similar to: Question about implementing statistical test in R

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 <-

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 DC9, As no one has answered, I would say that as both the survey package and Professor Lumley are widely respected, that is as good as it gets. Jim On Fri, Jun 23, 2017 at 7:26 PM, Dreams Collector <dreamscollector9 at gmail.com> wrote: > Dear R-users, > > I?m trying to perform a non-parametric statistical pairwise comparison of two samples "x" and "y" using

Hi there, I am trying to do multiple pairwise Wilcoxon signed rank tests in a manner similar to: a <- c(runif(1000, min=1,max=50), rnorm(1000, 50), rnorm(1000, 49.9, 0.5), rgeom(1000, 0.5)) b <- c(rep("group_a", 1000), rep("group_b", 1000), rep("group_c", 1000), rep("group_d", 1000)) pairwise.wilcox.test(a, b, alternative="two.sided",

how do I check the rank of a matrix ? say A= 1 0 0 0 1 0 then rank(A)=2 what is this function? thanks I did try help.search("rank"), but all the returned help information seem irrelevant to what I want. I would like to know how people search for help information like this. rank(base) Sample Ranks SignRank(stats) Distribution of the

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

Dear R-help, I have a question about pairwise.t.test and adjustment for multiple comparisons for paired data points. I have the following data: n=c("x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "y", "y", "y", "y", "y", "y",

Hi all, I'm not quite sure if this is a feature or a bug or if I just fail to understand the documentation: If I use cor() with pairwise.complete.obs and method=pearson, the result is a scalar: ->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson") [1] 0.9819805 The documentation says that " '"pairwise.complete.obs"' only

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example

# Hello, # I have a list with 6 categories and with different numbers of rows. # I would like to change each of them into a unique data frame in order to match # values with other data frames and perform some calculations. # Or I could make each category or list element have the same number of rows and create one large data.frame. # below is a creation of a sample list # I apologize for the

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000

Hi , I am new to R . I am facing difficulty how to make pairwise comparisions. For example. I have a file which looks like below a b c d x 3 6 7 6 y 7 8 6 5 z 5 4 7 8 Here I need to look for the each pairwise comparisions (ab,ac,ad,bc,bd,cd for each row) For instance ,looking at first row, for x i need to look for ab values and take the min(3,6) >5 ,if its satistfies the count should be

Hi! I have been working several years with R but it's my first public question. I hope I'll be clear :) . This question is related to obtaining letter-based representation of non-parametric pairwise comparisons. I have a dataframe with this structure (but with quite more rows and cols): A B C factor 1 2 2 one 2 1 2 one 2 2 3 two 2 3 2 two 1 4 2 three 9 8 1 three I have no normality,

Hi, I have a huge number of categorical variables, say at least 10000, and I put them into a matrix, each column is one variable. The question is: how can I make all of the pairwise cross tabulation tables efficiently? The straightforward solution is to use for-loops by looping two indexes on the table() function, but it was just too slow. Is there a more efficient way to do that? Any guidance

Dear R subscribees, First, I am new to R and I apologize if the question is naive. I am trying to find out where arguments specifications can be found. For example, the code below (at the very bottom of this email) contains the argument 'digits' in the function 'lin' which printout my output to the 3rd decimal place. Somebody showed me the 'digits' argument, but did

There was a BioConductor thread today where the poster wanted to find pairwise difference between columns of a matrix. I suggested the slow solution below, hoping that someone might suggest a faster and/or more elegant solution, but no other response. I tried unsuccessfully with the apply() family. Searching the mailing list was not very fruitful either. The closest I got to was a cryptic chunk

I am writing a little abstraction for a series of tests. For example, I am running an anova and kruskal.test on a one-factor model. That isn't a particular problem, I have an interface like: my.function <- function(model,data) { print(deparse(substitute(data))) a <- anova(lm(formula,data)) print(a) if(a$"Pr(>F)"[1] < 0.05) { pairwise.t.test(???) }

Hello, I have some patient data for my masters thesis with three groups (n=16, 19 & 20) I have completed compiling the results of 7 tests, for which one of these tests the variances are unequal. I wish to perform an ANOVA between the three groups but for the one test with unequal variance (<0.001 by both bartlett and levene's test) I am not sure what to do. I thought i would run

Hallo List, I'm trying to implemement a restricted permutation scheme in permutest(). More precisely I have dependence in my data that should be allowed for in the permutation - I simulated the problem in the example of the vegan documentation p.24: library(vegan) data(varespec) ## Bray-Curtis distances between samples dis <- vegdist(varespec) ## First 16 sites grazed, remaining 8 sites

Hi, I was trying to figure out how to do post-hoc tests for Two Way ANOVAs and found the following 2 approaches: a. Do pairwise t-tests (bonferroni corrected) if one finds significance with the ANOVA. Link- http://rtutorialseries.blogspot.com/2011/01/r-tutorial-series-two-way-anova-with.html b. Do TukeyHSD on an aov model Link-