# localmoran_bv: Compute the Local Bivariate Moran's I Statistic In spdep: Spatial Dependence: Weighting Schemes, Statistics

 localmoran_bv R Documentation

## Compute the Local Bivariate Moran's I Statistic

### Description

Given two continuous numeric variables, calculate the bivariate Local Moran's I.

### Usage

```localmoran_bv(x, y, listw, nsim = 199, scale = TRUE, alternative="two.sided",
iseed=1L, no_repeat_in_row=FALSE)
```

### Arguments

 `x` a numeric vector of same length as `y`. `y` a numeric vector of same length as `x`. `listw` a listw object for example as created by `nb2listw()`. `nsim` the number of simulations to run. `scale` default `TRUE`. `alternative` a character string specifying the alternative hypothesis, must be one of "greater" (default), "two.sided", or "less". `iseed` default NULL, used to set the seed for possible parallel RNGs. `no_repeat_in_row` default `FALSE`, if `TRUE`, sample conditionally in each row without replacements to avoid duplicate values, https://github.com/r-spatial/spdep/issues/124

### Details

The Bivariate Local Moran, like its global counterpart, evaluates the value of x at observation i with its spatial neighbors' value of y. The value of I_i^B is xi * Wyi. Or, in simpler words, the local bivariate Moran is the result of multiplying x by the spatial lag of y. Formally it is defined as

I_i^B= cx_iΣ_j{w_{ij}y_j}

### Value

a `data.frame` containing two columns `Ib` and `p_sim` containing the local bivariate Moran's I and simulated p-values respectively.

### Author(s)

Josiah Parry josiah.parry@gmail.com

### References

Anselin, Luc, Ibnu Syabri, and Oleg Smirnov. 2002. “Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows.” In New Tools for Spatial Data Analysis: Proceedings of the Specialist Meeting, edited by Luc Anselin and Sergio Rey. University of California, Santa Barbara: Center for Spatially Integrated Social Science (CSISS).

### Examples

```# load columbus data