- permutations
python:int
number of random permutations for calculation of pseudo p_values
- n_jobs
python:int
Number of cores to be used in the conditional randomisation. If -1, all available cores are used.
- keep_simulations
Boolean
(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.
- connectivity
Methods
__init__
([connectivity, permutations, ...])Initialize a Local_Join_Counts_MV estimator
fit
(variables[, n_jobs, permutations])- Parameters:
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.