Dear All,
Here are the codes of a histogram & a kernel density estimates that I used.
For the hist estimate
>par(mex=1.3)
>dens<-density(q)
>options(scipen=4)
> ylim<-range(dens$y)
> h<-hist(q,breaks="scott",freq=FALSE,probability=TRUE,
+ right=FALSE,xlim=c(9000,16000),ylim=ylim,main="Histogram of q(scott)")
> lines(dens)
>box()
For the kernel estimate>options(scipen=4)
> d <- density(q, bw = "nrd0",kernel="gaussian")
> d
> plot(d)
In fact the variable q is a vector of 1000 simulated positive values; that is I
generated 1000 samples from the pareto distribution, from each sample I
calculated the value of q ( a certain fn in the sample observations), and thus
I was left with 1000 values of q and I don't know the distribution of q.
Hence, I used the above codes for histogram and kernel density estimation to
estimate the density of q.
But what I'm really intersed in is to estimate the probability that q is
greater than a certain value , for ex.,P(q>11000), using the density estimates
I obtained.
Could u help me with a fn or some document to do this?
Thank u so much
Maram
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