similar to: One Sample Nonparametric

When I use wilcox.test, I get vastly different p-values than the problems from Statistics textbooks. For example: The following problem comes from "Applied Statistics and Probability for Engineers", 2nd Edition, by D. C. Montgomery. Page736, problem 14.7. The problem is to compare the sample data with a population median of 8.5. The book answer is p = 0.25, wilcox.test answer is p =

Hello, Gurus: I tried to test if the sample mean of a dataset is zero. The data has 1500 numbers with a lot of zeros and some small positive numbers. The data range on [0,1] but the distribution is unknown. It is zero inflated anyway. I tried to use the Wilcoxon Signed Ranks test. But I read from this website that it does assume the population pdf is symmetric.

My interpretation of the relation between 1-way ANOVA and Wilcoxon's test (wilcox.test() in R) is the following. 1-way ANOVA is to test if two or multiple distributions are the same, assuming all the distributions are normal and have equal variances. Wilcoxon's test is to test two distributions are the same without assuming what their distributions are. In this sense, I'm wondering

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

Hi useRs, can a variance test for 2 non-normal samples be tested in R? Also, thus far I have not been able to find the Friedman two way analysis of variance. For normal r.v., the var.test is available, but are there any tests available for non-normal samples? Thanks! Bernd

I have a question concerning the Wilcoxon signed-rank test, and specifically, which R subroutine I should use for my particular dataset. There are three different commands in R (that I'm aware of) that calculate the Wilcoxon signed-rank test; wilcox.test, wilcox.exact, and wilcoxsign_test. When I run the three commands on the same dataset, I get different p-values. I'm hoping that

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

The point estimates produced by wilcox.test are perverse (not wrong, just brain damaged). The Hodges-Lehmann estimator that goes with the signed rank test is the median of the Walsh averages. The Hodges-Lehmann estimator that goes with the rank sum test is the median of the pairwise differences. wilcox.test agrees except that it uses the following very peculiar definition of "sample

Por si alguno pudiera ayudarme. Al realizar el t.test para una muestra, junto con el valor de t y el p-valor, la función proporciona la estimación de la media y su INTERVALO DE CONFIANZA. Desde el punto de vista de la estadística de rangos esto se puede hacer mediante: > iris$MEDIANA <- with(iris, 2.95) > median(iris$Sepal.Width - iris$MEDIANA, na.rm=TRUE) # median difference [1]

Well fellow R users, I throw myself on your mercy. Help me, the unworthy, satisfy my employer, the ungrateful. My feeble ramblings follow... I've searched R-Help, the R Website and done a GOOGLE without success for a one way ANOVA procedure to analyse data that are both non-normal in nature and which exhibit unequal variances and unequal sample sizes across the 4 treatment levels. My

Could somebody recommend some good nonparametric statistical inference textbooks for a beginner? And what are pros and cons of each book? Nonparametric statistical methods by Hollander seems to be more difficult for a beginner, but is great as a reference, right? Are there any books that are easier to learn than Hollander's? Also, I see some books in the wiki page. I don't find the

Hi! I'm an R newbie and this should be a trivial problem, but I can't make it work and cannot find what I'm doing wrong in the literature. I entered the the command: table<-data.frame(x, scientific=F, digits=4) table This prints a column of x with 16 useless decimal places after the decimal point. Also, it prints an unwanted index number (1-20) in the left column. How do I get rid

Hi, I became a little bit confused when working with the Wilcoxon test in R. As far as I understood, there are mainly two versions: 1) wilcox.test{stats}, which is the default and an approximation, especially, when ties are involved 2) wilcox_test{coin}, which does calculate the distribution _exactly_ even, with ties. I have the following scenario: #---BeginCode--- # big example size = 60

Hi all, The Kruskal-Wallis test is a generalization of the two-sample Mann-Whitney test to *k* samples. That being the case, the Kruskal-Wallis test with *k*=2 should give an identical p-value to the Mann-Whitney test, should it not? x1<-c(1:5) x2<-c(6,8,9,11) a<-wilcox.test(x1,x2,paired=FALSE) b<-kruskal.test(list(x1,x2),paired=FALSE) a$p.value [1] 0.01587302 b$p.value [1]

Hi, I recently ran into the problem that I needed a Siegel-Tukey test for equal variability based on ranks. Maybe there is a package that has it implemented, but I could not find it. So I programmed an R function to do it. The Siegel-Tukey test requires to recode the ranks so that they express variability rather than ascending order. This is essentially what the code further below does. After the

Hi all, I wondered whether anyone has some advice on a stats-related 'sanity check', as I ran a nonparametric multivariate test (mulrank function as decribed by R. Wilcox, 2005) on both systems, but got different results (please see below for the system-specific outputs)! The functions I used are attached as well. Any advice would be much appreciated! Thanks in advance for getting back to

Hello, I think I am right in saying that a 2 sample wilcox.test is equal to a 2 sample kruskal.test and a 2 sample t.test is equal to a 2 sample anova. This is also stated in the ?kruskal.test man page: The Wilcoxon rank sum test (wilcox.test) as the special case for two samples; lm together with anova for performing one-way location analysis under normality assumptions; with Student's t

Hi all, I have a distribution, and take a sample of it. Then I compare that sample with the mean of the population like here in "Wilcoxon signed rank test with continuity correction": > wilcox.test(Sample,mu=mean(All), alt="two.sided") Wilcoxon signed rank test with continuity correction data: AlphaNoteOnsetDists V = 63855, p-value = 0.0002093 alternative hypothesis:

I have been examining the Mann-Whitney test closely. And there are two features of the R implementation that puzzles me. The test statistic is reported as "W" and depends on the order of the arguments to the function. > x <- c(1,3,5,7,9) > y <- x-1 > x [1] 1 3 5 7 9 > y [1] 0 2 4 6 8 > wilcox.test(x, y)$statistic W 15 > wilcox.test(y,x)$statistic W 10

Does anyone know of an implementation in R of the Hodges-Lehmann nonparametric difference between two groups? I am interested in the estimate of the difference and the CI or significance of that difference. I did some quick searching and didn't see it, but I may not have been looking for the right name, etc. Thanks, Sean