install.packages("McSpatial")
install.packages("geosphere")

setwd("C:/Pedro")
library(McSpatial)
library(readxl)
library(geosphere)

memory.limit(size = 33000)

data1<-read_excel("Empresas_submuestra.xlsx")

lmat <-cbind(data1$X1,data1$Y1)
# df1 <- data.frame(lat = data1$X1, lon = data1$Y1)
# df2 <- data.frame(lat = data1$X1, lon = data1$Y1)
# distmatrix <- distm(df1[,2:1], df2[,2:1], fun = distHaversine)
# median_value <- median(distmatrix)
median_value # en metros

lmat1 <- lmat[data1$CIIU=="2029",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=107, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=FALSE)

jpeg("CIIU_2029.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
# lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
# lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2029<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2029, file = "v2029.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="2892",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_2892.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2892<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2892, file = "v2892.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="2222",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_2222.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2222<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2222, file = "v2222.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="1721",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_1721.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector1721<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector1721, file = "v1721.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="3691",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_3691.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector3691<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector3691, file = "v3691.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="3430",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_3430.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector3430<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector3430, file = "v3430.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="3190",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_3190.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector3190<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector3190, file = "v3190.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="1554",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_1554.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector1554<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector1554, file = "v1554.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="1512",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_1512.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector1512<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector1512, file = "v1512.csv", sep = ",", col.names = NA)
#------------------------------------------



lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="1520",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_1520.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector1520<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector1520, file = "v1520.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="2424",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_2424.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2424<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2424, file = "v2424.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="2413",]

fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_2413.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2413<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2413, file = "v2413.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="2411",]

#estimador y su gr?fico:
fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_2411.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2411<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2411, file = "v2411.csv", sep = ",", col.names = NA)
#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="2694",]

#estimador y su gr?fico:
fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_2694.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector2694<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector2694, file = "v2694.csv", sep = ",", col.names = NA)

#------------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="1723",]

#estimador y su gr?fico:
fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_1723.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector1723<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector1723, file = "v1723.csv", sep = ",", col.names = NA)
#-----------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="1542",]

#estimador y su gr?fico:
fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_1542.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector1542<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector1542, file = "v1542.csv", sep = ",", col.names = NA)
#-----------------------------------------

lmat <-cbind(data1$X1,data1$Y1)
lmat1 <- lmat[data1$CIIU=="3410",]

#estimador y su gr?fico:
fit <- ksim(lmat1[,1],lmat1[,2],lmat[,1],lmat[,2],kilometer=TRUE,dmin=0, dmax=180, dlength=512,kern="gaussian", nsim=1000,noplot=FALSE,cglobal=TRUE)

jpeg("CIIU_3410.jpeg", quality = 100, width = 1600, height = 782)

plot(fit$distance, fit$dhat, xlab="Distance (km.)",xaxt="n", ylab="K-Density",type="l", lwd=3, ylim=c(0,0.010), cex.lab="1.5", cex.axis="1.2")
#lines(fit$distance, fit$local.lo, lty=2,lwd=3, col="red")
#lines(fit$distance, fit$local.hi, lty=2,lwd=3, col="red")
lines(fit$distance, fit$global.lo,lty=3, lwd=3, col="blue")
lines(fit$distance, fit$global.hi, lty=3, lwd=3, col="blue")
axis(side=1, c(0,20,40,60,80,100,120,140,160,180), tcl=-0.2, labels=FALSE, cex.axis="1.2")
mtext(c(0,20,40,60,80,100,120,140,160,180), side=1, las=1, at=c(0,20,40,60,80,100,120,140,160,180), line=0.3,
col="black", cex=1.2, cex.lab="1.5", cex.axis="1.2")
dev.off()

vector3410<-cbind(fit$distance,fit$dhat, fit$local.lo, fit$local.hi, fit$global.lo, fit$global.hi, fit$h)
write.table(vector3410, file = "v3410.csv", sep = ",", col.names = NA)
#-----------------------------------------