esda.Moran_Local¶
- class esda.Moran_Local(y, w, transformation='r', permutations=999, geoda_quads=False, n_jobs=1, keep_simulations=True, seed=None, island_weight=0)[source]¶
Local Moran Statistics.
- Parameters:
- w
W
weight instance assumed to be 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
all available cores are used.
- 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.
Notes
For technical details see [Ans95].
Examples
>>> import libpysal
>>> import numpy as np
>>> np.random.seed(10)
>>> w = libpysal.io.open(libpysal.examples.get_path("desmith.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("desmith.txt"))
>>> y = np.array(f.by_col['z'])
>>> from esda.moran import Moran_Local
>>> lm = Moran_Local(y, w, transformation = "r", permutations = 99)
>>> lm.q
array([4, 4, 4, 2, 3, 3, 1, 4, 3, 3])
>>> lm.p_z_sim[0]
0.24669152541631179
>>> lm = Moran_Local(y, w, transformation = "r", permutations = 99, geoda_quads=True)
>>> lm.q
array([4, 4, 4, 3, 2, 2, 1, 4, 2, 2])
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
original w object
values indicate quandrant location 1 HH, 2 LH, 3 LL, 4 HL
I values for permuted samples
p-values 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 extremely
high or extremely low in the distribution of simulated Is.
average values of local Is from permutations
variance of Is from permutations
from []. Is the same at each site,
and equal to the expectation of I itself when
transformation=’r’. We recommend using EI_sim, not EI,
for analysis. This EI is only provided for reproducibility.
from []. Varies according only to
cardinality. We recommend using VI_sim, not VI, for
analysis. This VI is only provided for reproducibility.
from [SOT98]. Varies strongly by site, since it
conditions on z_i. We recommend using EI_sim, not EIc,
for analysis. This EIc is only provided for reproducibility.
from [SOT98]. Varies strongly by site, since
it conditions on z_i. We recommend using VI_sim, not VIc,
for analysis. This VIc is only provided for reproducibility.
standard deviations of Is under permutations.
- z_sim
arrray
(if permutations>0) standardized Is based on permutations
p-values based on standard normal approximation from
permutations (one-sided)
for two-sided tests, these values should be multiplied by 2
all available cores are used.
- 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.
- __init__(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 statistic on a dataframe. |
- classmethod by_col(df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]¶
Function to compute a Moran_Local statistic on a dataframe.
- Parameters:
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’
the Moran_Local statistic’s documentation for available p-values
Moran_Local statistic
documentation for the Moran_Local statistic.
the
relevant
columns
attached.