psam_mc: Polygons Spatial Association Test - PSAM
In lcgodoy/tpsa: Testing Polygons Spatial Association

psam_mc

R Documentation

Polygons Spatial Association Test - PSAM

Description

A Monte Carlo test based on the test statistic psam, to verify if two sets of polygons are associated.

Usage

psam_mc(
  obj_sp1,
  obj_sp2,
  n_sim = 499L,
  unique_bbox = NULL,
  alpha = 0.05,
  alternative = "two_sided",
  fixed = FALSE,
  hausdorff = F,
  ...
)

Arguments

`obj_sp1`	an object from class `SpatialPolygons` or `SpatialPointsDataFrame`
`obj_sp2`	an object from class `SpatialPolygons` or `SpatialPointsDataFrame`
`n_sim`	an `integer` corresponding to the number of Monte Carlo simulations for the test
`unique_bbox`	a `matrix` `2 \times 2` corresponding to the boundary box that contains both sets
`alpha`	a `numeric` indicating the confidence level
`alternative`	a `character` indicating the alternative hypothesis, it can be: "two_sided", "repulsion", or "attraction" if you interest is only check if the sets are independent or not, if the two sets repulses each other, or if the two sets attracts each other, respectively.
`fixed`	a `boolean` indicating if the first pattern should be fixed on the toroidal shift or the first will be fixed in half of iterations and then the other one. `TRUE` or `FALSE`, respectively.
`hausdorff`	a `boolean`. If `TRUE`, then the Hausdorff distance is used. Otherwise the Euclidean distance is used. Default is `FALSE`.
`...`	parameters for test statistics functions

Value

a list from class psam_test, with values:

p_value: a numeric scalar giving the p-value of the test
mc_sample: a numeric vector giving the test statistic for each of the Monte Carlo simulations
alternative: a character giving the alternative hypothesis
alpha: a numeric scalar giving the significance level
rejects: a logical scalar, TRUE if the null hypothesis is reject