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adding alternative option in local_moran and moral_local_rate #205

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adding alternative option in local_moran and moral_local_rate #205

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22ed3af
adding alternative option in local_moran and moral_local_rate
Mar 31, 2022
cdcd839
convert Moran_BV_matrix into a class
Mar 31, 2022
f197358
convert Moran_BV_matrix into a class correct
Mar 31, 2022
c4892d9
changed test and documentation
Apr 7, 2022
5b4435d
reverting Moran_BV_matrix to default
Apr 8, 2022
a89ae03
updated documentation , test coverage is still left
Apr 12, 2022
f3c9a1c
added test coverage for local_moran
Apr 12, 2022
1ae6263
changed test
Apr 12, 2022
68595a3
changed test for single-tailed
Apr 12, 2022
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26 changes: 12 additions & 14 deletions esda/moran.py
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Original file line number Diff line number Diff line change
Expand Up @@ -509,9 +509,7 @@ def by_col(
def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
"""
Bivariate Moran Matrix

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It seems that your editor removed blank lines in the docstring. Can you please put them back (all of them)?

Calculates bivariate Moran between all pairs of a set of variables.

Parameters
----------
variables : array or pandas.DataFrame
Expand All @@ -531,36 +529,24 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
results : dictionary
(i, j) is the key for the pair of variables, values are
the Moran_BV objects.

Examples
--------

open dbf

>>> import libpysal
>>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))

pull of selected variables from dbf and create numpy arrays for each

>>> varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79']
>>> vars = [np.array(f.by_col[var]) for var in varnames]

create a contiguity matrix from an external gal file

>>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()

create an instance of Moran_BV_matrix

>>> from esda.moran import Moran_BV_matrix
>>> res = Moran_BV_matrix(vars, w, varnames = varnames)

check values

>>> round(res[(0, 1)].I,7)
0.1936261
>>> round(res[(3, 0)].I,7)
0.3770138

"""
try:
# check if pandas is installed
Expand Down Expand Up @@ -889,6 +875,9 @@ class Moran_Local(object):
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.
alternative: string
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Suggested change
alternative: string
alternative: {"two-tailed", "one-tailed"}
(default="two-tailed")

This follows the pattern used above.

possible values -> "one-tailed"/ "two-tailed"
default value ->"two-tailed"
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this part should contain a description of what the argument controls, i.e. what does it mean if I use "one-tailed" vs "two-tailed". Does not have to be detailed but just a hint, an explanation in plain words. Like in permutations, where we say "number of random permutations for calculation of pseudo p_values".


Attributes
----------
Expand Down Expand Up @@ -1005,6 +994,7 @@ def __init__(
keep_simulations=True,
seed=None,
island_weight=0,
alternative = "two-tailed"
):
y = np.asarray(y).flatten()
self.y = y
Expand Down Expand Up @@ -1063,6 +1053,12 @@ def __init__(
self.VI_sim = np.nan
self.z_sim = np.nan
self.p_z_sim = np.nan
if alternative=="one-tailed":
self.p_z_sim= self.p_z_sim*2
folded_replicates = np.abs(self.rlisas - np.median(self.rlisas, axis=1, keepdims=True))
self.p_sim = (folded_replicates >= np.abs(self.Is[:,None])).mean(axis=1)



def __calc(self, w, z):
zl = slag(w, z)
Expand Down Expand Up @@ -1575,6 +1571,7 @@ def __init__(
keep_simulations=True,
seed=None,
island_weight=0,
alternative="two-tailed"
):
e = np.asarray(e).flatten()
b = np.asarray(b).flatten()
Expand All @@ -1592,6 +1589,7 @@ def __init__(
n_jobs=n_jobs,
keep_simulations=keep_simulations,
seed=seed,
alternative=alternative
)

@classmethod
Expand Down