similar to: npmc and data.frame problems

I followed the threads that enquired about doing post-hoc tests after doing Kruskal testing. It took me to npmc. But npmc is giving an output I do not understand. I noticed a thread entitled "npmc function: 'x' must be atomic" but there never appeared to be a resolution. > npmc(npmcinput) Error in sort(unique.default(x), na.last = TRUE) : 'x' must be atomic

Hi, I recently tried to conduct non-parametric multiple comparison with npmc package, and encountered some problem; sometimes it stopped with following error. Error in uniroot(f = function(arg) p - z.dist(arg, corr = corr, df = df, : f() values at end points not of opposite sign Although I'm not quite sure about calculation of z.quaintile in npmc.R, it seems increment of

HI, I am a new user of R and want to analyse some data using npmc. My data have several levels of factor (Site, Year and Season) and several variable (Percentages). I have tried to use npmc but I always get an error message. My data are in a table following this example: Site YEar Season Var1 Var2 .... A 2009 Dry 10 56 B ........ here is the error

Hi folks, sorry if this has been answered before, I searched long and hard before deciding to make a thread. I'm trying to include multiple variables in a non-parametric analysis (hah!). So far what I've managed to figure out is that the NPMC package from CRAN MIGHT be able to do what I need, but I can't get it to. First I created a dataset as NPMC calls for. > Ind=Individual

Hi there, I am having a problem with package npmc. If I use the included datasets (brain, kronen) everything is fine, but if I try and use my dataset I receive an error message: Error in probval.GenzBretz(algorithm, n, df, lower, upper, infin, corr, : NAs in foreign function call (arg 2) In addition: Warning message: In probval.GenzBretz(algorithm, n, df, lower, upper, infin, corr, : NAs

Hi, I'm having trouble getting the function `npmc' working. I have a 2 column 30 row table called `mydata': > npmc(mydata, df=2, alpha=0.05) Error in sort(unique.default(x), na.last = TRUE) : `x' must be atomic > is.atomic(mydata) [1] TRUE Is there anything I have overlooked? Am I invoking the function correctly? I wasn't able to find any hints in the archives

Hi Does anyone know where is the package: npmc (Nonparametric Multiple Comparisons). I found the reference on R Site Search, but not the package itself on CRAN as suggested. Thanks Mauricio

Hi, Please dput() your example dataset. dat1<- read.table(text="a?? a?? a?? a??? a??? b?? b??? b??? c??? c?? c??? c 2? 4??? 5?? 2??? 7??? 2?? 2??? 6??? 3??? 7?? 9??? 3 3? 3?? 4?? 1???? 6??? 8?? 1??? 3??? 5??? 2??? 6??? 3",sep="",header=FALSE,stringsAsFactors=FALSE) library(reshape) ?dat2<-melt(as.data.frame(t(dat1)),id.var="V1")[,-2]

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

Recently, I ran a series of Kruskal-Wallace tests [kruskal.test()] using by() to group by site Output is a list: >Herb.KW Herb.df$ID: 10-1 Kruskal-Wallis rank sum test data: Indicator_Rating by Year Kruskal-Wallis chi-squared = 15.24, df = 7, p-value = 0.03302 ----------------------------------------------------------------------------------------------------- Herb.df$ID: 18-1

Hello all, Here goes one of my first functions. I want to make a nonparametric multiple sample comparison with unequal sample sizes (see Zar?s Biostatistical Analysis, 3rd. Ed., pg. 201 Example 10.11, pg. 288 Example 11.10). In the real world, I want to compare samples of fish length captured with different fishing gears. After using the Kruskal-Wallis test I want to check the differences

Hi, I am trying to do a kruskal wallis test on two lists, fVisited and cSN: fVisited[[1]] [1] 0.17097623 0.30376141 0.17577266 0.14951855 0.03959753 0.08096217 0.05744888 0.02196257.... .... cSN[[1]] [1] 0.08557303 0.36477791 0.19601252 0.12981040 0.05351320 0.10385542 0.03539577 0.03106175.... So if I just want to do a test on just one of the entries this is simple enough:

Hi, ?kruskal.test() ?a<- c(2,4,5,2,7) ?b<- c(2,2,6) ?c<- c(3,7,9,3) ?kruskal.test(list(a,b,c)) # ?#? Kruskal-Wallis rank sum test # #data:? list(a, b, c) #Kruskal-Wallis chi-squared = 2.003, df = 2, p-value = 0.3673 A.K. Hi I need an expression in R to apply a kruskal.test to this data (for example). a ? a ? a ? a ? ?a ? ?b ? b ? ?b ? ?c ? ?c ? c ? ?c 2 ?4 ? ?5 ? 2 ? ?7 ? ?2 ? 2 ?

Hi everyone, I am working on analyzing spectra which requires hundreds of kruskal wallis tests to be performed. Once significant results are found post tests are performed ect. My questions are: how do I perform all these kw tests and save just the pvalues in a table? Is there anyway to have R automatically perform post tests if a result of p<0.05 is found? Currently when I run the test

I am interested in using "multcompLetters" after running "kruskalmc" but I'm a newbie and I'm not having luck figuring it out. I can run "kruskalmc" just fine, but after studying the documentation for "multcompletters" for a long time, I cannot figure out how to make it work. Any ideas? R input is below > >> #here is my data set >>

Hi all, The Kruskal-Wallis test is a generalization of the two-sample Mann-Whitney test to *k* samples. That being the case, the Kruskal-Wallis test with *k*=2 should give an identical p-value to the Mann-Whitney test, should it not? x1<-c(1:5) x2<-c(6,8,9,11) a<-wilcox.test(x1,x2,paired=FALSE) b<-kruskal.test(list(x1,x2),paired=FALSE) a$p.value [1] 0.01587302 b$p.value [1]

I have run a kruskal.test() using the by() function, which returns a list of results like the following (subset of results): Herb.df$ID: 4-2 Kruskal-Wallis chi-squared = 18.93, df = 7, p-value = 0.00841 -------------------------------------------------------------------- Herb.df$ID: 44-1 Kruskal-Wallis chi-squared = 4.43, df = 6, p-value = 0.6187 So then, how do extract a vector

Dear Researchers, Sorry for this email but I am not a statistician, and for this I have this problem to understand. Thanks in Advance for help and suggestions. Gianni I have 21 classes (00, 01, 02, 04, ....,020) with different length. I did a kruskal wall test in R with the following code kruskal.test(m.class.l, m.class.length.lf) Kruskal-Wallis rank sum test data: m.class.l and

Hi, I have searched and found a response to a question similar to mine but when I tried the code, R says it's not an actual function so I thought I'd ask here. http://r.789695.n4.nabble.com/file/n4633035/Cookies.csv Cookies.csv I have attached the data I am using. I am trying to look at two things: how moisture content changes over time, and how it changes along the length of a log

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no