esda.LOSH¶

class esda.LOSH(connectivity=None, inference=None)[source]¶

Local spatial heteroscedasticity (LOSH)

__init__(connectivity=None, inference=None)[source]¶

Initialize a losh estimator

Parameters:

connectivityW | Graph: spatial weights instance as W or Graph aligned with y
inferencestr: 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])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.

fit(y, a=2)[source]¶

Parameters:

ynumpy.ndarray: array containing continuous data
aint: 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¶

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:

astr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for a parameter in fit.

Returns:

selfobject: The updated object.