search for: pgpd

...ical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y <- seq(1.6, 3, 0.1) lines(y,pnorm(y, mean(z),sqrt(var(z))), col="blue") The emperical CDF and normal CDF look rather resonable, but the pareto CDF looks quite odd. I am not sure whether I plot the pareto CDF correctly e.g. in the right...

...tremes pkg). I'm in doubt on what of the following command is the most appropriate: Let: x<-sample t<-threshold xt<-x[x>t] xihat<-gpdFit(x, threshold=t, type = "pwm")$par.ests[1] betahat<-gpdFit(x, threshold=t, type = "pwm")$par.ests[2] (1) ks.test(xt,"pgpd",list(xi=xihat,beta=betahat),H=t,estfun = "as.list(gpdFit(x, 0)$par.ests)", tol = 1e-02) (2) ks.test(xt,"pgpd",list(xi=xihat,beta=betahat),H=t,estfun = "as.list(gpdFit(x, t)$par.ests)", tol = 1e-02) (3) ks.test(xt,"pgpd",list(xi=xihat,beta=betahat,mu=t),...

Hi, I would like to use the following codes to plot the CDF for pareto distribution. Before doing this, I have plot the emperical one. x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4477557,), col="red") Could anyone give me some advice whether the above codes are correct? Many thanks. -- View this message in context: http://www.nabble.com/CDF-for-pareto-distribution-tf4061253.html#a11538272 Sent from the R help mailing list archive at Nabble.com.

Hi: I am going through some of the xtable examples and I can't make the one below work. I need to create a longtable on the fly keeping the column headers for all the pages and I thought this example could give some ideas on how to do it. I am using Sweave and xtable to create my tables and graphics. I wonder if someone could tell me what's wrong. Thanks ## Not run: \begin{small}

...a): 62.63489 1.999964e-06 Shape (xi): -11.59905 3.948669e-04 Negative log-likelihood: -12.2703516139708 The results did not give me a location (mu) or a beta value. I'm wondering which function I can use to find the probability (like when I used the pgev). I've already tried to use the pgpd function from the evd library, but it kept giving me an error message (I don't remember exactly what), and I've tried to use the ppareto and pgenpareto function from the actuar package, but they did not work either (I think because the shape parameter has to be positive, which mine...