similar to: Question about package coin

Dear R helpers, I am trying to understand how to use the independence_test function in the coin package. I think I suffer from a misunderstanding about what the package does. Either that or I do not understand how to use it properly. Specifically, I cannot understand if I can test independence of arbitrary statistics. Take the following example: set.seed(10) d <- data.frame(y = c(rnorm(10,

Dear List, I'm interested in performing a permutation test on the interaction between a binary treatment indicator and a covariate (either continuous or categorical). I'm interested in the p-value of the interaction effect from a permutation test, and I'm using the coin package for that purpose. As I haven't seen any examples like this in the package documentation (or anywhere

Dear sir, I am Shibu John from Thrombosis Research Institute India. It is a multidisciplinary organisation concerned with the interrelated problems of thrombosis and atherosclerosis. I was searching for Cochran armitage trend test program in R. Then I had seen your R coding for C-A trend test. I tried that in the R software. But I can?t run the program due the [Error: could not find function

Is it possible to implement the Jonckheere-Terpstra test for ordered alternatives using the coin package: Conditional Inference Procedures in a Permutation Test Framework? I found jonckheere.test{clinfun}, but it uses a normal approximation when ties are present in the data. To make this concrete, I've include a small dataset. Thanks. --Dale Hollander and Wolfe, 1999 Table 6.6, pg 205

Dear R-list members, This is in fact a question about statistics, not directly about the R software. The coin package, for conditional inference through permutation methods, has as it main function the function independence_test. One of its parameters is teststat, about which the package documentation says: teststat: "a character, the type of test statistic to be applied: either a

Hello, Would you tell my how to extract a result from a test - it's justified because I need to run this test many times. Here is an example from authors' test: > library("coin") > lungtumor <- data.frame(dose = rep(c(0, 1, 2), c(40, 50, 48)), tumor = c(rep(c(0, 1), c(38, 2)), rep(c(0, 1), c(43, 7)), rep(c(0, 1), c(33, 15)))) > ca.test<-independence_test(tumor ~

I am using "apply" to run exact permutation t-tests by columns using the coin package. For example: library(coin) dat <- matrix(rnorm(7*35),7,35) fun <- function(x) { pvalue( independence_test(x~f, data=data.frame(x, f=factor(c(rep("a",4),rep("b",3)))), distribution = "exact")) } p.vals <- apply(dat, 2, fun) Some small-scale

HI R-users, I am trying to repeat an example from Rayner and Best "A contingency table approach to nonparametric testing (Chapter 7, Ice cream example). In their book they calculate Durbin's statistic, D1, a dispersion statistics, D2, and a residual. P-values for each statistic is calculated from a chi-square distribution and also Monte Carlo p-values. I have found similar p-values

Dear fellow R users, Keywords: Kruskal-Wallis, Post-Hoc, pair-wise comparisons, Nemenyi-Damico-Wolfe-Dunn test, coin package, oneway_test I am using the "oneway_test" function in the R package "coin" and I am obtaining results which I cannot believe are accurate. I do not wish to waste anyone's time and so if the following problem is rather trivial, I apologize, however I

Hallo Is there an elegant way to do the following: Dataset consists of 2 variables: var1: some measurements, and var2: a grouping variable with two values, 1 and 2. There are (say) 10 measurements from group 1 and 15 measurements from group 2. The idea is to study the permutation distribution of mean(group 1) * mean(group2). One way would be to permute 1s and 2s and select the corresponding

I would like to compare the incidence rates of three groups. They are supposed to have different risks so I would like to test whether there is a increasing trend in the incidence rates. Does R or any packages provide a trend test for incidence rates? I checked epiR and epitools. It seems they do not have this function. Thank you for the help. -- View this message in context:

I am perfectly aware that this question is not an R question, at least not yet, but I have not succeeded in finding what I want in other ways, so ... What I am looking for are two algorithms, preferabley in Pascal, but other languages may do. For (a) systematic (complete) permutations for grouped data with unequal group sizes, and (b) random permutations for the same kind of data. I know

Are there internal methods available for R2.6 (I'm using the mac os x gui) for extracting (or subsetting) S4 objects? Using the independence_test() function implemented in the COIN package, I can't seem to select out p-values upon completion of each iteration of a loop. Sorry if my search was incomplete beforehand. -- View this message in context:

Dear R-enthusiasts, I am trying to do a Cochran-Armitage test for trend in R. After consulting google I found Torsten Hothorn's remark that the 'coin' library could be used. lungtumor <- data.frame(dose = rep(c(0, 1, 2), c(40, 50, 48)), tumor = c(rep(c(0, 1), c(38, 2)), rep(c(0, 1), c(43, 7)),

Hi, I'm trying to run a permutation test on paired samples. First I tried the package "exactRankTests": require("exactRankTests") x <- c(1.83,0.50,1.62,2.48,1.68,1.88,1.55,3.06,1.30) y <- c(0.878,0.647,0.598,2.05,1.06,1.29,1.06,3.14,1.29) wilcox.test(x,y,paired = TRUE,alternative = "greater") perm.test(y,x,paired = TRUE,exact = TRUE,alternative =

Apropos the question about permutation tests in multiple regression: We do have perm.test in package ExactRankTests, but it does one- and two-sample tests, as in t.test, wilcox.test, etc. There doesn't seem to be an exact version of the permutation test for correlations, i.e., the one that could be estimated using replicate(10000, cor(x,sample(y))) # or other values of 10000 or, of course,

Dear R users, I am trying to perform a hypothesis test on a marked point pattern. I would like to calculate the mean of the absolute value of the difference of marks between nearest neigbours, randomize the marks among points, then calculate this mean again. Ideally, I would test whether random mean values smaller than the observed mean value occur less than 5% of the time. I suppose 1000

I partly answered my question since independence_test() function in coin package apparently do Cochran-Armitage trend test just like Eric Lecoutre's function tabletrend() - slightly modified here: > independence_test(pheno ~ geno, data = dat2, teststat = "quad", scores = list(geno = c(0, 1, 2))) Asymptotic General Independence Test data: pheno by groups 1 < 2

Using the package ?exactRankTests? one can execute a one-sample permutation test for a hypothesized location parameter of 0 like: perm.test(rnorm(30,0)) The package ?exactRankTests? seems now to be deprecated in favor of the ?coin? package which as I understand is a superset of ?exactRankTests? in terms of functionality. The ?coin? package allows one to run a two-sample permutation test using

Conditional Inference Procedures in a Permutation Test Framework The `coin' package implements a general framework for conditional inference procedures, commonly known as permutation tests, theoretically derived by Strasser & Weber (1999). The conditional expectation and covariance for a broad class of multivariate linear statistics as well as the corresponding multivariate limiting