View source: R/separability.test.R
separability.test | R Documentation |
Performs a separability test of the first-order intensity function based on a Fisher Monte Carlo test of cell counts.
separability.test(X, t = NULL, nx = NULL, ny = NULL, nt = NULL, nperm = 1000)
X |
A spatial point pattern (an object of class |
t |
A numeric vector of temporal coordinates with equal length to the number of points in |
nx, ny, nt |
Numbers of quadrats in the x,y and t directions. |
nperm |
An integer specifying the number of replicates used in the Monte Carlo test. |
This function performs a basic test of the separability hypothesis in a manner similar to independence test in two-way contingency tables. The test is conditional on the observed number of points. It considers a regular division of the interval T into disjoint sub-intervals T_1,...,T_{n_t} and similarly a division of the window W into disjoint subsets W_1, ..., W{n_x \times n_y}. Then the function computes Fisher's test statistic and get a p-value based on Monte Carlos approximation.
A list with class "htest" containing the following components:
p.value |
the approximate p-value of the test. |
method |
the character string "Separability test based on Fisher's for counting data". |
alternative |
a character string describing the alternative hypothesis. |
data.name |
a character string giving the name(s) of the data. |
This is a fast preliminary separability test.
Jonatan A. González
Ghorbani et al. (2021) Testing the first-order separability hypothesis for spatio-temporal point patterns, Computational Statistics & Data Analysis, 161, p.107245.
data(lGCpp) separability.test(lGCpp, nx = 5, ny = 4, nt = 3, nperm = 500) data(aegiss) separability.test(aegiss, nx = 8, ny = 8, nt = 4)
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