I tried to run Moran's I test for the spatial autocorrelation test by using the function Moran.Ifrom the package ape and moran.test from the package spdep I got different results by applying the two methods on the same data. So at this point why we're getting such a difference and what is the most efficient method? See the following code:
library(ape) #For Moran.I
library(spdep) #For moran.test
Var <- rnorm(200,1, 1)
xy<- as.data.frame(cbind(rnorm(200,0, 1), (rnorm(200,0, 1))))
colnames(xy) <-c('X','Y')
dists <- as.matrix(dist(cbind(xy$X, xy$Y)))
dists.inv <- 1/dists
diag(dists.inv) <- 0
# TEST WITH "Moran.I"
Moran.I(Var, dists.inv, alternative = "greater")
# TEST WITH "moran.test"
lw <- mat2listw(dists.inv)
moran.test(Var, lw)
The two methods return the same result if you supply the argument style = "W" to mat2listw.
As seen below: mi$observed has the same value as mt2$estimate[1].
library(broom) # to tidy output of moran.test
mi <- Moran.I(Var, dists.inv, alternative = "greater")
mt1 <- moran.test(Var, mat2listw(dists.inv))
mt2 <- moran.test(Var, mat2listw(dists.inv, style = "W"))
str(mi)
List of 4
$ observed: num -0.0184
$ expected: num -0.00503
$ sd : num 0.0106
$ p.value : num 0.896
tidy(mt1)
# A tibble: 1 x 7
estimate1 estimate2 estimate3 statistic p.value method alternative
<dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 -0.0167 -0.00503 0.000199 -0.829 0.796 Moran I test under randomisation greater
tidy(mt2)
# A tibble: 1 x 7
estimate1 estimate2 estimate3 statistic p.value method alternative
<dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 -0.0184 -0.00503 0.000112 -1.26 0.896 Moran I test under randomisation greater