similar to: problem with ad.test

Hi, I'm trying to run an anderson-darling test for normality on a given variable 'Y': ad.test(Y) I think I need the 'nortest' package, but since it does not appear in any of the Ubuntu repositories for 2.10.1, I am wondering if it goes by the name of something else now? Thanks -- View this message in context:

Hello all, Would somebody be kind enough to show me how to do a KS test in R for a lognormal distribution with ESTIMATED parameters. The R function ks.test()says "the parameters specified must be prespecified and not estimated from the data" Is there a way to correct this when one uses estimated data? Regards, Kwabena. -------------------------------------------- Kwabena Adusei-Poku

Hi, I have the binned data (observed and generated from model)  that I would like to test using the anderson-darling goodness of fit test.  But I'm not sure which package in R to use. I tried ad.test(...) but it does not recognise the test by Vito Ricci in FITTING DISTRIBUTIONS WITH R   > ad.test(hist_hume_beec[,1],hist_hume_beec[,2]) Error: could not find function "ad.test"

Hi all, i have 2 cumulative (F(x)) distribution function that not defined in R. i want to make anderson darling goodness of fit test to first function (function 1) to check if it behaves as the other distributed function(function 2) how can i insert my 2 unknown by R function to ad.test()? Regards, Irit -- View this message in context:

Hi, I'm a newbie and i am unable to use lillie.test in nortest I have a message: "Couldn't find function "lillie.test" I am under windows2000 with R1.8.1 nortest is listed with .packages(TRUE) How to do to use lillie.test function? Laurent Houdusse Analyste Programmeur

I don't know from the nortest package, but it should ***always*** be the case that you test hypotheses H_0: The data have a normal distribution. vs. H_a: The data do not have a normal distribution. So if you get a p-value < 0.05 you can say that ***there is evidence*** (at the 0.05 significance level) that the data are not from a normal distribution. If the nortest package does

I believe there to be a bug in the cvm.test module of the nortest package authored by Juergen Gross. I do not know how to contact the author directly. I've been running some normality tests using the nortest package. For some of my datasets the Cramer-von Mises normality test generates an extremely high probability (e.g., 1.637e+31) and indicates normality when the other tests do

Hi everybody, somehow i dont get the shapiro wilk test for normality. i just can?t find what the H0 is . i tried : shapiro.test(rnorm(5000)) Shapiro-Wilk normality test data: rnorm(5000) W = 0.9997, p-value = 0.6205 If normality is the H0, the test says it?s probably not normal, doesn ?t it ? 5000 is the biggest n allowed by the test... are there any other test ? ( i know qqnorm

Hello all: Is there a way to detect in R if a dataset is normally distributed or skewed without graphically seeing it? The reason I want to be able to do this is because I have developed and application with Visual Basic where Word,Access and Excel "talk" to each other and I want to integrate R to this application to estimate confidence intervals on fish sizes (mm). I basically want to

Hi there, I am trying to perform different normality tests in R. I have no problem when I use Shapiro.test( ). However, when I try to use any normality tests from the Nortest package, I kept getting this error " Error in read.table(source, header = T, fill = T) : 'file' must be a character string or connection". I have no idea why because there was no problem with

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

I have written a function to emulate minitab's QQ plotting output (with SW test and AD test results on the graph): mtab.norm<-function(x) { library(nortest) library(lattice) x<-as.numeric(x) x<-as.vector(x) plot.ht<-4.6 plot.wd<-4.6 pt.ht=plot.ht/5 txt.sz<-(plot.ht/7.5) X11(width=plot.wd, height=plot.ht, bg='gray96') qqplot(x, pch=16, cex=pt.ht,

Hi, this is my first help request so please bear with me. I've been running some normality tests using the nortest package. For some of my datasets the Cramer-von Mises normality test generates an extremely high probability (e.g., 1.637e+31) and indicates normality when the other tests do not. Is there something I'm misunderstanding or potentially a bug in the code? Below are the

Hola, LLevo buscando desde hace tiempo como hacer el Goodness of fit test en R. Es decir, me explico, intento hacer una cosa parecida que se hace en Minitab, por ejemplo, yo tengo un conjunto de datos, y lo que quiero es sabes que tipo de distibución es, en minitab se hace un histograma para ver si se ajusta bien o no a la campana de Gauss, luego vemos si aproximar la distribución de la muestra

Hi, I have a small set of data on which I have tried some normality tests. When I make a histogram of the data the distribution doesn't seem to be normal at all (rather lognormal), but still no matter what test I use (Shapiro, Anderson-Darling,...) it returns a very small p value (which as far as I know means that the distribution is normal). Am I doing something wrong here? Thanks Pieter

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks

Dear Sir, I am writing to inquire about normality test given in nortest package. I have a random data set consisting of 300 samples. I am curious about which normality test in R would give me precise measurement, whether data sample is following normal distribution. As p value in each test is different in each test, if you could help me identifying a suitable test in R for this medium size of

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

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