esda.Geary_Local_MV¶
- class esda.Geary_Local_MV(connectivity=None, permutations=999, drop_islands=True)[source]¶
Local Geary - Multivariate
- __init__(connectivity=None, permutations=999, drop_islands=True)[source]¶
Initialize a Local_Geary_MV estimator
- Parameters:
- connectivity
scipy.sparse
matrix
object
the connectivity structure describing the relationships between observed units. Need not be row-standardized.
- permutations
int
(default=999) number of random permutations for calculation of pseudo p_values
- drop_islandsbool (default
True
) Whether or not to preserve islands as entries in the adjacency 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()
.
- connectivity
- Attributes:
Methods
__init__
([connectivity, permutations, ...])Initialize a Local_Geary_MV estimator
fit
(variables)- Parameters:
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- fit(variables)[source]¶
- Parameters:
- variables
numpy.ndarray
array containing continuous data
- variables
- Returns:
the
fitted estimator.
Notes
Technical details and derivations can be found in [].
Examples
Guerry data replication GeoDa tutorial >>> import libpysal >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> import libpysal >>> import geopandas as gpd >>> guerry = lp.examples.load_example(‘Guerry’) >>> guerry_ds = gpd.read_file(guerry.get_path(‘Guerry.shp’)) >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds) >>> x1 = guerry_ds[‘Donatns’] >>> x2 = guerry_ds[‘Suicids’] >>> lG_mv = Local_Geary(connectivity=w).fit([x1,x2]) >>> lG_mv.localG[0:5] >>> lG_mv.p_sim[0:5]