esda.LOSH

class esda.LOSH(connectivity=None, inference=None)

Local spatial heteroscedasticity (LOSH)

__init__(connectivity=None, inference=None)

Initialize a losh estimator

Parameters:

connectivityW | Graph

spatial weights instance as W or Graph aligned with y

inferencepython:str

describes type of inference to be used. options are “chi-square” or “permutation” methods.

Attributes:

Hinumpy array

Array of LOSH values for each spatial unit.

ylagnumpy array

Spatially lagged y values.

yresidnumpy array

Spatially lagged residual values.

VarHinumpy array

Variance of Hi.

pvalnumpy array

P-values for inference based on either “chi-square” or “permutation” methods.

Methods

__init__([connectivity, inference])	Initialize a losh estimator
fit(y[, a])
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_fit_request(*[, a])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.

fit(y, a=2)

Parameters:

ynumpy.ndarray

array containing continuous data

apython:int

residual multiplier. Default is 2 in order to generate a variance measure. Users may use 1 for absolute deviations.

Returns:

the fitted estimator.

Notes

Technical details and derivations can be found in [].

Examples

>>> import libpysal
>>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("stl_hom.txt"))
>>> y = np.array(f.by_col['HR8893'])
>>> from esda import losh
>>> ls = losh(connectivity=w, inference="chi-square").fit(y)
>>> np.round(ls.Hi[0], 3)
>>> np.round(ls.pval[0], 3)

Boston housing data replicating R spdep::LOSH() >>> import libpysal >>> import geopandas as gpd >>> boston = libpysal.examples.load_example(‘Bostonhsg’) >>> boston_ds = gpd.read_file(boston.get_path(‘boston.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(boston_ds) >>> ls = losh(connectivity=w, inference=”chi-square”).fit(boston[‘NOX’]) >>> np.round(ls.Hi[0], 3) >>> np.round(ls.VarHi[0], 3)

set_fit_request(*, a: bool | None | str = '$UNCHANGED$') → LOSH

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:

apython:str, python:True, python:False, or python:None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for a parameter in fit.

Returns:

selfobject

The updated object.