esda.Spatial_Pearson¶

class esda.Spatial_Pearson(connectivity=None, permutations=999)[source]¶

Global Spatial Pearson Statistic

Initialize a spatial pearson estimator

Parameters:¶

connectivity : scipy.sparse matrix object¶: the connectivity structure describing the relationships between observed units. Will be row-standardized.
permutations : int¶: the number of permutations to conduct for inference. if < 1, no permutational inference will be conducted.

array containg the estimated Lee spatial pearson correlation coefficients, where element [0,1] is the spatial correlation coefficient, and elements [0,0] and [1,1] are the “spatial smoothing factor”

Type:¶: numpy.ndarray (2,2)

reference_distribution_[source]¶

distribution of correlation matrices for randomly-shuffled maps.

Type:¶: numpy.ndarray (n_permutations, 2,2)

significance_[source]¶

permutation-based p-values for the fraction of times the observed correlation was more extreme than the simulated correlations.

Type:¶: numpy.ndarray (2,2)

Methods

`fit`(x, y)	bivariate spatial pearson's R based on Eq.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_fit_request`(*[, x])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.

fit(x, y)[source]¶

bivariate spatial pearson’s R based on Eq. 18 of [Lee, 2001].

L = dfrac{Z^T (V^TV) Z}{1^T (V^TV) 1}

Parameters:¶

x : numpy.ndarray¶: array containing continuous data
y : numpy.ndarray¶: array containing continuous data

Return type:¶

the fitted estimator.

Notes

Technical details and derivations can be found in [Lee, 2001].

get_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:¶: routing – A MetadataRequest encapsulating routing information.
Return type:¶: MetadataRequest

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:¶

deep : bool, default=True¶: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:¶

params – Parameter names mapped to their values.

Return type:¶

dict

set_fit_request(*, x='$UNCHANGED$')[source]¶

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:¶

x : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED¶: Metadata routing for x parameter in fit.

Returns:¶

self – The updated object.

Return type:¶

object

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:¶

**params : dict¶: Estimator parameters.

Returns:¶

self – Estimator instance.

Return type:¶

estimator instance