# R/local.sp.runs.test.boots.R In spqdep: Testing for Spatial Independence of Qualitative Data in Cross Section

#### Defines functions local.sp.runs.test.boots

```local.sp.runs.test.boots <-  function(fx = fx,listw = listw, nv = nv){

####
y <- fx
q <- max(y)
n <- length(y)
# Cont is a binary variable that takes on the value of 1 if data are
# continuous and 0 if data are categorical.

m <- numeric() # matrix(0,nrow=q,ncol=1)
pprod <- numeric() # matrix(0,nrow=q,ncol=1)
for (i in 1:q){
m[i] <- sum(y==i)
pprod[i]<- m[i]*(n-m[i])
}
if (inherits(listw,"knn")){
lnnb <- matrix(dim(listw\$nn)[2],ncol = 1,
nrow = dim(listw\$nn)[1])}
if (inherits(listw, "nb")){
lnnb <- rowSums(nb2mat(listw, style='B',
zero.policy = TRUE))
}

# here we categorize the original data set y into the q categories
# compute the m_k needed for the computation of mean and variance
# pprod is needed for the computation of p
p=sum(pprod)/(n*(n-1))

##### COMPUTING THE VARIANCE #####
## case 1 ##
aux1 <- numeric()
aux31 <- numeric()
aux3 <- numeric()

t1=0;
for (k in 1:q){
for (c in 1:q){
t1=t1+1
aux1[t1]=m[k]*m[c]*(n-m[c]-1)
aux31[t1]=m[k]*m[c]*((m[k]-1)*(n-m[k]-1)+(m[c]-1)*(n-m[c]-1))
if(k==c){
aux1[t1]=0
aux31[t1]=0
}
}
}

t3=0
aux3<-numeric()
for (k in 1:q){
for (c in 1:q){
for (d in 1:q){
t3=t3+1
aux3[t3] <- m[k]*m[c]*m[d]*(n-m[d]-2)
if (c==k){aux3[t3]=0}
if (d==k){aux3[t3]=0}
if (d==c){aux3[t3]=0}
}
}
}

var1 <- 1/(n*(n-1)*(n-2)*(n-3))*(sum(aux3)+sum(aux31));
var2 <- 1/(n*(n-1)*(n-2))*sum(aux1);
var3 <- p;

varSR <-p*(1-p)*sum(lnnb)+nv[1]*var1+nv[2]*var2+nv[3]*var3-(nv[1]+nv[2]+nv[3])*p^2

############################################################################
# Here we compute the runs starting at each location and it sum is the total number of runs
############################################################################
nruns <- matrix(0,ncol = 1,nrow = n)
for (i in 1:n){
if (lnnb[i]!= 0){ # Solo calcula los test locales si el elemento tiene vecinos
if (inherits(listw, "knn")){
runs <- y[c(i,listw\$nn[i,])]}
if (inherits(listw, "nb")){
runs <- y[c(i,listw[[i]])]}
nruns[i] <- 1 + sum(abs(diff(runs))>0)
}
}

# MeanR <- 1 + lnnb*p
# StdR <- sqrt(lnnb*p*(1-p)+2*(lnnb-1)*(var2-p^2)+(lnnb-1)*(lnnb-2)*(var1-p^2))
# ZZ <- (nruns-MeanR)/StdR
# pZ <- 2*(1 - pnorm(abs(ZZ), mean = 0, sd = 1))
# SRQlocal <- cbind(nruns,MeanR,StdR,ZZ,pZ)
#
# SRQlocal <- as.data.frame(SRQlocal)
# names(SRQlocal)<-c("runs","mean","std","z-value","p-value")

# El test de rachas da NaN en caso de una sola racha. Pongo Z=99

# # OJO VER QUE PASA CON RACHAS CORTAS EN HEXAGONOS
# SRQlocal[is.na(SRQlocal[,4]),4] <- 99

# # La distribuciĆ³n del numero de rachas
# dnr <- table(SRQlocal[,1])

SR=sum(nruns)
#The mean of the statistic
meanSR=n+p*sum(lnnb)

# # The SRQ global test statistic which is N(0,1) distributed
SRQ=(SR-meanSR)/sqrt(varSR)
# p.valueSRQ <- 2*(1 - pnorm(abs(SRQ), mean = 0, sd = 1))
return <- list(nruns=nruns)
}
```

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spqdep documentation built on March 28, 2022, 5:06 p.m.