similar to: Using statistical test to distinguish two groups

Folks, It has been a whole day spent on this but I still can''t get my mind around the most probably very stupid/basic mistake here... First, the code I use: <h3>List A</h3> <ul id="lista" > <li id="item1"> Item 1</li> <li id="item2"> Item 2</li> <li id="item3"> Item 3</li> </ul>

Hi, I am attempting to use phyper to test the significance of two overlapping lists. I keep getting a zero and wondered if that was determining non-significance of my overlap or a p-value too small to calculate? overlap = 524 lista = 2784 totalpop = 54675 listb = 1296 phyper(overlap, lista, totalpop, listb,lower.tail = FALSE, log.p=F) [1] 0 If I plug in some different values I get a p-value but

Hi everybody, I am currently quite inexperienced with R. I try to create a function that simply take a value in a dataframe, look for this value in another dataframe and copy all the row that have this value This example is a simplified version of what I am doing but it's enough to help me listA Name NACE a 1 b 2 c 3 ListB NACE aaa bbb ccc 1 a a c 1 a a c 1 a a c 2 a a c 2 a a c 3 a a c 4 a

Can anyone suggest a good non-parametric test, and an R implementation of the test, that allows for two or more factors? Wilcoxon signed rank allows for only one. Thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC and University of Maryland School of Medicine Claude Pepper OAIC University of Maryland School of Medicine Division of

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 there, Given 2 lists of integer vectors, i.e.: > lista1 $"1" [1] 1 34 5 $"2" [1] 2 1 $"3" [1] 3 10 15 > lista2 $"1" [1] 1 5 $"2" [1] 2 1 $"3" [1] 3 10 29 I want to obtain the union of both, defined as the union of the vectors, that is lista.union[[1]] <- union(lista1[[1]],lista2[[1]]): > lista.union

On 20 May 2014 17:55, Tobias Grosser <tobias at grosser.es> wrote: > On 20/05/2014 18:20, Yi Kong wrote: >> >> On 20 May 2014 16:40, Tobias Grosser <tobias at grosser.es> wrote: >>> >>> On 20/05/2014 16:01, Yi Kong wrote: >>>> >>>> >>>> I've set up a public LNT server to show the result of perf stat. There

Hi, I'm not used to thinking along these lines, and wanted to ask your advice: Suppose you have a sample of around 100, consisting of patients according to doctors, in which patients and doctors are given a questionnaire with categorical responses. Each patient somehow has roughly 3 doctors, or 3 rows of data. The goal is to assess by category of each question or DV the agreement between

I have 3 sortables, namely lista, listb and listc. Lista being a static list of items and will be drag n drop to listb and listc. Everytime I drag from lista to either listb or listc, the items in lista will be removed. Is that normal? If so, can anyone advice how can I maintain the items in lista so that I can have duplicate items in listb and listc. I am using rails for the development.

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

Hello, I got two sets of data x=(124738, 128233, 85901, 33806, ...) y=(25292, 21877, 45498, 63973, ....) When I did a t test, I got two tail p-value = 0.117, which is not significantly different. If I changed x, y to log scale, and re-do the t test, I got two tail p-value = 0.042, which is significantly different. Now I got confused which one is correct. Any help would be very appreciated.

Probably, it's an obvious info, but I have not found anything in R FAQ concerning this feature/bug. The results of ks.test and wilcoxon.test (in the Mann-Whitney version, paired = 'FALSE') don't coincide with the results from the other statistical packages, e.g. Statistica, Medcalc, and (as for MW test) from the numerous online MW tests. E.g. Statistica p-value=0.0435353 Medcalc

Dear R members, I have been trying to find out whether one can use multivariate regression trees (for example mvpart) to analyze repeated measures data. As a non-parametric technique, CART is insensitive to most of the assumptions of parametric regression, but repeated measures data raises the issue of the independence of several data points measured on the same subject, or from the same plot

Hello I am interested in finding out a method of power analysis (effect size and sample size calculation ) using R in non parametric methods? I am running R 2.8.1 running on linux open SUSE Any libraries or documentation , I was not bale to google up any. Thanks in Advance, Ben-Ari Alon, MD University of Pittsburgh. [[alternative HTML version deleted]]

hi, knowing there are a lot of statistics guru's in the R-mailing list, i throw in a statistics question. i have data that i do not know how to statistically test: subjects are repeatedly asked to make a decision (e.g. left-right -> coded as 0 or 1). i have 20 subjects, each subject made 8 decisions. i now want to analyse whether my experimental manipulation induced a systematic bias

Hi All: I was looking for, but could not locate in the packages, or in the R archive searches if there exists an R implementation of Cuzick's test of trend. The test is described as follows: An extension of the Wilcoxon rank-sum test is developed to handle the situation in which a variable is measured for individuals in three or more (ordered) groups and a non-parametric test for trend

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

Dear R users, I would like to know if there is a way in R to execute a post-hoc test (factor levels comparison, like Tukey for ANOVA) of a non-parametric analysis of variance with kruskal.test() function. I am comparing three different groups. The preliminary analysis using the kruskal-wallis-test show significance, but I still don''t know the relationship and the significance level

We need a two-way non-parametric ANOVA in order to analysis properly some ecological data, do you know any reference in R? or how to do it? Thank you very much All the best diana -- View this message in context: http://www.nabble.com/Two-way-non-parametric-ANOVA--tp15039308p15039308.html Sent from the R help mailing list archive at Nabble.com.

Hello, Thanks James for the reply. That cleared a few things out. Apologies for replying late because of exams going on. I was going through the previous clustering API to understand how it worked and it seems like the the approach for construction of the termlists which are used for distance metrics use TF-IDF weighting with cosine similarity, which is very similar to the approach I would need