esda.Geary_Local¶
- class esda.Geary_Local(connectivity=None, labels=False, sig=0.05, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Local Geary - Univariate
- __init__(connectivity=None, labels=False, sig=0.05, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Initialize a Local_Geary estimator
- Parameters:
- connectivity
scipy.sparse
matrix
object
the connectivity structure describing the relationships between observed units. Need not be row-standardized.
- labelsbool
(default=False) If True use, label if an observation belongs to an outlier, cluster, other, or non-significant group. 1 = outlier, 2 = cluster, 3 = other, 4 = non-significant. Note that this is not the exact same as the cluster map produced by GeoDa.
- sig
float
(default=0.05) Default significance threshold used for creation of labels groups.
- permutations
int
(default=999) number of random permutations for calculation of pseudo p_values
- n_jobs
int
(default=1) 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
- Attributes:
Methods
__init__
([connectivity, labels, sig, ...])Initialize a Local_Geary estimator
fit
(x)- Parameters:
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.