test global spatial autocorrelation — moran

Spatial autocorrelation test based on global moran index.

Usage

moran_test(sfj, wt = NULL, alternative = "greater", symmetrize = FALSE)

Arguments

sfj: An sf object or can be converted to sf by sf::st_as_sf().
wt: (optional) Spatial weight matrix. Must be a matrix class. If wt is not provided, sdsfun will use a first-order queen adjacency binary matrix.
alternative: (optional) Specification of alternative hypothesis as greater (default), lower, or two.sided.
symmetrize: (optional) Whether or not to symmetrize the asymmetrical spatial weight matrix wt by: 1/2 * (wt + wt'). Default is FALSE.

Value

A list with moran_test class and result stored on the result tibble. Which contains the following information for each variable:

MoranI: observed value of the Moran coefficient
EI: expected value of Moran's I
VarI: variance of Moran's I (under normality)
ZI: standardized Moran coefficient
PI: p-value of the test statistic

Note

This is a C++ implementation of the MI.vec function in spfilteR package, and embellishes the console output.

The return result of this function is actually a list, please access the result tibble using $result.

The non-numeric columns of the attribute columns in sfj are ignored.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
moran_test(gzma)
#> ***                 global moran test                 
#> -------------------------------------------------------------------
#>  Variable     MoranI         EI         VarI      zI        pI     
#> ---------- ------------- ----------- ---------- ------- -----------
#>  PS_Score   0.504972***   -0.008547   0.003119   9.195   1.877e-20 
#> 
#>  EL_Score   0.351095***   -0.008547   0.003119   6.44    5.988e-11 
#> 
#>  OH_Score   0.593021***   -0.008547   0.003119   10.77   2.347e-27 
#> 
#>  IL_Score   0.483476***   -0.008547   0.003119   8.81    6.257e-19 
#> -------------------------------------------------------------------
#>