similar to: Nemenyi test as a post-hoc test to Kruskal Wallis

Dear fellow R users, Keywords: Kruskal-Wallis, Post-Hoc, pair-wise comparisons, Nemenyi-Damico-Wolfe-Dunn test, coin package, oneway_test I am using the "oneway_test" function in the R package "coin" and I am obtaining results which I cannot believe are accurate. I do not wish to waste anyone's time and so if the following problem is rather trivial, I apologize, however I

Dear Researchers, Sorry for this email but I am not a statistician, and for this I have this problem to understand. Thanks in Advance for help and suggestions. Gianni I have 21 classes (00, 01, 02, 04, ....,020) with different length. I did a kruskal wall test in R with the following code kruskal.test(m.class.l, m.class.length.lf) Kruskal-Wallis rank sum test data: m.class.l and

I am trying to do a Nonparametric Tukey-type multiple comparison post-hoc test to determine which groups are significantly different. I have read the dialogue on this topic from the R-help, and am still not clear why no statistical packages include this test as an option? Is it not an appropriate test to conduct on non-normally distributed data? Is the only option to calculate it by hand

Hello, I need nemenyi-test or any other post-hoc test for kruskal-wallis, but I just can't find out how to implement this in R. My data set is nitrite concentrations in four different groups which I intend to compare. The kruskal-wallis-test showed significance, but I still don't know between which groups these significances are. Can anybody help me with this? Thanks

Hi there, I've got a small question : is there any post-hoc test for Kruskal rank sum test integrated in R ? I know that the Nemenyi test is one of the post-hoc that can be used, but there's no (to by knowledge) R function for it. What should I do ? Thanks, Etienne

Hi R users! I have the following problem: how appropriate is my aov model under the violation of anova assumptions? Example: a<-c(1,1,1,1,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,3) b<-c(101,1010,200,300,400, 202, 121, 234, 55,555,66,76,88,34,239, 30, 40, 50,50,60) z<-data.frame(a, b) fligner.test(z$b, factor(z$a)) aov(z$b~factor(z$a))->ll TukeyHSD(ll) Now from the aov i found that my model

Hello. We have some questions concerning the statistical analysis of a dataset. We aim to compare the sample means of more than 2 independent samples; the sample sizes are unbalanced. The requirements of normality distribution and variance homogeneity were not met even after transforming the data. Thus we applied a nonparametric test: the Kruskal-Wallis-test (H-Test). The null hypothesis was

Hi list, I would like to know if there is a way in R to execute a post-hoc test (factor levels comparison, like Tukey for ANOVA) of a non-parametric analysis of variance (kruskal.test() function object). I was looking for something like the Dunn Test. I’ve searched the internet but nothing seemed to define a way to it. Do You have any suggestion? Other equivalent test maybe! Thanks in

I am trying to do a Nonparametric Nemenyi test for multiple comparison in a two way layout data because I need to know the exact distribution. I don?t know if this test is in the R software. I just found a Nemenyi test for a one way design. But in Hollander book the Nemenyi test is in page 253 for to way layout data. Thank you for your help.

Dear expeRts, I would like to use a oneway_test (from package coin) to test whether two groups differ on various variables. The variables are encoded within a data frame. Unfortunately, I obtained an error, that I don't understand. Could you please help me ? Example: library(coin) y <- as.data.frame(matrix(rnorm(200), ncol=2)) group <- as.factor(unif(100)) lapply(y,

Dear R users, I would like to know if there is a way in R to execute a post-hoc test (factor levels comparison, like Tukey for ANOVA) of a non-parametric analysis of variance with kruskal.test() function. I am comparing three different groups. The preliminary analysis using the kruskal-wallis-test show significance, but I still don''t know the relationship and the significance level

Is it possible to implement the Jonckheere-Terpstra test for ordered alternatives using the coin package: Conditional Inference Procedures in a Permutation Test Framework? I found jonckheere.test{clinfun}, but it uses a normal approximation when ties are present in the data. To make this concrete, I've include a small dataset. Thanks. --Dale Hollander and Wolfe, 1999 Table 6.6, pg 205

Hi, I'm trying to run a permutation test on paired samples. First I tried the package "exactRankTests": require("exactRankTests") x <- c(1.83,0.50,1.62,2.48,1.68,1.88,1.55,3.06,1.30) y <- c(0.878,0.647,0.598,2.05,1.06,1.29,1.06,3.14,1.29) wilcox.test(x,y,paired = TRUE,alternative = "greater") perm.test(y,x,paired = TRUE,exact = TRUE,alternative =

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui

------------------------------------------------------------------------------- New versions released for the distr family of package ------------------------------------------------------------------------------- We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distr"-family (version 2.1),

------------------------------------------------------------------------------- New versions released for the distr family of package ------------------------------------------------------------------------------- We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distr"-family (version 2.1),

Hello! I need to use Kruskal-Wallis test and post-hoc test (Dunn's test) for my data. But when I searched around, I only found this function: kruskal.test. But nothing for Dunn's test. So I started to write one myself. But I do not know how to count ties in the data frame. I can use for loops but it seems long and unnecessary since the rank function actually knows the ties. So

Hi everyone, I am working on analyzing spectra which requires hundreds of kruskal wallis tests to be performed. Once significant results are found post tests are performed ect. My questions are: how do I perform all these kw tests and save just the pvalues in a table? Is there anyway to have R automatically perform post tests if a result of p<0.05 is found? Currently when I run the test

Hi I have a dataframe of genomic positions something like below: chr start end strand 1a 2a 3a 1b 2b 3b 1c 2c 3c 1d 2d 3d chr1 201199522 201199523 - 51 62 63 46 53 58 53 78 37 27 65 62 chr1 201199584 201199585 - 42 49 35 54 62 55 43 56 26 21 64 63 chr1 201199603

Dear All, I need to compare 4 groups of binary data having different sample sizes and would like to know if the non parametric Kruskal-Wallis test (kruskal.test) can be used for this purpose or not. Many Thanks, GK