esda.Geary_Local_MV¶

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

Local Geary - Multivariate

Initialize a Local_Geary_MV estimator

Parameters:¶

connectivity : W | Graph¶: spatial weights instance as W or Graph aligned with y
permutations : int¶: (default=999) number of random permutations for calculation of pseudo p_values
drop_islands : bool (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().

localG[source]¶

array containing the observed multivariate Local Geary values.

Type:¶: numpy array

p_sim[source]¶

array containing the simulated p-values for each unit.

Type:¶: numpy array

Methods

`fit`(variables)
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_fit_request`(*[, variables])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.

fit(variables)[source]¶

Parameters:¶

variables : numpy.ndarray¶: array containing continuous data

Return type:¶

the fitted estimator.

Notes

Technical details and derivations can be found in [Anselin, 1995].

Examples

Guerry data replication GeoDa tutorial

>>> import libpysal
>>> import geopandas as gpd
>>> from esda import Geary_Local_MV
>>> guerry = libpysal.examples.load_example('Guerry')
>>> guerry_ds = gpd.read_file(guerry.get_path('guerry.shp'))
>>> w = libpysal.weights.Queen.from_dataframe(guerry_ds, use_index=False)
>>> x1 = guerry_ds['Donatns']
>>> x2 = guerry_ds['Suicids']
>>> lG_mv = Geary_Local_MV(connectivity=w).fit([x1, x2])
>>> lG_mv.localG[0:5]
array([0.15381853, 0.30355953, 2.95472008, 0.12313959, 0.38795991])
>>> lG_mv.p_sim[0:5]
array([0.012, 0.004, 0.016, 0.021, 0.252])

get_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:¶: routing – A MetadataRequest encapsulating routing information.
Return type:¶: MetadataRequest

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:¶

deep : bool, default=True¶: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:¶

params – Parameter names mapped to their values.

Return type:¶

dict

set_fit_request(*, variables='$UNCHANGED$')[source]¶

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Parameters:¶

variables : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED¶: Metadata routing for variables parameter in fit.

Returns:¶

self – The updated object.

Return type:¶

object

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:¶

**params : dict¶: Estimator parameters.

Returns:¶

self – Estimator instance.

Return type:¶

estimator instance