esda.G¶
-
class
esda.G(y, w, permutations=999)[source]¶ Global G Autocorrelation Statistic
- Parameters
- yarray (n,1)
Attribute values
- wW
DistanceBand W spatial weights based on distance band
- permutationsint
the number of random permutations for calculating pseudo p_values
Notes
Moments are based on normality assumption.
For technical details see [GO10] and [OG10].
Examples
>>> import libpysal >>> import numpy >>> numpy.random.seed(10)
Preparing a point data set
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
Creating a weights object from points
>>> w = libpysal.weights.DistanceBand(points,threshold=15) >>> w.transform = "B"
Preparing a variable
>>> y = numpy.array([2, 3, 3.2, 5, 8, 7])
Applying Getis and Ord G test
>>> from esda.getisord import G >>> g = G(y,w)
Examining the results
>>> round(g.G, 3) 0.557
>>> round(g.p_norm, 3) 0.173
- Attributes
- yarray
original variable
- wW
DistanceBand W spatial weights based on distance band
- permutationint
the number of permutations
- Gfloat
the value of statistic
- EGfloat
the expected value of statistic
- VGfloat
the variance of G under normality assumption
- z_normfloat
standard normal test statistic
- p_normfloat
p-value under normality assumption (one-sided)
- simarray
(if permutations > 0) vector of G values for permutated samples
- p_simfloat
p-value based on permutations (one-sided) null: spatial randomness alternative: the observed G is extreme it is either extremely high or extremely low
- EG_simfloat
average value of G from permutations
- VG_simfloat
variance of G from permutations
- seG_simfloat
standard deviation of G under permutations.
- z_simfloat
standardized G based on permutations
- p_z_simfloat
p-value based on standard normal approximation from permutations (one-sided)
Methods
by_col(df, cols[, w, inplace, pvalue, outvals])Function to compute a G statistic on a dataframe
-
__init__(self, y, w, permutations=999)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(self, y, w[, permutations])Initialize self.
by_col(df, cols[, w, inplace, pvalue, outvals])Function to compute a G statistic on a dataframe