similar to: packages for power law distribution

Some background: I have some data on structural dependencies in a base of code artifacts. The dependency structure is reflected in terms of relative node degrees, with each node representing some code unit (just as an example). This gives me real data of the following form (sorry for the longish posting): dat1 <- c(0.00245098039215686, 0, 0, 0, 0, 0, 0, 0, 0.0563725490196078, 0, 0, 0,

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,

Hello all, I would like to plot the emperical 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

I am having difficulty with the calculation of the power law exponent for set of nodes within a graph. Specifically, I am interested in the distribution of in-degree and out-degree among communities of web pages where the web pages are the nodes of the graph and the hyperlinks the edges. According to the literature, the distribution of incoming and outgoing links obeys a power law distribution

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

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]]

First, thanks to everyone who helped me get to grips with R in (x)emacs (I get confused easily). Special thanks to Stephen Eglen for continued support. My question is about non-linear binning, or density functions over distributions governed by a power law ... y ~ mu*x**lambda # In one of its forms # (can't find Pareto in the online help) Looking at the following

I apologize if this is somewhere in the archives, but I can't seem to find a solution to this question. I've been trying to plot a bwplot: print( bwplot( n.pareto ~ as.factor(gen) | mut.rate * n.pop, data=p6, horizontal=FALSE, box.ratio=0.75, cex=0.6, xlim=c(-1,51), ylim=c(-1,500),

Dear R-help readers, I am writing to you in order to ask you a few questions about distribution fitting in R. I am trying to find out whether the set of event interarrival times that I am currently analyzing is distributed with a Gamma or General Pareto distribution. The event arrival granularity is in minutes and interarrival times are in seconds, so the values I have are 0, 60, 120, 180, and

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:

Hello! I must estimate the parameters of a exponential power density. There is the normalp package, but this works only for a shape parameter bigger than 1. But what should i do if the shape parameter is less than 1? (Sorry for my english) Thank you very much for help! -- View this message in context: http://www.nabble.com/Parameters-of-exponential-power-density-tp20774495p20774495.html Sent from

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

Dear all, How can I fit a truncated power law to a vector? I can't find a function to do that. If the function provides an AIC, even better. John

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

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

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 All I am trying to replicate a numerical application (not computed on R) from an article. Using, ks.test() I computed the exact D value shown in the article but the p-values I obtain are quite different from the one shown in the article. The tests are performed on a sample of 37 values (please see "[0] DATA" below) for truncated Exponential, Pareto and truncated LogNormal

Dear all, being a relative beginner in R, I apologize for posting the second question within two days. So I want a stacked barchart, which should look like the one produced by this code: Tuvalu <- c(9,3,4,0,3,0,0) Singapor <- c(38,0,0,0,12,19,0) Samoa <- c(26,16,2,0,5,2,0) PNG <- c(56,4,0,5,2,0,56) Micronesia <- c(6,0,0,0,0,0,0) graph4 <-

Dear Sirs, The below listed code fits a gamma and a pareto distribution to a data set danishuni. However the distributions are not appropriate to fit both tails of the data set hence a mixed distribution is required which has ben defined as "mixgampar" as shown below. library(fitdistrplus) x<- danishuni$Loss fgam<- fitdist(x,"gamma",lower=0) fpar<-