search for: ppareto

...) if(plot.it) { lines(x, dpareto(x, alpha = alpha, b = b), lty = lty,col="red")} result <- list(alpha = alpha, b = b) class(result) <- "pareto" result} dpareto<-function(x, alpha,b) ifelse(x<b,0,alpha*(b^alpha)/(x^(alpha+1))) ppareto<-function(x, alpha,b) ifelse(x<b,0,1-(b/x)^alpha) qpareto<-function(p, alpha,b) b*exp(-log(1-p)/alpha) rpareto<-function(n, alpha,b) qpareto(runif(n),alpha=alpha,b=b) #LN lnormtfit_function(x,b, plot.it = F, lty = 1){ if (mode(x) != "numeric") stop(...

Hi, guys, I need to simulate Pareto distribution. But I found 'rpareto' didn't exist in R. And it seems that Pareto distribution don't have mathematical relationships with other distributions. What can I do? Thanks a lot. Ni --------------------------------- [[alternative HTML version deleted]]

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:

...u) 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 is not). Since the shape paremeter is less than 0, I believe that the GPD is of the beta type. If any of you could help me answer this quandry (...

...ributions Kind regards, Christophe dmixgam <- function(x, prob, shape1, scale1, shape2, scale2) prob*dgamma(x, shape1, scale=scale1)+ (1-prob)*dpareto(x, shape2, scale=scale2) pmixgam <- function(q, prob, shape1, scale1, shape2, scale2) prob*pgamma(q, shape1, scale=scale1)+ (1-prob)*ppareto(q, shape2, scale=scale2) library(actuar) library(fitdistrplus) data("danishuni") x<- danishuni$Loss fgam<- fitdist(x,"gamma",lower=0) fpar<- fitdist(x,"pareto",start = list(shape=2,scale=2),lower=0) fmixgam<- fitdist(x,"mixgam",start =...

...mma = dgamma, "log-normal" = dlnorm, lognormal = dlnorm, weibull = dweibull, pareto = dpareto, NULL) distfun <- switch(distname,exponential = pexp,gamma = pgamma, "log-normal" = plnorm, lognormal = plnorm, weibull = pweibull, pareto = ppareto, NULL) if (is.null(densfun)) stop("unsupported distribution") if (distname %in% c("lognormal", "log-normal")) { if (!is.null(start)) stop("supplying pars for the log-Normal is not supported"...

...L) if (is.null(densfun)) stop("unsupported distribution") distfun <- switch(distname, exp = pexp, exponential = pexp, gamma = pgamma, `log-normal` = plnorm, lnorm = plnorm, lognormal = plnorm, weibull = pweibull, pareto = ppareto, loglogistic = pllogis, transbeta = ptrbeta, `transformed beta` = ptrbera, burr = pburr, paralogistic = pparalogis, genpareto = pgenpareto, generalizedpareto = pgenpareto, `generalized pareto` = pgenpareto, invburr = pinvburr, `inverse burr` = pinvbu...