similar to: ECDF, distribution of Pareto, distribution of Normal

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:

Hi all, I would like to plot part of the emperical CDF. Suppose the variable is x, I just need the part when x>1,therefore, I am using the following codes. tail <- x>1 plot(ecdf(x[tail]), do.points=FALSE, verticals=TRUE) The "x" value starts from 1, but the yaxs still begins from 0, not the corresponding value when "x" is 1. How can I make it match? Could anyone

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]]

Dear all, I am trying to simulate from truncated Pareto distribution. I know there is a package called PtProcess for Pareto distribution...but it is not for truncated one. Can anyone please help me with this? Thanks in advance. Cassie [[alternative HTML version deleted]]

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}

Dear R helpers, I need to use KS and AD test for Generalized Pareto and Generalized extreme value. E.g. if I need to use KS for Weibull, I have teh syntax ks.test(x.wei,"pweibull", shape=2,scale=1) Similarly, for AD I use ad.test(x, distr.fun, ...) My problem is fir given data, I have estimated the parameters of GPD and GEV using lmom. But I am not able to find out the distribution

I would like to fit a Pareto Distribution and I am using the following codes. I thought the fitted (fit1) should be the fitted value for the data, is it correct? As the result of the "fitted" turns out to be a single value for all. fit=vglm(ycf1 ~ 1, pareto1(location=alpha), trace=TRUE, crit="c") fitted(fit) The result is fitted(fit) [,1] [1,] 0.07752694

Hi r-users,   I would like to compare the cdf between historical and predicted. My x.obs and x.pre are the frequency data in classes of 0-300. I tried: plot(ecdf(x.obs),ecdf(x.pre),type="l",col="red")   and it gives me: Error in plot.stepfun(x, ..., ylab = ylab, verticals = verticals, pch = pch) :   argument 4 matches multiple formal arguments   Thank you so much for any

In https://en.wikipedia.org/wiki/Pareto_distribution, it is clear what the parameters are for the pareto distribution: *xmin *the scale parameter and *a* the shape parameter. I am using rmutil to generate random deviates from a pareto distribution. It says in the documentation that the probabilty density of the pareto distribution The Pareto distribution has density f(y) = s (1 + y/(m

Up to now, I have recognized problems with "gpd(..)", the function from the package "evir" I think that all these functions that estimate the parameters xi, beta for the GPD by given threshold mu use the function "optim(..)" ( gpd, fitgpd, ...) "Error" example: data1 <- rgpd(1000, xi= -1.5, mu=1000, beta=100) so the created poinnts take place in about

Dear All, I am trying to generate some Pareto random variates using the inverse method. This is really straightfoward and my R function looks as below : pareto <- function(c, a, cnt=1000) { u <- runif(cnt) x <- (c / ((u ^ (1 / a)))) mean.theo <- ((c * a) / (a - 1)) mean.gen <- mean(x) cat('Pareto mean : theoritical', mean.theo, 'generated', mean.gen,

I would like to fit a Pareto Distribution and I am using the following codes fit=vglm(ycf1 ~ 1, pareto1(location=alpha), trace=TRUE, crit="c") fitted(fit) But the fitted values turn out to be the same for each observation. I guess the problem is with "ycf1 ~ 1", I would be grateful if anyone can give me some advice on how to define the formula. Many thanks -- View this

Dear R gurus, I have a function "goftests" that receives the following arguments: * a vector "x" of data values; * a distribution name "dist"; * the dots list ("...") containing a list a parameters to pass to CDF function; and calls several goodness-of-fit tests on the given data values against the given distribution. That is: ##### BEGIN CODE SNIP #####

Hello Well I am relatively new so some of these issues may not fall under the subject that I have used. 1. How do I do a Pareto. Following is the approach I took. My data looks like this df2_9 Reaason.for.failure Frequency 1 Phy Conn 1 2 Power failure 3 3 Server software 29 4 Server hardware 2 5 Server out of mem 32

Hi all I have a large dataframe with (among others) a categorical variable of 52 levels and would like to create a barplot with the bars ordered in decreasing frequency of the levels. I belive it is referred to as a pareto plot. Consider a subset where I keep only the categorical variable in question. # Example: v1 = c("aa", "cc", "bb", "bb",

Dear UseRs, maybe is a silly question: how can I get Empirical CDF values from an object created with ecdf()?? Using print I obtain: Empirical CDF Call: ecdf(t) x[1:57] = 4.1, 4.4, 4.5, ..., 491.3, 671.27 Thanks in advance. Regards, Vito Diventare costruttori di soluzioni Became solutions' constructors "The business of the statistician is to catalyze the scientific

Hi- Is there a quick way to plot a Pareto diagram? I couldn't find one. I'm being forced to do some pretty weird stuff, with awk and all, to extract data in order to plot the frequency of qualitative data from a larger set. Perhaps it's just my GNUrance. I mean, one day, if I have time, I might even write a generic Perl script, but right now, it doesn't look too good on the

Dear R users, Could you please help me out. I am in trouble as I am unable to model graphs to explain the GARCH (1 , 1) model, the Hill estimator (of alpha), and the Pareto estimator. I just got introduce to R. I am working on a paper which must be worked from R. You look at the difficulty I had from the text below. [1] "DAX" "DAX_CAC" "DAX_CAC40"

Dear R helpers, I have a bimodal dataset dealing with loss amounts. I have divided this dataset into two with the bounds for the first dataset i.e. dataset-A being 5,000$ to 100,000$ and the dataset-B deals with the losses exceeding 100,000$ i.e. dataset-B is left truncated. I need to fit truncated normal disribution to dataset - I having lower bound of 5000 and upper bound of 100,000. While I

http://r.789695.n4.nabble.com/file/n3567636/ecdfs.jpg ecdfs.jpg http://r.789695.n4.nabble.com/file/n3567636/ecdf_curve.gif ecdf_curve.gif Hello, I have generated a plot of two empirical CDFs (attachment 1). As a result, they are stepwise when plotted. The following code was used: > plot(ecdf(mut), do.points=FALSE, verticals=TRUE, xlim=range(mut, non), > col="red") >