similar to: perm.test()

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 =

Hi! I am doing a permutation test on 7 data randomly selected into two groups, one has 4 and the other 3. The purpose is to list all the 35 possible outcomes and calculate p-value. I googled the function perm.test() and not sure if my usage is correct. So I set a=c(7 data), b=(1,1,1,1,2,2,2) which represents the group. Then I use the function pt=perm.test(a,b...). There's a p-value popped

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,

Hi, I have a little problem that puzzles me about contradictory results returned by a bootstraped t-test (as in MASS 3rd ed p. 146) versus a permutation t-test (as in MASS 3rd ed, p 147). Data are measurements done on 100 cells in 9 slides of fish blood. With one method, cells are randomly sampled, and with the other method, the operator selects cells arbitrarily (in a way it is done usually

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

Hi, The permutation test for two samples in R is function perm.test(). I could not figure out what is the statistics it estimate and how many permutation it did in default? Thanks, [[alternative HTML version deleted]]

Hi All: OK, this is probably a stupid thing to do but....... Say I have 706 controls and 6 treatments, and want to do a basic two-sample permutation test. I could do a monte carlo approximation but instead want to use perm.test. Now it happens that my data is counts with > unique(x) [1] 1 2 > unique(y) [1] 1 2 5 3 10 9 7 4 6 11 8 23 18 30 16 29 42 so there aren't that many

Dear R friends, I want to compare two datasets and I get the message Error in .Call("cpermdist2", ma = as.integer(m), mb = as.integer(col), : negative length vectors are not allowed after specifying the exact test. I'm using the exactRankTests package. Do you suggest me using the coin library, or is there anything "wrong" with my data? Kind regards, David --

Hi *, is there a way to obtain some kind of p-value for a model fitted with RMA using the lmodel2 package? I know that p-values are discussed and criticized a lot and as you can image from my question I'm not very much of a statistican (only writing my bachelor thesis). As fare as I understood the confidence interval statistic correctly, a coefficient is regarded as statistically significant

Hi all, Suppose we have the following matrix m <- matrix(c(1,2,3,2,1,3,3,1,2), ncol = 3, byrow=T) where in each row each number occurs only once. I'd like to define a permutation, e.g. 1 -> 2, 2 -> 1, 3 -> 3 and apply it to the matrix. Thus, the following matrix should result: m.perm <- matrix(c(2,1,3,1,2,3,3,2,1), ncol = 3, byrow=T) i.e. each 1 should map to 2 and vice

Hi all Consider the classic data below from Darwin on the heights of 15 pairs of zea mays (corn) plants either cross-fertilized or self-fertilized, where the goal is to see if it makes a difference. > head(ZeaMays) pair pot cross self diff 1 1 1 23.500 17.375 6.125 2 2 1 12.000 20.375 -8.375 3 3 1 21.000 20.000 1.000 4 4 2 22.000 20.000 2.000 5 5 2 19.125

Hi everyone, I am somewhat new to R and I am trying to write a permutation function such that it inputs a character vector and from an arbitrary length "n" which is the length of the combinations for the character vector. I know there are R packages for permutation but this is for an assignment. So far this is what I have: alphabet =

Dear R Programmers, I am trying to run a Poisson regression on all pairs of variables in a data set and obtain the permutation distribution. The number of pairs is around 100000. It seems my code will take weeks to run, unless I try something else. Could you give me any suggestions on how to improve the speed of the code below, or any general suggestions on how I may accomplish this task. Thanks

I saw the manual of this function but not sure what to do. I have a array contain 7 numbers and want to choose 4 to do permutation test. But using this function with parameters as onet.permutation(scores,4), it returns 0. Instead, with no parameter, onet.permutation(), it returns something but every time it's different. Please tell me how am I supposed to do it. Thanks a lot -- View this

Hello dear subscribed Users, this is my first post, so please forgive me for any inconveniences. The following problem: I have a dataframe containing a factor column. For each column i would like to compare means as parted by the factor. Using the normal t.test function I have already achieved my goal. But i think, that for my original data, a permutational t.test would be better suited.

Hello, I am using logistic discriminant analysis to check whether a known classification Yobs can be predicted by few continuous variables X. What I do is to predict class probabilities with multinom() in nnet(), obtaining a predicted classification Ypred and then compute the percentage P(obs) of objects classified the same in Yobs and Ypred. My problem now is to figure out whether P(obs) is

Dear all, I did a RDA and when I looked to the signification of the test with permutest, the output was non-significant. But when I used the envfit function, some of the vectors are significant. All the test's conditions are respected. What it means? Is it an error in the script? Commands and output: > permutest(rda.ind, perm=999, first=TRUE) Permutation test for rda  Call:

Hi, I want to ue a randomisation test, to compare two sample means, for a small set of data, I've looked at towt.permutation, perm.test and permtest, as well as permTS(), which is the best one to use for such a simple purpose? I know perm.test package isn't used anymore and coins recommended, but reading the manual I couldnt find the function most like it, ore more likely I

I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest

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