| moran_bv | R Documentation |
Given two continuous numeric variables, calculate the bivariate Moran's I. See details for more.
moran_bv(x, y, listw, nsim = 499, scale = TRUE)
x |
a numeric vector of same length as |
y |
a numeric vector of same length as |
listw |
a listw object for example as created by |
nsim |
the number of simulations to run. |
scale |
default |
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. The output object is of class "boot", so that plots and confidence intervals are available using appropriate methods.
An object of class "boot", with the observed statistic in component t0.
Josiah Parry josiah.parry@gmail.com
Wartenberg, D. (1985), Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis. Geographical Analysis, 17: 263-283. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1538-4632.1985.tb00849.x")}
data(boston, package = "spData")
x <- boston.c$CRIM
y <- boston.c$NOX
listw <- nb2listw(boston.soi)
set.seed(1)
res_xy <- moran_bv(x, y, listw, nsim=499)
res_xy$t0
boot::boot.ci(res_xy, conf=c(0.99, 0.95, 0.9), type="basic")
plot(res_xy)
set.seed(1)
lee_xy <- lee.mc(x, y, listw, nsim=499, return_boot=TRUE)
lee_xy$t0
boot::boot.ci(lee_xy, conf=c(0.99, 0.95, 0.9), type="basic")
plot(lee_xy)
set.seed(1)
res_yx <- moran_bv(y, x, listw, nsim=499)
res_yx$t0
boot::boot.ci(res_yx, conf=c(0.99, 0.95, 0.9), type="basic")
plot(res_yx)
set.seed(1)
lee_yx <- lee.mc(y, x, listw, nsim=499, return_boot=TRUE)
lee_yx$t0
boot::boot.ci(lee_yx, conf=c(0.99, 0.95, 0.9), type="basic")
plot(lee_yx)
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