similar to: Wilcoxon test for mixed design (between-within subjects)

Hi, I would need to extend the Friedman test to a replicated design. Currently the function: friedman.test(y, ...) only works for unreplicated designs. I found in Conover 1999 "Practical Nonparamteric statistics" an extension of the formula to my case. Nevertheless, other sources, like Sheskin 2000 "Parametric and Nonparametric statistical Procedures" and Daniel 1990

Dear all, I have a problem installing a package required by Hmisc on MacOS 10.3.5 with R 2.00. g77 -fno-common -g -O2 -c avas.f -o avas.o g77 -fno-common -g -O2 -c rlsmo.f -o rlsmo.o gcc -bundle -flat_namespace -undefined suppress -L/usr/local/lib -o acepack.so ace.o avas.o rlsmo.o -L/usr/local/lib -L/usr/local/lib/gcc/powerpc-apple-darwin6.8/3.4.2

Dear list, I would like to plot the empirical cumulative distribution of the time needed by a treatment to attain a certain goal. A number of experiments is run with a strict time limit. In some experiments the goal is attained before the time limit, in other experiments time expires before the goal is attained. The situation is very similar to survivial analysis with censored data. I tryed

Dear all, I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric

Hi R Users This is a code I wrote and just want to confirm if the first 1000 values are raw gamma (z) and the next 1000 values are transformed gamma (k) or not. As I get 2000 rows once I import into excel, the p - values beyond 1000 dont look that good, they are very high. -- sink("a1.txt"); for (i in 1:1000) { x<-rgamma(10, 2.5, scale = 10) y<-rgamma(10, 2.5, scale = 10)

Hi, Could anyone provide the formula of the statistics which the wilcox.test used for the two-sample rank-sum test? I got some statistics of 0 values, but it is impossible to have 0 "rank-sum". Does the function use the Mann-Whitney U test statistics? Thanks. Ting-Yuan Liu

Hello there, I would like to do a Wilcoxon matched pairs signed rank sum test in R and tried the function wilcoxon.test. Is it in the "base" library? If not, please let me know which library it is in. Thank you in advance for your help Lisa Wang Biostatistician Princess Margaret Hospital Toronto, Ca

Hi, R allows me to run a one sample Wilcoxon test like this: wilcox.test(c(1,3.5,2.1,4,1.5,5), mu=2, exact=TRUE) The function 'wilcoxsign_test' from the package 'coin' should (I suppose) be able to calculate exact p values even if there are ties in the ranks. However, I couldn't find information on how to run a one sample test using 'wilcoxsign_test' like in the

Hi everyone! I need to perform a Wilcoxon rank sum test, but I have some ties and the groups have different size also. When I deal with ties I use the wilcox.exact function, how can I solve the different size problem using this function? thanks net -- View this message in context: http://r.789695.n4.nabble.com/Wilcoxon-rank-sum-in-unbalanced-design-tp3447400p3447400.html Sent from the R help

Dear R-ers, I am currently running some Wilcoxon tests in R-64. How do I find the degrees of freedom in the output I am receiving? > wilcox.test(good$TRUE, good$x4a, paired=FALSE) Wilcoxon rank sum test with continuity correction data: good$TRUE and good$x4a W = 2455, p-value < 2.2e-16 alternative hypothesis: true location shift is not equal to 0 Thank you, Stephen.

Dear all, I am using the ROCR library to compute the AUC and also the Hmisc library to compute the C-index of a predictor and a group variable. The results of AUC and C-index are similar and give a value of about 0.57. The Wilcoxon p-value is <0.001! Why the AUC is showing small value and the p-value is high significant? The AUC is based on Wilcoxon calculation? Many thanks, Lina

Hi. I'm going to introduce the R-package for a group of medical doctors later this week and is a little confused about there use of a test named "willcoxon-pratt" for testing if the clinical and biochemical markers has decreased significantly after the use of some drugs for a group of patients. Looking into the R-functions I would in R recommand using a matched-pairs Wilcoxon

Dear R-users, I?m trying to perform a non-parametric statistical pairwise comparison of two samples "x" and "y" using the Wilcoxon test in R, but each of the pairs have a weight associated. An example of my data is the following one: set.seed(9) x <- sample(x = c(1:100), size = 20, replace = TRUE) y <- sample(x = c(1:100), size = 20, replace = TRUE) weight <-

Hi! I need to run a wilcoxon (Mann-whitly, in fact) test with bonferroni correction, as I am running 10 consecutive wilcoxon test not independent, and I know that bonferroni will partially correct for this problem, but I have no idea how to do it with R, I have been looking in the archive but couldn't understand how to do it. The format I am using at the moment is r4_o <- [1]

Hi, I have a data set: > Dataset X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 1 user1 m 22 19 28 24 12 18 9 7 4 5 4 7 5 7 9 2 user2 f 25 19 23 18 18 15 6 8 6 6 7 10 7 7 7 3 user3 f 28 21 24 18 15 12 10 6 7 9 5 10 5 9 5 4 user4 f 26 19 26 21 12 18 6 6 5 1 3 8 6 5 6 5 user5 m 21 22 26 18 9 6 4 6 1

I am sure I am opening myself up to looking stupid, but I have two samples with medians of 613.5 and 189 (difference in location of 424 compared to the difference suggested from the wilcoxon of 291.5) > wilcox.test(pipwtCount,pipwdCount, conf.int=TRUE, na.rm=TRUE) Wilcoxon rank sum test data: pipwtCount and pipwdCount W = 822, p-value = 0.01227 alternative hypothesis: true location

Hello All, I am writing to ask your opinion on how to interpret this case. I have two vectors "a" and "b" that I am trying to compare. The wilcoxon test is giving me a pvalue of 5.139217e-303 of a over b with the alternative "greater". Now if I make a summary on each of them I have the following > summary(a) Min. 1st Qu. Median Mean 3rd Qu.

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:

Hi there, I'm a total newbie to R. I'd like to use a Wilcoxon Rank Sum test to compare two populations of values. Further, I'd like to do this simultaneously for 114 sets of values. The two populations are C and N. The different sets of values have arbitrary names (I'll call them a, b, c etc). The set-up is as follows: a b c d .... C 2 C 3 C 5 C 9 C 4

I am running a pairwise wilcoxon signed rank test, and I am not sure how to interpret the result. I would like to see if there is a difference between the values in conditions a and b. It doesn't seem possible to have a V = 0, but a significant p value. Am I doing something wrong? The command I used is this: wilcox.test(x=a$x,y=b$x,paired=TRUE) The output looks like this: Wilcoxon