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

__init__(connectivity=None, permutations=999, n_jobs=1, keep_simulations=True, seed=None, island_weight=0, drop_islands=True)[source]

Initialize a Local_Join_Counts_MV estimator

Parameters:
the relationships between observed units. Need not be row-standardized.

all available cores are used.

in memory and accessible; otherwise, replications are not saved

Must be set here, and not outside of the function, since numba does not correctly interpret external seeds nor numpy.random.RandomState instances.

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.

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().

Methods

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

Initialize a Local_Join_Counts_MV estimator

fit(variables[, n_jobs, permutations])

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:
Returns
——-
the fitted estimator.

Notes

Technical details and derivations can be found in [].

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

Request metadata passed to the fit method.

Note that this method is only relevant if 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

Parameters:
Returns: