similar to: Friedman test with replicated samples

Hi, I would need to extend the Friedman test to a replicated design. Currently the function: friedman.test(y, ...) only works for unreplicated designs. I found in Conover 1999 "Practical Nonparamteric statistics" an extension of the formula to my case. Nevertheless, other sources, like Sheskin 2000 "Parametric and Nonparametric statistical Procedures" and Daniel 1990

Dear all, quite a naive question: I have a data frame and I computed "the kernel density estimate" with density on each column. Now I'd like to export in a txt file the density function output for each column, but, when if I use write.table, I get a message "Error in as.data.frame.default(x[[i]], optional = TRUE) : can't coerce class "density" into a

I am trying to use the friedman.test() on a data frame, d, but I am receiving the following error message: > d AW HS IAC WA 1 6 8 3 5 2 2 2 3 6 3 7 7 8 3 4 8 5 4 5 .... 20 2 5 2 7 21 7 7 6 7 22 7 8 6 8 23 6 8 4 5 24 5 7 5 2 > friedman.test(d) Error in any(is.na(groups)) : argument "groups" is missing, with no default I think I

Some attribute x from 17 individuals was recorded repeatedly on 6 time points using a Likert scale with 7 distractors. Which statistical test(s) can I apply to check whether the changes along the 6 time points were significant? set.seed( 123 ) x <- matrix( sample( 1:7, 17*6, repl=T ), nrow = 17, byrow = TRUE, dimnames = list(1:17, paste( 'T', 1:6, sep='' )) ) I found

Dear R-users, tsboot fails when I try to perform a block bootstrap on seaKen (package wq): these commands: require(wq) require(datasets) boot.block.sen <- function(data){seaKen(data)[[1]]} tsboot(sunspot.month, boot.block.sen, R=1999, l=12, sim="fixed") return: Error in seaKen(data) : x must be a 'ts' Any suggestion on how might I change seaKen in order to use it with

> R.version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 8.1 year 2008

There are two misspellings in the german Error message for friedman test: Fehler in friedman.test.default(cont$score, group = cont$goup, blocks = cont$cont) : y, Gruppen und blöcke müssen die sekbe Länge haben The correct spelling would be: Fehler in friedman.test.default(cont$score, group = cont$goup, blocks = cont$cont) : y, Gruppen und *Blöcke* müssen die *selbe* Länge haben Instead of

Dear all, I'm using friedman rank test in R. Need to know how to calculate rank sum in friedman test (R options to get rank sum in friedman test). Kindly help me. With regards jeevitesh

Hello, I have problems interpreting the results of a Friedman test. It seems to me that the p-value resulting from a Friedman test and with it the "significance" has to be interpreted in another way than the p-value resulting from e.g. ANOVA? Let me describe the problem with some detail: I'm testing a lot of different hypotheses in my observer study and only for some the premises

I have to start by saying that I am new to R, so I might miss something crucial here. It seems to me that the results of friedman.test and ks.test are "wrong". Now, obviously, the first thing which crossed my mind was "it can't be, this is a package used by so many, someone should have observed", but I can't figure out what it might be. Problem: let's start with

Hello everyone, I have been trying to perform a Friedman test for a while with no success. I have tried the command: > friedman.test (y~A|B, data = mydata), but it doesn't work . I searched youtube for help, but still could not find much. Can anyone help me with that! Thank you very much, Pedro [[alternative HTML version deleted]]

Hi I am looking to generate permutations for data tested using the friedman.test. Just read a paper by Joachim Rohmel, "The permutation distribution of the Friedman test" (Computational Statistics & Data Analysis 1997, 26: 83-99). He offers APL code for carrying out these procedures; wondering if anyone has recoded this program for R? Any direction/help on this would be very much

That worked perfectly! Thanks! ---------- Forwarded message ---------- From: Eli Friedman <eli.friedman at gmail.com> To: LLVM Developers Mailing List <llvmdev at cs.uiuc.edu> Date: Thu, 22 Jan 2009 18:52:22 -0800 Subject: Re: [LLVMdev] Problem invoking win32 api calls On Thu, Jan 22, 2009 at 6:43 PM, Vijay D <techvd at gmail.com> wrote: > Hi, > > I use the kaliedoscope

Estimados Este es el test de friedman que se logra asi library(PMCMR) y <- matrix(c( 3.88, 5.64, 5.76, 4.25, 5.91, 4.33, 30.58, 30.14, 16.92, 23.19, 26.74, 10.91, 25.24, 33.52, 25.45, 18.85, 20.45, 26.67, 4.44, 7.94, 4.04, 4.4, 4.23, 4.36, 29.41, 30.72, 32.92, 28.23, 23.35, 12, 38.87, 33.12, 39.15, 28.06, 38.23, 26.65),nrow=6, ncol=6, dimnames=list(1:6,LETTERS[1:6])) print(y)

My data looks like this: (treatments) T1 T2 T3 DK 8 5 3 JP 5 4 1 AS 9 7 4 MK 8 4 4 DK, JP, AS, and MK are 4 different people (blocks) I am using. This is my code

Hi there all, This is my first post to the list and I'll first say a few things: - R is great! - The archives of this list have helped me solve all of my problems/questions so far - I only know enough statistics "to be dangerous" I'm looking for a way to do post-hoc tests for the Friedman test. I have a dataset from a within-subjects design with 5 conditions where some of

Dear all Is there any way of doing a Tukey HSD post-hoc test after a Kruskal- Wallis or Friedman rank sum test (in the ctest package)? Thanks in advance, Albertus Dr. Albertus J. Smit Department of Botany University of Cape Town Private Bag Rondebosch 7700 South Africa Tel. +27 21 689 3032

Hi, pchisq -> distribution function dchisq -> density function pval is the area under the curve, to calculte it you use distribution function which is the integral of density function. See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm http://mathworld.wolfram.com/DistributionFunction.html f(x) density function F(x) distribution function =Pr(X<x)= integral(f(x))

Hi all, I have been trying to reproduce an analysis from Douglas Montgomery?s book on design and analysis of experiments. Table 6.10 of example 6.2 on page 246, gives a table as follows: > NPK <- expand.grid(A=mp,B=mp,C=mp,D=mp) > Rate <- c(45,71,48,65,68,60,80,65,43,100,45,104,75,86,70,96) > filtration <- cbind(NPK,Rate) > filtration A B C D Rate 1 - - - - 45 2

Hi, I simplified the config in my first email, but I actually have 2x4 servers in replicate-distribute with each 4 bricks for 6 of them and 2 bricks for the remaining 2. Full healing will just take ages... for a just single brick to resync ! > gluster v status home volume status home Status of volume: home Gluster process TCP Port RDMA Port Online Pid