esda.Geary¶
- class 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{‘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.
- permutations
int
number of random permutations for calculation of pseudo-p_values
- y
Notes
Technical details and derivations can be found in [].
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
- 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)
- y
Methods
__init__
(y, w[, transformation, permutations])by_col
(df, cols[, w, inplace, pvalue, outvals])Function to compute a Geary statistic on a dataframe