A Spatial Autocorrelation Test R Code

> #=========================================================#

> #FIX UJI AUTOKORELASI SPASIAL#

> #=========================================================#

> library(sp)

> library(ape)

> library(geosphere)

> library(pracma)

> library(mvtnorm)

> library(Runuran)

> library(coda)

>

>

> #PANGGILAN

>

> getwd()

[1] "C:/Users/ALLAH/Documents"

> ozone <- read.table("tij 110pb baru mei 2.csv",header=TRUE, sep=";")

> rata <- read.table("rata2 baru mei.csv",header=TRUE, sep=";")

> p <- read.table("length of i each j.csv",header=TRUE, sep=";")

>

> n=length(ozone[-(1:5),])*length(ozone[,1])

> n1=length(ozone[,1])

> n2=length(ozone[-(1:5),])

> n3=365

> n4=sum(p)

>

> datas <- ozone[,-(1:5)]

> data<-list()

> for (f in 1:n1) data[f]=list(datas[f,1:p[f,]])

>

> tj <- NULL

> for (f in 1:n1) tj[f] <- c(datas[f,p[f,]])

> rata2 <- rata$RATA.RATA

> mean.data.wilayah = rata2

> koordinat <- ozone[,2:3]

>

> ozone.dists <- as.matrix(dist(cbind(ozone$Long, ozone$Lat)))

> ozone.dists.inv <- 1/ozone.dists

> diag(ozone.dists.inv) <- 0

> ozone.dists.inv[1:5, 1:5]

         1        2        3        4        5

1 0.000000 3.130749 2.868183 5.325525 2.632469

2 3.130749 0.000000 5.847130 3.557764 2.726813

3 2.868183 5.847130 0.000000 4.693390 4.740872

4 5.325525 3.557764 4.693390 0.000000 5.201668

5 2.632469 2.726813 4.740872 5.201668 0.000000

> ozone.dists.bin <- (ozone.dists > 0 & ozone.dists <= .2)

> Moran.I(rata2, ozone.dists.bin)

$observed

[1] 0.1730037

 

$expected

[1] -0.04761905

 

$sd

[1] 0.06633233

 

$p.value

[1] 0.0008809498