pointpats.mantel¶
- pointpats.mantel(s_coords, t_coords, permutations=99, scon=1.0, spow=-1.0, tcon=1.0, tpow=-1.0)[source]¶
Standardized Mantel test for spatio-temporal interaction. [Man67]
- Parameters:
- s_coordsarray
(n, 2), spatial coordinates.
- t_coordsarray
(n, 1), temporal coordinates.
- permutationsint, optional
the number of permutations used to establish pseudo- significance (the default is 99).
- sconfloat, optional
constant added to spatial distances (the default is 1.0).
- spowfloat, optional
value for power transformation for spatial distances (the default is -1.0).
- tconfloat, optional
constant added to temporal distances (the default is 1.0).
- tpowfloat, optional
value for power transformation for temporal distances (the default is -1.0).
- Returns:
- mantel_resultdictionary
contains the statistic (stat) for the test and the associated p-value (pvalue).
- statfloat
value of the knox test for the dataset.
- pvaluefloat
pseudo p-value associated with the statistic.
Examples
>>> import numpy as np >>> import libpysal as lps >>> from pointpats import SpaceTimeEvents, mantel
Read in the example data and create an instance of SpaceTimeEvents.
>>> path = lps.examples.get_path("burkitt.shp") >>> events = SpaceTimeEvents(path,'T')
Set the random seed generator. This is used by the permutation based inference to replicate the pseudo-significance of our example results - the end-user will normally omit this step.
>>> np.random.seed(100)
The standardized Mantel test is a measure of matrix correlation between the spatial and temporal distance matrices of the event dataset. The following example runs the standardized Mantel test without a constant or transformation; however, as recommended by [Man67], these should be added by the user. This can be done by adjusting the constant and power parameters.
>>> result = mantel(events.space, events.t, 99, scon=1.0, spow=-1.0, tcon=1.0, tpow=-1.0)
Next, we examine the result of the test.
>>> print("%6.6f"%result['stat']) 0.048368
Finally, we look at the pseudo-significance of this value, calculated by permuting the timestamps and rerunning the statistic for each of the 99 permutations. According to these parameters, the results indicate space-time interaction between the events.
>>> print("%2.2f"%result['pvalue']) 0.01