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:
- Attributes:
Methods
__init__
([connectivity, inference])Initialize a losh estimator
fit
(y[, a])- Parameters:
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- fit(y, a=2)[source]¶
- Parameters:
- y
numpy.ndarray
array containing continuous data
- a
int
residual multiplier. Default is 2 in order to generate a variance measure. Users may use 1 for absolute deviations.
- y
- 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)