similar to: goodness of fitting for a mixed distribution

Hello What is the appropriate method to test if a given distribution is a weibull thank you meriema email meriema.belaidouni at int-evry.fr

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

Hi All, My group is looking for any suggestions on what to purchase to achieve the most powerful number crunching system that $50k can buy. The main application that will be used is R so input on what hardware benefits R most will be appreciated. The requirements are that it be a single standalone server (i.e. not a cluster solution), and it that must be able to run unix/linux. If

I simulate a uniform data and then transformed into Burr(1,3,1) data, which is of pdf: f(x)=[3*(x^2)] / [(1+x^3)^2], x>0 How can I perform a goodness-of-fit test (k-s, anderson-darling,chisq,cramer-von mises,...) on it (should highly accept) to get test-statistics & p-values? Thanks! Sincerely, Shelton Jin -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hello! I have such trouble: I like R, but often I don't have administrator permissions to install R on some (not mine) computers. Could you give me shell on any server with R installed in order to apply R without installing it (simply with PuTTY). Great thanks! -- Best regards, Altshuler Eugeny

Hi, The functions prepanel.default.bwplot() and lpretty() are not running in Deepayan's barley example concerning vertical bars with the lattice function barchart(). Why, is there a restricted use for the package lattice-Internal? Urs Simmen mailto:usimmen at dtc.ch

Estimados todos: Me es grato escribir a esta lista de ayuda para R, ya que comparto 100% la filosofía de Software libre en especial software en Estadística ya que es la carrera que estoy siguiendo. El motivo de este mensaje es por un par de dudas que no pude resolver: 1. He utilizado las funciones para realizar pruebas de normalidad (kolmogorov-smirnov, cuando n>50) y (Shapiro, cuando

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

I'm writing an publication in two column format and need to shrink some plots. After increasing the axis labels it does not look nice at all. The y-axis label and tick labels almost touch each other and the x-axis tick labels expand into the plot instead of away from it. Is there a better way than "cex" to control the: 1) font size of axis and tick labels 2) font thickness 3)

Dear List: I have been using the MASS package until 5 minutes ago. I just updated some packages from CRAN, something happened and R crashed. I then started R again and tried to source in some code that calls MASS, but received an error that there is not a package called MASS. I then went to install packages from CRAN and MASS was not visible as an option and I then went to the CRAN website and

Forum, I'm a grad student in Civil Eng, took some Stats classes that required students learn R, and I have since taken to R and use it for as much as I can. Back in my lab/office, many of my fellow grad students still use proprietary software at the behest of advisers who are familiar with the recommended software (Statistica, @Risk (Excel Add-on), etc). I have spent a lot of time learning

Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In

Hi, I was wondering whether there is an implementation of the Kolmogorov-Smirnov goodness of fit test for discrete, ordinal scale data in R - I've only managed to find the test for continuous data. Thanks! Gila

Hi, is there any method for goodness of fit testing of an (as general as possible) univariate distribution with parameters estimated, for normal, exponential, gamma distributions, say (e.g. the corrected p-values for the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation method)? It seems that neither ks.test nor chisq.test handle estimated parameters. I am aware of function

Hi everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

Hi, I have a problem with Kolmogorov-Smirnov test fit. I try fit distribution to my data. Actualy I create two test: - # First Kolmogorov-Smirnov Tests fit - # Second Kolmogorov-Smirnov Tests fit see below. This two test return difrent result and i don't know which is properly. Which result is properly? The first test return lower D = 0.0234 and lower p-value = 0.00304. The lower 'D'

Dear all R users, I have a query on "Edit" function. Suppose I have a data frame named "data". I can use EDIT function to see the materials contained in data, by using the command: > edit(data) But when I close the window then again the materials contained in data is displayed in the command window. But I do not want to see these materials again. Can

Hi, I'm using the power.law.fit function from the igraph package to fit a power law distribution to some data. This function returns the power law exponent as it's only result. I would like to have some sort of goodness-of-fit and/or error estimate of the exponent returned. This paper: http://www.edpsciences.org/articles/epjb/pdf/2004/18/b04111.pdf suggests using the

Hello list: I generate by simulation (using different procedures) two sample vectors of size N, each corresponding to a discrete variable and I want to text if these samples can be considered as having the same probability distribution (which is unknown). What is the best test for that? I've read that Kolmogorov-Smirnov and Anderson-Darling tests are restricted to continuous data

Is the kolmogorov-smirnov test valid on both continuous and discrete data? I don't think so, and the example below helped me understand why. A suggestion on testing the discrete data would be appreciated. Thanks, a <- rnorm(1000, 10, 1);a # normal distribution a b <- rnorm(1000, 12, 1.5);b # normal distribution b c <- rnorm(1000, 8, 1);c # normal distribution c d <- rnorm(1000,