Given two continuous numeric variables, calculate the bivariate Moran's I. See details for more.

## Usage

global_moran_bv(x, y, nb, wt, nsim = 99, scale = TRUE)

## Arguments

x

a numeric vector of same length as y.

y

a numeric vector of same length as x.

nb

a neighbor list object for example as created by st_contiguity().

wt

a weights list as created by st_weights().

nsim

the number of simulations to run.

scale

default TRUE.

## Value

an object of class boot

## Details

The Global Bivariate Moran is defined as

$$I_B = \frac{\Sigma_i(\Sigma_j{w_{ij}y_j\times x_i})}{\Sigma_i{x_i^2}}$$

It is important to note that this is a measure of autocorrelation of X with the spatial lag of Y. As such, the resultant measure may overestimate the amount of spatial autocorrelation which may be a product of the inherent correlation of X and Y.

Other global_moran: global_moran(), global_moran_perm(), global_moran_test(), local_moran_bv()

## Examples

x <- guerry_nb$crime_pers y <- guerry_nb$wealth
nb <- guerry_nb$nb wt <- guerry_nb$wt
global_moran_bv(x, y, nb, wt)
#>
#> DATA PERMUTATION
#>
#>
#> Call:
#> boot(data = xx, statistic = bvm_boot, R = nsim, sim = "permutation",
#>     listw = listw, parallel = parallel, ncpus = ncpus, cl = cl)
#>
#>
#> Bootstrap Statistics :
#>       original    bias    std. error
#> t1* -0.1006674 0.1024929  0.05090268