similar to: kruskalmc, significant differences while median values are the same

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

Hi all, After running kruskal.test I have got results (p<0,005) pointing to reject the hypothesis that the samples were draw from the same population. Howerver when I run the kruskalmc there are no significant differences in any of the multiple comparisons. Is that possible? Some clarification? Thanks, Humber <https://sites.google.com/site/humberandrade> [[alternative HTML version

I am interested in using "multcompLetters" after running "kruskalmc" but I'm a newbie and I'm not having luck figuring it out. I can run "kruskalmc" just fine, but after studying the documentation for "multcompletters" for a long time, I cannot figure out how to make it work. Any ideas? R input is below > >> #here is my data set >>

Dear all, I run a kruskal wallis test and found significant results. Then, I conducted all pairwise comparisons and found no significant results. Could anyone please give me a hint as to why this happens or redirect me towards a specific web page where I can find more info? (I used alpha=5% and made no bonferroni or other correction for the pairwise comparisons) Thank you   Dr. Iasonas

Hi, I have searched and found a response to a question similar to mine but when I tried the code, R says it's not an actual function so I thought I'd ask here. http://r.789695.n4.nabble.com/file/n4633035/Cookies.csv Cookies.csv I have attached the data I am using. I am trying to look at two things: how moisture content changes over time, and how it changes along the length of a log

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, A client has a consumption measure on each of four products. The sample size is 75. The consumption distributions are highly skewed for each product. He would like a pairwise comparison test of the products, much like Tukey's HSD but using medians rather than means. Is there such a median comparison test in R? Thanks, Walt ________________________ Walter R. Paczkowski, Ph.D.

Hola, tenemos como equivalencia no paramétrica al Anova de 1 vía el test Kruskal Wallis, pero ¿existe un test no paramétrico equivalente al Anova de 2 vías? Gracias. Saludos. -- Sec. Apoyo Estadístico. Servicio de Apoyo a la Investigación (SAI) Vicerrectorado de Investigación e Internacionalización. Universidad de Murcia Edif. SACE. Campus de Espinardo. 30100 Murcia @. mariaelvira.ferre en

Dear listers, I was used to package pgirmess under Windows with everything OK, but, for the first time, I had a trial this afternoon on Ubuntu 7.10 gutsy (I have a double boot computer and work more and more under unix) and R 2.7.0. Everything went OK except this: sudo R CMD check pgirmess ..... * checking examples ... ERROR Running examples in 'pgirmess-Ex.R' failed. The error most

Dear list! I would like to calculate "chisq.test" on simple data set with 70 observations, but the output is ''Warning message:'' Warning message: In chisq.test(tabele) : Chi-squared approximation may be incorrect Here is an example:         tabele <- matrix(c(11, 3, 3, 18, 3, 6, 5, 21), ncol = 4, byrow = TRUE)         dimnames(tabela) <- list(        

Dear list!   I would like to write a function to transform matrix, which is input argument of a written function. It is easy with new matrix (see below), but my idea is to transform input argument (matrix) of function without any additional matrixes. Here is an example: fun1 <- function(xy) { xy <- cbind(xy[,1], xy[,2], xy[,1] + xy[,2]) return(xy) }   df1 <- matrix(c(1,2,3,1,2,3), ncol =

Dear all, I did a Kruskall-Wallis test for a comparison of a variable of interest between 10 sites and I get a significant result (p=0.0019). however, when I perform a post-hoc test using kruskalmc from the pgirmess package, I get no difference between any of the paired comparisons. I cannot find anything in the internet, not in my stats books about how to explain this contradictory result. I

Hi, I would like to find out if the kruskalmc command in the pgirmess package controls for the family-wise type-I error rate. Thank you Dr. Iasonas Lamprianou Assistant Professor (Educational Research and Evaluation) Department of Education Sciences European University-Cyprus P.O. Box 22006 1516 Nicosia Cyprus Tel.: +357-22-713178 Fax: +357-22-590539 Honorary Research Fellow Department of

Dear list members! I am looking for ''nice solution'' for (maybe) simple problem. I need a code (small program) to calculate row index for max value (example below: df1$values) by groups (example below: df1$groups). df1 <- data.frame( groups = c(1,1,1,1,1,2,2,2,3,3,3,3), values = c(1,1,1,2,1,1,2,3,2,1,4,3) ) df1 expected results > 4 8 11 # row index of max values by group

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

Dear list! I have problem with buffer size (width) in package rgeos. I would like to expand given geometry (points) to specified width based on the z value from attribute table. Here is example: point <- data.frame(x=c(10,20), y=c(10, 10), z = c(2,7)) point_shp <- SpatialPointsDataFrame(point[,1:2],point) plot(point_shp, xlim = c(0,30), ylim = c(0,20)) plot(gBuffer(point_shp, width = 5,

Recently, I ran a series of Kruskal-Wallace tests [kruskal.test()] using by() to group by site Output is a list: >Herb.KW Herb.df$ID: 10-1 Kruskal-Wallis rank sum test data: Indicator_Rating by Year Kruskal-Wallis chi-squared = 15.24, df = 7, p-value = 0.03302 ----------------------------------------------------------------------------------------------------- Herb.df$ID: 18-1

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

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

I have run a kruskal.test() using the by() function, which returns a list of results like the following (subset of results): Herb.df$ID: 4-2 Kruskal-Wallis chi-squared = 18.93, df = 7, p-value = 0.00841 -------------------------------------------------------------------- Herb.df$ID: 44-1 Kruskal-Wallis chi-squared = 4.43, df = 6, p-value = 0.6187 So then, how do extract a vector