esda.G¶
- class esda.G(y, w, permutations=999)[source]¶
Global G Autocorrelation Statistic
- Parameters:
Notes
Moments are based on normality assumption.
For technical details see [] and [].
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:
- y
array
original variable
- w
W
DistanceBand W spatial weights based on distance band
- permutation
int
the number of permutations
- G
float
the value of statistic
- EG
float
the expected value of statistic
- VG
float
the variance of G under normality assumption
- z_norm
float
standard normal test statistic
- p_norm
float
p-value under normality assumption (one-sided)
- sim
array
(if permutations > 0) vector of G values for permutated samples
- p_sim
float
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_sim
float
average value of G from permutations
- VG_sim
float
variance of G from permutations
- seG_sim
float
standard deviation of G under permutations.
- z_sim
float
standardized G based on permutations
- p_z_sim
float
p-value based on standard normal approximation from permutations (one-sided)
- y
Methods
__init__
(y, w[, permutations])by_col
(df, cols[, w, inplace, pvalue, outvals])Function to compute a G statistic on a dataframe