similar to: paired t-test vs pairwise t-test

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, how can I extract the t-values after running a pairwise.t.test? The output just list the p-values. Many thanks for your help. Cheers Guido ____________________________________ Guido J. Parra School of Tropical Environment Studies and Geography James Cook University Townsville Queensland 4811 Phone: 61 7 47815824 Fax: 61 7 47814020 Mobile: 0437630843 e-mail:

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# I'm doing a pairwise.t.test on a large dataset and need the output in a data frame so I can work further with it, e.g. so I can export it to a spreadsheet. Is there any way to coerce the results to an exportable format? # For example, if I do: test <- pairwise.t.test(numbers, factors, p.adj="bonferroni") # and then write.table(test, file="output.csv",

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,

Faraway's book titled "Practical Regression and Anova using R", with full text available online at: http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf refers to a data set, stat500, which compares midterm and final grades. It can be used to illustrate similar concepts. A google search for faraway.zip will locate the actual data. --- Date: Sun, 15 Feb 2004 10:37:08 -0800

Dear All, I am doing some exercise in statistics textbook on comparison of two experimental means. Is it possible to use t.test() do t-test when I have only two means, sample size, two standard deviations ? (no raw data). Thanks. Pramote

Hi all, After my reply on R-Help to the relevant thread, I noted what appear to be a couple of typos in the Details section of ?pairwise.t.test. Note text with '**'. Current text: The **pool.SD** switch calculates a common SD for all groups and **used** that for all comparisons (this can be useful if some groups are small). This method does not actually call t.test, so extra arguments

Hi R users, Could anyone let me know how to export a paired t-test output table (see below) to an excel file? Jason, with(score2,pairwise.t.test(values,ind, + p.adjust.method="holm", paired=T)) Pairwise comparisons using paired t tests data: values and ind test1 test2 test3 test4 test5 test6 test1 1.0000 - - -

http://r.789695.n4.nabble.com/file/n4630909/pfaff_fig1.gif I have run the statistics and found no significance in my pairwise t test. I want to create a graph similar to the one I included showing similar slopes/lines for my data points. For my data a correlation graph is not appropriate and looks very confusing. Is the easiest way to create several lines between points I enter manually or is

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

Hi, If I set the p.adjust="none", does it meant that the output p values from the pairwise.t.test will be the same as those from individual t.tests (set var.equal=T, alternative="t")? I actually got different p values from the two tests. See below. Is it supposed to be this way? Thanks Johnny > x [1] 61.6 52.7 61.3 65.2 62.8 63.7 64.8 58.7 44.9 57.0 64.3 55.1 50.0 41.0

Hi list-members can anybody tell me why > pairwise.t.test(val, fac) produces an empty p-table. As shown below: Pairwise comparisons using t tests with pooled SD data: val and fac AS AT Fhh Fm Fmk Fmu GBS Gf HFS Hn jAL Kol R_Fill AT - - - - - - - - - - - - - Fhh - - - - - - - - - - - - - Fm - - - - - - -

I'm trying to personalize the rpart function by introducing a list('init','split','eval') in the argument method. But I receive an error message:"Error in t.default(x): argument is not a matrix". Can anyone tell me what the argument of this function is or where this function appears in the rpart function.Thanks Simone Vantini

Hi everyone, I am quite new to R. my command is t.test(c(1:5,7:11), y=c(1:10),alternative = c("two.sided"), paired = TRUE) The output is Paired t-test data: c(1:5, 7:11) and c(1:10) t = 3, df = 9, p-value = 0.01496 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1229738 0.8770262 sample estimates: mean of the differences

Dear all, I am studying a dataset using the aov() function. The independant variable 'cds' is a factor() with 8 levels and here is the result in studying the dependant variable 'rta' with aov() : > summary(aov(rta ~ cds)) Df Sum Sq Mean Sq F value Pr(>F) cds 7 0.34713 0.04959 2.3807 0.02777 Residuals 92 1.91635 0.02083 The dependant variable

Hello, new to the list, first message. This question perhaps might be more appropriate to R-sig-teaching, and I'd be happy to take it there if this is not the right place for it. I am teaching applied statistics at a small liberal arts college with limited resources, and we are currently using SPSS for our courses. Mainly the reason for this, as I understand it, is that this is what

pairwise.t.test is returning NAs when one of the samples only has one entry, while TukeyHSD returns results (maybe not trustworthy or believable, but results). I stumbled on this because I did not realize one of my samples only had one entry while most of the others had several hundred, so I realize this is not a desirable situation. I'm really just curious about the difference between how

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",

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On