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

## 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`

.

## 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.

## See also

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
```