# Dotplot
x=c(-2.2,-.7,-.3,.1,1.4,2.0)
plot(x,rep(0,6),ylim=c(-0.1,2),ylab="",yaxs=NULL,xlim=c(-3,3),main="Dotplot")

#Density estimate at a single point
#Weighting function for density estimate at x=-1
plot(x,rep(0,6),ylim=c(-0.1,2),ylab="",yaxs=NULL,xlim=c(-3,3),main=expression(paste("Normal kernel with ", sigma,"=.5 at X=-1")))
curve(dnorm(x,mean=-1,sd=.5),from=-3,to=3,add=TRUE)
lines(c(-1,-1),c(0,2),lwd=2,col="red")

#Weight assignments
plot(x,rep(0,6),ylim=c(-0.1,2),ylab="",yaxs=NULL,xlim=c(-3,3),main="Density Estimate at X=-1",sub="(for probability histogram, scale by 1/n)")
curve(dnorm(x,mean=-1,sd=.5),from=-3,to=3,add=TRUE)
for(i in 1:6){
lines(c(x[i],x[i]),c(0,dnorm(x[i],mean=-1,sd=.5)),col=i,lwd=2)
}
text(1.6,.2,"Density too small to see lines \n for these two points",cex=.8)
dxm1=c(0,cumsum(dnorm(x,mean=-1,sd=.5)))
for(i in 1:6){
lines(c(-1,-1),c(dxm1[i],dxm1[i+1]),col=i,lwd=2)
}

#Histogram with density estimates computed over a grid
hist(x,freq=FALSE,main="Histogram with Density Estimates Computed Over a Grid")
nj=length(seq(-3,3,by=.2))
for(j in seq(-3,3,by=.2)){
dxm1=sum(dnorm(x,mean=j,sd=.5))/6
lines(c(j,j),c(0,dxm1),lwd=2,col="red")
}

#Finer grid
hist(x,freq=FALSE,main="Histogram with Two Density Estimates Over a Fine Grid")

nj=length(seq(-3,2,by=.01))
dx=rep(0,nj)
ict=1
for(j in seq(-3,2,by=.01)){
dx[ict]=sum(dnorm(x,mean=j,sd=.5))/6
ict=ict+1
}
lines(seq(-3,2,by=.01),dx,col="red",lwd=2)

nj=length(seq(-3,2,by=.01))
dx=rep(0,nj)
ict=1
for(j in seq(-3,2,by=.01)){
dx[ict]=sum(dnorm(x,mean=j,sd=1))/6
ict=ict+1
}
lines(seq(-3,2,by=.01),dx,col="blue",lwd=2)
points(x,rep(0,6))

legend(-2.5,.25,c(expression(paste(sigma,"=.5")),expression(paste(sigma,"=1"))),lwd=c(2,2),col=c("red","blue"))