similar to: Testing multidimensional random numbers?

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

Hello, While doing test of normality under R and SAS, in order to prove the efficiency of R to my company, I notice that Anderson Darling, Cramer Van Mises and Shapiro-Wilk tests results are quite the same under the two environnements, but the Kolmogorov-smirnov p-value really is different. Here is what I do: > ks.test(w,pnorm,mean(w),sd(w)) One-sample Kolmogorov-Smirnov test data: w D

Hello R-Users, I'm new to R so I apologize in advance for any big mistake I might be doing. I'm trying to fit a set of samples with some probabilistic curve, and I have an important question to ask; in particular I have some data, from which I calculate manually the CDF, and then I import them into R and try to fit: I have the x values (my original samples) and the y values

Dear useRs, There is a new version 0.1-2 of the package surv2sample available on CRAN. Users of the previous versions should update because a bug in the function cif2.ks has been fixed. General information about the package: surv2sample provides various two-sample tests for right-censored survival data. Three main areas and corresponding methods are: * comparison of two survival

Dear useRs, There is a new version 0.1-2 of the package surv2sample available on CRAN. Users of the previous versions should update because a bug in the function cif2.ks has been fixed. General information about the package: surv2sample provides various two-sample tests for right-censored survival data. Three main areas and corresponding methods are: * comparison of two survival

Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?) However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

Hi all, I'm working with data to which I'm fitting three-parameter weibull distributions (shape, scale & shift). The data are of low sample sizes (between 10 and 80 observations), so I'm reluctant to check my fits using chi-square (also, I'd like to avoid bin choice issues). I'd use the Kolmogorov-Smirnov test, but of course this is invalid when the distribution

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

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

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

Hi, I'm not a statistician so sorry for possible trivial questions ... I want to perform a GoF test on sample data against several distribution (like Extreme Value, Phase Type, Pareto, ...). Since I suspect a long-tailed behaviour on data I want to use Anderson-Darling (AD) GoF test because it's well known it's more sensible to tail data. Looking at R packages the only AD test is

Hi, I'm not a statistician so sorry for possible trivial questions ... I want to perform a GoF test on sample data against several distribution (like Extreme Value, Phase Type, Pareto, ...). Since I suspect a long-tailed behaviour on data I want to use Anderson-Darling (AD) GoF test because it's well known it's more sensible to tail data. Looking at R packages the only AD test is

Dear R Users, Suppose comparing two non-normal distributions is our interest. Like distribution of financial time series, they are negative skewed with fat tail. Which test can better help and in which pachage? ( For example in goodness-of-fit) Kolmogorov-Smirnov test has its own incompatibility. Are there for example Anderson-Darling or Kuiper statistics in R? I'm grateful to any

Hello, I have a vector of samples x of length N. Associated with each sample x_i is a certain weight w_i. All the weights are in another vector w of the same length N. I have another vector of samples y of length n (small n). All these samples have equal weights 1/n. The ECDF of these samples is defined as for example at http://en.wikipedia.org/wiki/Empirical_distribution_function and I can

Hi - A little while ago I posted a question about the implementation of a two-dimensional analog of the Kolmogorov-Smirnov test in R[1][2]. As there isn't one, as far as I know, people might be interested in a very fast C++ implementation called MUAC which is available as a function and as a standalone program from http://www.acooke.org/jara/muac/index.html. Apparently the code is

Hello R-world, I am facing a peculiar problem and hope someone out there can comment on it. In goodness-of-fit tests for evaluation of distributions, there are three well-known methods: 1. Chi-square 2. Anderson-Darling 3. Kolmogorov-Sminrov I am trying to use the second test. Many researchers have reported results using this test. I wrote programs in C and now in R to do this. I run into

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 #####

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'

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