similar to: Pareto 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

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 All, I am trying to fit some data to a Pareto distribution and would like to estimate the parameters with the fitting. I have come across some options so far. Unfortunately I haven't managed to get any of them to make the right fits (as is evident when I check with the goodness of fit). One such option is: library(VGAM) b1 <- read.table(file("FitPareto_Values.txt",

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

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear 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:

Hi All, could anybody help me to understand what is this error means ? mydata=read.table("C:/Documents and Settings/angieb/Desktop/CommercialGL/cl_ilf_claimdata.csv",header=TRUE,sep=",") > names(mydata) [1] "ILFTable" "liabLimit" "AnnAggLimit" "DedAmt" "Loss" "TIL" >

Dear R user, I am a newcomer and need help concerning 'draw a random number for a restricted area of a prareto distribution'. (1) For estimation of pareto distribution: >http://stats.stackexchange.com/questions/27426/how-do-i-fit-a-set-of-data-to-a-pareto-distribution-in-r< We calculate the pareto distribution (parameter estimation) as follows: pareto.MLE <- function(X) { n

Hi, I am exploring GUI's for doing Quality Management/Assurance/Improvement activities and this is another mail in series! Focus of this mail is Pareto Analysis for following data (Truncated): Date Defect code Operator Shift Machine Cost - Internal Cost - External Cost - Total 8-Jun-2011 410 Joe 1 AAA 5 50 55 8-Jun-2011 465 Joe 1 AAA 1.5 25 26.5 8-Jun-2011 412 Joe 1 AAA 1.5 10 11.5

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

Dear all: First, apologies for cross-posting multiplicities and for a query that is more analytically related than S-language related. The bottom-line wish is: Could you please provide and advice, references, etc on S software approaches for fitting a distribution with density: p*g(x) + (1-p)*f(x) where g(x) is the familiar lognormal 2-parameter density and f(x) is Pareto as defined below?

Hello all This is my first posting for some years. I am back using R again and must say I do like the language (regarding scripting, I also use matlab, perl, and bash). My question involves plotting a Pareto frontier in three dimensions. This is strictly a exercise in visualization, I make no attempt to extract the Pareto set (aka dominating subset) first. EXAMPLE PLOTS For some example

UseRs, actuar is a package for Actuarial Science. A rather preliminary version (0.1-3) of the package has been available on CRAN since February 2006. We now announce the immediate availability of version 0.9-2 sporting a large number of new features. Non actuaries behold! There can be some features of interest for you, especially those related to new probability distribution and to the

UseRs, actuar is a package for Actuarial Science. A rather preliminary version (0.1-3) of the package has been available on CRAN since February 2006. We now announce the immediate availability of version 0.9-2 sporting a large number of new features. Non actuaries behold! There can be some features of interest for you, especially those related to new probability distribution and to the

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]]

Dear, When i multiply the y of a regression by 10, I would expect that the coefficient would be multiply by 10 and the z value to stay constant. Here some reproducible code to support the case. *Ex 1* library(mvtnorm) library(VGAM) set.seed(1) x=rmvnorm(1000,sigma=matrix(c(1,0.75,0.75,1),2,2))

Hello all, I am trying to estimate the cumulative distribution function for a single stock return time series. A piecewise estimation is composed of three parts: parametric generalized Pareto (GP) for the lower tail (10% of the observation), non-parametric kernel-smoothed interior (80% of the observations), and GP for the upper tail (10%). I wonder if anyone has clue about this in R. The

Dear R-users, I am trying to fit pareto distribution to some data but i've one problem. Using optim to calculate the maximum of the likelihood function of the pareto I use as start parameters the moments method(using the distribution function in the package actuar): media=mean(x) var=mean(x^2)-media^2 scale=2*var/(var-media^2) shape=(scale-1)*media

Dear R users, the first 1.0 version of the rPref package is now on CRAN. rPref allows to select the Pareto-optimal tuples from a data set, also called Skylines in the database community. For example, optimal tuples from mtcars according to "high(mpg) * high(hp)" (where "*" is the Pareto operator) are those cars, for which no other dominating car exists. There, a car