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
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
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Initialize a Local_Join_Counts_MV estimator |
|
|
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Get metadata routing of this object. |
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Get parameters for this estimator. |
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Request metadata passed to the |
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Set the parameters of this 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 tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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: