Message-ID: <CANZ=wL3bJdgDv_HOVHRwhzx2AQNEG5VR9Qr=_C2KWY5GrFt1mg@mail.gmail.com>
Date: 2019-03-06T21:31:35Z
From: Jay Wang
Subject: inference of local Gi* using permutations
Hello,
I am currently using the localG () in spdep package, I was wondering if we
can have a conditional permutation-based inference to get the P value for
every Gi*. I saw that a Mote Carlo simulation is used in Moran.MC(), and I
borrowed the following codes from this function and tried to see if I can
do a permutation for localG():
pvals<-matrix(0, nrow = V, ncol = 1)
for (i in 1:V){
rankresi<-rank(res[i, ])
xranki <- rankresi[length(res[i, ])]
diffi <- nsim - xranki
diffi <- ifelse(diffi > 0, diffi, 0)
pvali <- punif((diffi + 1)/(nsim + 1))
pvals[i,]<-pvali
}
After running these codes with several different datasets, I found that all
the negative Gi*s have very high P values say 0.999 with 999 permutations,
meaning that there are no significant cold spots. Where is the problem? How
can we do conditional permutation-based inference for localG() with R
spdep? I understand the critics of permutation-based inference for local
indicators, but I just want to explore this. Thank you!
Best
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