similar to: normality testing with nortest

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 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, 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

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 all, I'm still a bit new to R and I'm trying to figure something out. I have the solution but the solution is what a C programmer would do :-) and I was wondering if someone could tell me the R way of doing it... What I have is a vector x of length |x|. And a matrix y of size m rows and n columns where n = |x|. I want to plot m * n points on a graph so that the x-coordinates

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

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

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:

Full_Name: David Sterling Version: 2.4.0 OS: OS X Submission from: (NULL) (64.81.102.199) lillie.test() dies without grace producing the error message: 'Error in if (pvalue > 0.1) { : missing value where TRUE/FALSE needed' when passed a vector of identical values. Examples: > lillie.test(c(0,0,0,0,0,0,0,0,0,0)) Error in if (pvalue > 0.1) { : missing value where TRUE/FALSE

Hi all, I am trying to figure out the formula used by R's Spearman rho (using cor(method="spearman")) because I can't seem to get the same value as by calculating "by hand". Perhaps I'm using "cor" wrong, but I don't know where. Basically, I am running these commands: > y=read.table(file="tmp",header=TRUE,sep="\t") >

Respected Sir I am a very new user of R. I want to ask a question about "the nortest package". In this package how we can write the code of ad.test, cvm.test, ks.test for other distributions like GEV, GPA etc. I request you to please guide to me. Kind Regards AMNA -- AMINA SHAHZADI Department of Statistics GC University Lahore, Pakistan. Email: amnakhan493@gmail.com

I have compiled a package in R which performs the Doornik-Hansen (1994) version of the omnibus normality test (a finite sample version of the Jarque-Bera test), including a variation which allows for weak dependence rather than independence of the variable(s) in question. I have tried to contact the compiler of the "nortest" package (which deals with the same subject) but have not

Hello, as far as I see, R reports type I sums of squares. I'd like to get R to print out type III sums of squares. e.g. I have the following model: vardep~factor1*factor2 to get the type III sum of squares for factor1 I've tried anova(lm(vardep~factor2+factor1:factor2),lm(vardep~factor1*factor2)) but that didn't yield the desired result. Could anyone give me a hint how to proceed?

Hello, Do you know a R implemented normality test like the shapiro test but more suitable for large data set ? Thanks, _________________________________________________________________ Découvrez toutes les possibilités de communication avec vos proches [[alternative HTML version deleted]]

Hi all, I have a dataset of 2000 numbers ( it's noise measured with a scoop ) Now i want to know of my data is normal distributed (Gaussian distribution). I did already: - 68-95-99.7 test - Q-Q-plot and now i used "nortest library" and the Lilli.test() However i don't understad the output? lillie.test(z) Lilliefors (Kolmogorov-Smirnov) normality test data: z D =

I currently have a program that automates 2-way ANOVA on a series of endpoints, but before the ANOVA is carried out I want the code to test the assumptions of normality and equal variance and report along with each anova result in the output file.  How can I do this? I have pasted below the code that I currently use.   library(car) numFiles = x #

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, I am trying to convert a simple CUDA program to LLVM IR using clang 3.0. The program is as follows, #include<stdio.h> #nclude<clang/test/SemaCUDA/cuda.h> __global__ void kernfunc(int *a) { *a=threadIdx.x+blockIdx.x*blockDim.x; } int main() { int *h_a,*d_a,n; n=sizeof(int); h_a=(int*)malloc(n); *h_a=5; cudaMalloc((void*)&d_a,n); cudaMemcpy(d_a,h_a,n,cudaMemcpyHostToDevice);

Hi, from what you're writing: "The logaritmic transformation "shapiro.test(log10(y))" says: W=0.9773, p-value= 2.512e-05." it seems the log-values are not distributed normally and so original data are not distributed like a log-normal: the p-value is extremally small! Other tests for normality are available in package: nortest compare the log-transformation of your ecdf

Dear all, I coded a function called u.glm u.glm <- function (x,ahi,age,bmiz,gender) { library(nortest) lil.rslt <- lillie.test(x) if (lil.rslt$p.value >0.05) { cat("Logtrans=0, lillie=",lil.rslt$p.value,"\n") xmodel<-glm(x~ahi+age+bmiz+as.factor(gender)) summary(xmodel) confint(xmodel) } else { cat("Logtrans=1,