esda.Geary¶
- class esda.Geary(y, w, transformation='r', permutations=999)[source]¶
Global Geary C Autocorrelation statistic
- Parameters:
- w
W
spatial weights
- transformation{‘R’, ‘B’, ‘D’, ‘U’, ‘V’}
weights transformation, default is row-standardized. Other options include “B”: binary, “D”: doubly-standardized, “U”: untransformed (general weights), “V”: variance-stabilizing.
pseudo-p_values
Notes
Technical details and derivations can be found in [CO81].
Examples
>>> import libpysal
>>> from esda.geary import Geary
>>> w = libpysal.io.open(libpysal.examples.get_path("book.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("book.txt"))
>>> y = np.array(f.by_col['y'])
>>> c = Geary(y,w,permutations=0)
>>> round(c.C,7)
0.3330108
>>> round(c.p_norm,7)
9.2e-05
>>>
- Attributes:
- w
W
spatial weights
vector of I values for permutated samples
p-value based on permutations (one-tailed)
null: sptial randomness
alternative: the observed C is extreme
it is either extremely high or extremely low
average value of C from permutations
variance of C from permutations
standard deviation of C under permutations.
standardized C based on permutations
p-value based on standard normal approximation from
permutations (one-tailed)
Methods
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Function to compute a Geary statistic on a dataframe |
- classmethod by_col(df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]¶
Function to compute a Geary 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, with default configurations,
the derived columns will be named like ‘column_geary’ and ‘column_p_sim’
the Geary statistic’s documentation for available p-values
Geary statistic
documentation for the Geary statistic.
Notes
Technical details and derivations can be found in [CO81].