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, drop_islands=True)[source]

Multivariate Local Join Count Statistic

Methods

fit(variables[, n_jobs, permutations])

Parameters:

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_fit_request(*[, n_jobs, permutations, ...])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.
fit(variables, n_jobs=1, permutations=999)[source]
Parameters:
variablesnumpy.ndarray

array(s) containing binary (0/1) data

Returns
——-
the fitted estimator.

Notes

Technical details and derivations can be found in [AL19].

Examples

>>> import libpysal
>>> w = libpysal.weights.lat2W(4, 4)
>>> x = np.ones(16)
>>> x[0:8] = 0
>>> z = [0,1,0,1,1,1,1,1,0,0,1,1,0,0,1,1]
>>> y = [0,1,1,1,1,1,1,1,0,0,0,1,0,0,1,1]
>>> LJC_MV = Local_Join_Counts_MV(connectivity=w).fit([x, y, z])
>>> LJC_MV.LJC
>>> LJC_MV.p_sim

Guerry data extending GeoDa tutorial >>> import libpysal >>> import geopandas as gpd >>> guerry = libpysal.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> guerry_ds[‘infq5’] = 0 >>> guerry_ds[‘donq5’] = 0 >>> guerry_ds[‘suic5’] = 0 >>> guerry_ds.loc[(guerry_ds[‘Infants’] > 23574), ‘infq5’] = 1 >>> guerry_ds.loc[(guerry_ds[‘Donatns’] > 10973), ‘donq5’] = 1 >>> guerry_ds.loc[(guerry_ds[‘Suicids’] > 55564), ‘suic5’] = 1 >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> LJC_MV = Local_Join_Counts_MV( … connectivity=w … ).fit([guerry_ds[‘infq5’], guerry_ds[‘donq5’], guerry_ds[‘suic5’]]) >>> LJC_MV.LJC >>> LJC_MV.p_sim

set_fit_request(*, n_jobs: bool | None | str = '$UNCHANGED$', permutations: bool | None | str = '$UNCHANGED$', variables: bool | None | str = '$UNCHANGED$') → Join_Counts_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.

New 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:
n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for n_jobs parameter in fit.

permutationsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for permutations parameter in fit.

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

Metadata routing for variables parameter in fit.

Returns:
selfobject

The updated object.