esda.Geary_Local_MV

class esda.Geary_Local_MV(connectivity=None, permutations=999, drop_islands=True)[source]

Local Geary - Multivariate

__init__(connectivity=None, permutations=999, drop_islands=True)[source]

Initialize a Local_Geary_MV estimator

Parameters:
connectivityW | Graph

spatial weights instance as W or Graph aligned with y

permutationsint

(default=999) number of random permutations for calculation of pseudo p_values

drop_islandsbool (default True)

Whether or not to preserve islands as entries in the adjacency list. By default, observations with no neighbors do not appear in the adjacency list. If islands are kept, they are coded as self-neighbors with zero weight. See libpysal.weights.to_adjlist().

Attributes:
localGnumpy array

array containing the observed multivariate Local Geary values.

p_simnumpy array

array containing the simulated p-values for each unit.

Methods

__init__([connectivity, permutations, ...])

Initialize a Local_Geary_MV estimator

fit(variables)

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_fit_request(*[, variables])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.
fit(variables)[source]
Parameters:
variablesnumpy.ndarray

array containing continuous data

Returns:
the fitted estimator.

Notes

Technical details and derivations can be found in [].

Examples

Guerry data replication GeoDa tutorial >>> import libpysal >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> import libpysal >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> x1 = guerry_ds[‘Donatns’] >>> x2 = guerry_ds[‘Suicids’] >>> lG_mv = Local_Geary(connectivity=w).fit([x1,x2]) >>> lG_mv.localG[0:5] >>> lG_mv.p_sim[0:5]

set_fit_request(*, variables: bool | None | str = '$UNCHANGED$') → Geary_Local_MV

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
variablesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for variables parameter in fit.

Returns:
selfobject

The updated object.