similar to: pairwise.t.test for paired data

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,

Dear R Core, pairwise.wilcox.test does not handle "paired = TRUE" correctly; e.g. set.seed(13) x <- rnorm(20) g <- c(rep(1, 10), rep(2, 10)) wilcox.test(x ~ g)$p.value # 0.075 pairwise.wilcox.test(x, g)$p.value # 0.075, o.k wilcox.test(x ~ g, paired = TRUE)$p.value # 0.105 pairwise.wilcox.test(x, g, paired = TRUE)$p.value # 0.075, wrong The line wilcox.test(xi, xj,

Dear R-help, I'd like ask for your opinion on choosing the "right" strategy for a particular dataset. We conducted 24-hour electric field measurements on 90 subjects. They are grouped by job (2 categories) and location (3 categories). There are four exposure metrics assigned to each subject. An excerpt from the data: n job location M OA UE all 0 job1 dist_200 0.297 0.072 0.171

What's the difference between t.test(x, y) and pairwise.t.test()? Is it just that the former takes two vectors, whereas the latter takes a vector and a factor?

Dear R-users, In a randomized placebo-controlled within-subject design, subjects recieved a psycho-active drug and placebo. Subjects filled out a questionnaire containing 15 scales on four different time points after drug administration. In order to detect drug effects on each time point, I compared scale values between placebo and drug for all time conditions and scales, which sums up to

While building a package, I see the following: * checking R code for possible problems ... NOTE cheat.fit: no visible binding for global variable 'Zobs' plot.jml: no visible binding for global variable 'Var1' I see the issue has come up before, but I'm having a hard time discerning how solutions applied elsewhere would apply here. The entire code for both functions is below,

# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor

Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the

Hi! I use simtest fonction of multcomp package to compile a Dunnett's test. I have 10 treatments and one control group, so i create a matrix with: m<-matrix(0,10,11) m[1,1]<--1 m[1,2]<-1 m[2,1]<--1 m[2,3]<-1 m[3,1]<--1 m[3,4]<-1 m[4,1]<--1 m[4,5]<-1 m[5,1]<--1 m[5,6]<-1 m[6,1]<--1 m[6,7]<-1 m[7,1]<--1 m[7,8]<-1 m[8,1]<--1 m[8,9]<-1

I am familiar with pairwise t-tests, corrections for multiple testing, etc. however I have a problem whose answer I have not found after extensive R-help archive and Google searching. What I have done in the past: I have N items which are measured, exposed to a condition, and then measured again. I wish to know if the condition changes the items so I can perform a t-test. Better yet I can

Dear all, I am looking for an equivalent of the pairwise.t.test() function using a complex survey design. Any suggestions on this or how to account for weights using the pairwise.t.test itself? Thanks, V [[alternative HTML version deleted]]

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Dear list I am comparing the results of several different experimental setups. With kruskal.test() I can test if there is any difference at all in any of them, if I understand it correctly. But now, when there is a difference, how do I generate a (half-) table of pairwise comparisons, using e.g. wilcox.test(), to find the ones where the difference actually occurs. I guess I don't have to

Does anybody know if there is some sort of "pairwise" option for handling missing cases in lm and computing the relevant statistics? I would be much obliged if anyone could help... Regards Alan Simpson Roberts Research Group

Hi, I have a number of genes (columns) for which I want to examine pairwise associations of genotypes (each row is an individual)...For example (see data below), I would like to compare M1 to M2, M2 to M3, and M1 to M3 (i.e. does ac from M1 tend to be found with bc from M2 more often than expected.) Down stream I will be performing chi square tests for each pair. But I am looking for a way to

I want to create a MDS plot with pairwise Fst values derived from a population genetics project. My Fst values are in a tab-delimited file (lower triangle only) that I view with Excel. When I use the cmdscale command I get the message: Error in cmdscale(x) : Distances must be result of dist or a square matrix In addition: Warning messages: 1: "^" not meaningful for factors in:

Hello R listing, I did two-way anova on lm. Further question the investigator interested in is what two treatments are different? I am looking for a command which could do pairwise comparison for every treatment. Could anyone help me out? Thanks a bunch Kevin

How can I compute pairwise p-values in Kaplan-meier plots for three or more groups? bin.1<-cut(score,c(-1000,-1,1,1000),c("low","intermediate","high")) I use "km.coxph.plot" currently which reports one p-value. Thanks very much. [[alternative HTML version deleted]]

Dear R gurus, to compute the correlation matrix of "n" variables with "n_obs" observations each, possibly including NA, I use cor(M, use="pairwise.obs") where m is a "n" x "nobs" matrix. Now I want to know the number of observations actually used in this computation, namely for each pair of columns in M, say pair (i,j), I want to compute sum(

There are two line 216 and 218 Three development stages 5 WEEK (5W), 7W, 9W. Three tissue: Ca, Co, Pa each with 2 biological replicate. With two biological replicate. I want to do differential gene expression analysis using DESeq2 so I tried these codes after reading about DESeq2: ,my aim is to do the pairwise comparison. how to make colData and design formula. library("DESeq2")