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 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 attached codes for histogram and kernel density estimation toestimate 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 Dear All, Attached are the codes of a histogram & a kernel density estimate and the output they produced. I'll copy the codes here in case there's something wrong with the attachement
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.