similar to: wilcox.test statistics

Dear all, I want to compare the efficiency of 2 methods in extracting proteins from algal samples. I collected 6 independant algal samples and I extracted 3 by the method 1 and 3 others by the method 2. So I have 2 groups of 3 samples, that are not paired. I would like to know if the results obtained by these 2 methods are significantly different, I hope method 2 to be more efficient than method

Hallo, is there any extension of the pairwise Wilcoxon test to a dependent samples layout with replicates (or, in other terms, a one-way layout with blocking and replicates)? The Wilcoxon method with matched pairs works for the case of dependent samples with one observation per block, while the Mann-Whitney test works for independent samples, thus one single block and replicated observations. Is

Hi, I believe I have significantly improved [dpq]wilcox functions by implementing Harding's algorithm: Harding, E.F. (1984): An Efficient, Minimal-storage Procedure for Calculating the Mann-Whitney U, Generalized U and Similar Distributions, App. Statist., 33, 1-6 Results on my computer show (against R-2.9.1): > system.time( dwilcox( 800, 800, 80) ) user system elapsed 0.240

I'm working on a quick tutorial for my students, and was planning on using Mann-Whitney U as one of the tests. I have the following (fake) data grade <- c("MVG", "VG", "VG", "G", "MVG", "G", "VG", "G", "VG") sex <- c( "male", "male", "female", "male",

Hi, Could someone please tell me how to perform a Mann-Whitney U test on a dataset with 2 groups where one group has more data values than another? I have split up my 2 groups into 2 columns in my .txt file i'm using with R. Here is the code i have so far... group1 <- c(LeafArea2) group2 <- c(LeafArea1) wilcox.test(group1, group2) This code works for datasets with the same number

Hi all, I have being using wilcox.test to test for differences between 2 independent samples. I had understood the difference in location to be conventionally the difference in the sample medians however this is not the case when implemented in R. I have tied ranks and therefore non-exact p-value and confidence intervals are calculated due to the normal approximation. But what exactly is this

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 <-

Hello, I'm trying to compare the allozyme data from two taxa. I have several columns of data (19 loci) for each species. I want to do a Mann-Whitney U-test or the wilcox.test (two sample Wilcoxon). When I try to run my code (the first two columns are 1:name of the species, 2:name of individual) I get the error message: "Error in wilcox.test.default(CaScSc, CaScCo, alternative

Dear Sir, I am comparing two samples using wilcox.test in R. Literature appears to describe mann whitney u test as the most appropriate test to use on my data. is the wilcox.test function equivalent to mann-whitney u? Is there a way to gain the U-value as apposed to the W-value in R? Thank you

Good day all, My first posting to this list. It looked like the best place to post this question. When running the wilcox.test(), I noticed that the output values change if you change the ordering of the levels (example below which includes a t.test for comparison). I think this has something to do with the change in ranking order, but this doesn't make much sense as I would expect the

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

Hi folks, sorry for this beginner question but what means a p-value = NA on a menn-whitney test? v1 <- c(0.022, 0.022, 0.022, 0.022, 0.022, 0.022) v2 <- c(0.022, 0.022, 0.022, 0.022, 0.022) wilcox.test(v1, v2, PAIRED=FALSE) W = 15, p-value = NA I know that there's no statistical difference between v1 and v2, so why my p-value is not ONE ?? Can I consider p-value= NA as ONE ?? what i

Hi, I know that I can perform a Mann-Whitney U test with wilcox.test(x,y) But instead of an U-value I get a W-Value. Is it possible to change this?

Hello List, I'm trying to prepare some lecture notes on non parametric methods, and I can't manually reproduce the results of the wilcox.test function for ordinal data. The data I'm using are from David Howell's website, available here http://www.uvm.edu/~dhowell/StatPages/More_Stuff/OrdinalChisq/OrdinalChiSq.html If I run the wilcox.test function on the data I get a p-value of

Hello, I am hoping someone could shed some light into the Wilcoxon Rank Sum Test for me? In looking through Stats references, the Mann-Whitney U-test and the Wilcoxon Rank Sum Test are statistically equivalent. When using the following dataset: m <- c(2.0863,2.1340,2.1008,1.9565,2.0413,NA,NA) f <- c(1.8938,1.9709,1.8613,2.0836,1.9485,2.0630,1.9143) and the wilcox.test command as

Hello, can someone please help me with coding a function for a bootstrap WMW test (package boot, R under Windows, version 1.6.2)?

Dear r-helpers, I want to test groups of samples for significant differences. Question: Does Group1 differ significantly from group2. This is a question to be answered by mann-whitney-u-test. I know that I can use wilcox.test with 2 samples. My problem: How can r perform the test automatically if there are more than 2 groups in my data frame. Test group1 vs. 2, 1 vs. 3, 1 vs. 4, etc. This is

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]

The function 'wilcox.test' in R and S gives (almost) identical results (see below). 'qwilcox' however, does not: > qwilcox(p,5,5) p: 0.025 0.975 -------------------- R> 3 22 S> 18 37 I originally wanted to ask a questions, but then I found the answer. Given the confusion I run into, I wonder if this experience is worth reporting. The

Hello everyone, I'm having some trouble interpreting the results of a Wilcoxon (Mann-Whitney U) test. Hope you can help. This is the R script that I am running: a <- c(1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 5, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1) b <- c(1, 2, 1, 1, 2, 3, 2, 2, 1, 2, 1, 1, 1, 2) wilcox.test(a, b, alternative="t", mu=0, exact=FALSE, paired=FALSE) #1st