esda.Moran_Local_BV¶
- class esda.Moran_Local_BV(x, y, w, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Bivariate Local Moran Statistics.
- Parameters:
- w
W
|Graph
spatial weights instance as W or Graph aligned with y
- transformation{‘R’, ‘B’, ‘D’, ‘U’, ‘V’}
weights transformation, default is row-standardized “r”. Other options include “B”: binary, “D”: doubly-standardized, “U”: untransformed (general weights), “V”: variance-stabilizing.
p_values
If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4
If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4
- keep_simulations
Boolean
(default=True) If True, the entire matrix of replications under the null is stored in memory and accessible; otherwise, replications are not saved
- seedNone/int
Seed to ensure reproducibility of conditional randomizations. Must be set here, and not outside of the function, since numba does not correctly interpret external seeds nor numpy.random.RandomState instances.
- island_weight:
value to use as a weight for the “fake” neighbor for every island. If numpy.nan, will propagate to the final local statistic depending on the stat_func. If 0, then the lag is always zero for islands.
Examples
>>> import libpysal
>>> import numpy as np
>>> np.random.seed(10)
>>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
>>> x = np.array(f.by_col['SIDR79'])
>>> y = np.array(f.by_col['SIDR74'])
>>> from esda.moran import Moran_Local_BV
>>> lm =Moran_Local_BV(x, y, w, transformation = "r", permutations = 99)
>>> lm.q[:10]
array([3, 4, 3, 4, 2, 1, 4, 4, 2, 4])
>>> lm = Moran_Local_BV(x, y, w, transformation = "r", permutations = 99, geoda_quads=True)
>>> lm.q[:10]
array([2, 4, 2, 4, 3, 1, 4, 4, 3, 4])
Note random components result is slightly different values across architectures so the results have been removed from doctests and will be moved into unittests that are conditional on architectures.
- Attributes:
- w
W
|Graph
original w object
values indicate quandrant location 1 HH, 2 LH, 3 LL, 4 HL
p-value based on permutations (one-sided)
null: spatial randomness
alternative: the observed Ii is further away or extreme
from the median of simulated values. It is either extremelyi
high or extremely low in the distribution of simulated Is.
average values of local Is from permutations
variance of Is from permutations
- seI_sim: array
(if permutations>0) standard deviations of Is under permutations.
- z_sim
arrray
(if permutations>0) standardized Is based on permutations
- p_z_sim: array
(if permutations>0) p-values based on standard normal approximation from permutations (one-sided) for two-sided tests, these values should be multiplied by 2
- __init__(x, y, w, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Methods
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Function to compute a Moran_Local_BV statistic on a dataframe |
- classmethod by_col(df, x, y=None, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]¶
Function to compute a Moran_Local_BV statistic on a dataframe
- Parameters:
the bivariate statistic. if no Y is provided, pariwise comparisons
among the X variates are used instead.
- w
W
|Graph
spatial weights instance as W or Graph aligned with the dataframe. If not provided, this is searched for in the dataframe’s metadata
return a series contaning the results of the computation. If
operating inplace, the derived columns will be named
‘column_moran_local_bv’
the Moran_Local_BV statistic’s documentation for available p-values
Moran_Local_BV statistic
documentation for the Moran_Local_BV statistic.
the
relevant
columns
attached.