# pysal.explore.esda.Geary¶

class pysal.explore.esda.Geary(y, w, transformation='r', permutations=999)[source]

Global Geary C Autocorrelation statistic

Parameters: y : array (n, 1) attribute vector w : W spatial weights transformation : {‘B’, ‘R’, ‘D’, ‘U’, ‘V’} weights transformation, default is binary. Other options include “R”: row-standardized, “D”: doubly-standardized, “U”: untransformed (general weights), “V”: variance-stabilizing. permutations : int number of random permutations for calculation of pseudo-p_values

Notes

Technical details and derivations can be found in [CO81].

Examples

>>> import pysal.lib
>>> from pysal.explore.esda.geary import Geary
>>> f = pysal.lib.io.open(pysal.lib.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: y : array original variable w : W spatial weights permutations : int number of permutations C : float value of statistic EC : float expected value VC : float variance of G under normality assumption z_norm : float z-statistic for C under normality assumption z_rand : float z-statistic for C under randomization assumption p_norm : float p-value under normality assumption (one-tailed) p_rand : float p-value under randomization assumption (one-tailed) sim : array (if permutations!=0) vector of I values for permutated samples p_sim : float (if permutations!=0) p-value based on permutations (one-tailed) null: sptial randomness alternative: the observed C is extreme it is either extremely high or extremely low EC_sim : float (if permutations!=0) average value of C from permutations VC_sim : float (if permutations!=0) variance of C from permutations seC_sim : float (if permutations!=0) standard deviation of C under permutations. z_sim : float (if permutations!=0) standardized C based on permutations p_z_sim : float (if permutations!=0) p-value based on standard normal approximation from permutations (one-tailed)

Methods

 by_col(df, cols[, w, inplace, pvalue, outvals]) Function to compute a Geary statistic on a dataframe
__init__(y, w, transformation='r', permutations=999)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(y, w[, transformation, permutations]) Initialize self. by_col(df, cols[, w, inplace, pvalue, outvals]) Function to compute a Geary statistic on a dataframe