similar to: kendall's w

Hello, I'm trying to calculate a chi-squared test to see if my data are different from the theoretical distribution or not: chisq.test(rbind(c(79, 52, 69, 71, 82, 87, 95, 74, 55, 78, 49, 60),c(80,80,80, 80, 80, 80, 80, 80, 80, 80, 80, 80))) Pearson's Chi-squared test data: rbind(c(79, 52, 69, 71, 82, 87, 95, 74, 55, 78, 49, 60), c(80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80,

Hi, I need to calculate Kendall's tau for large data vectors (length > 100'000). Is somebody aware of a faster algorithm or package function than "cor(, method="kendall")"? There are ties in the data to be considered (Kendall's tau-b). Any suggestions? Regards Ferdinand

Hello, I can't find a package which calculates Kendall's tau-c. There is the package Kendall, but it only calcuates Kendall's tau-b. Here is the example from ttp://www2.chass.ncsu.edu/garson/pa765/assocordinal.htm. cityriots <- data.frame(citysize=c(1,1,2,2,3,3), riotsize=c(1,2,1,2,1,2), weight=c(4,2,2,3,0,4)) cityriots <- data.frame(lapply(cityriots,function(x)

I discovered that the Kendall's tau calculation in R uses all pairwise comparisons which is O(n^2) and takes a long time for large vectors. I implemented a O(n*log(n)) algorithm based on merge-sort. Is this of interest to be included in core R? The code (fortran and R wrapper) is available in my package clinfun v0.9.7 (not exported in NAMESPACE). Thanks, Venkat -- Venkatraman E. Seshan,

Full_Name: Marek Ancukiewicz Version: 2.10.1 OS: Linux Submission from: (NULL) (74.0.49.2) Both cor() and cor.test() incorrectly handle ordered variables with method="kendall", cor() incorrectly handles ordered variables for method="spearman" (method="person" always works correctly, while method="spearman" works for cor.test, but not for cor()). In

I have 3 variables relating to the successful introductions of species to 95 different areas: introduction frequency; number of successes pre 1906; number of successes post 1906 The data are not normal, nor homo-skedatic, so I am using non-parametric statistics. I have calculated Kendall's tau between both introduction & successes pre 1906 (tau=0.3903) and introduction & successes

I've noticed that the implementation of Kendall's Tau in R is O(N^2). The following reference describes how it can be done in O(N log N): A Computer Method for Calculating Kendall's Tau with Ungrouped Data William R. Knight Journal of the American Statistical Association, Vol. 61, No. 314, Part 1 (Jun., 1966), pp. 436-439 http://www.jstor.org/pss/2282833 I'm interested in

Hi all I have a large dataframe with (among others) a categorical variable of 52 levels and would like to create a barplot with the bars ordered in decreasing frequency of the levels. I belive it is referred to as a pareto plot. Consider a subset where I keep only the categorical variable in question. # Example: v1 = c("aa", "cc", "bb", "bb",

Hello. Has any further action been taken regarding implementing David Simcha's fast Kendall tau code (now found in the package pcaPP as cor.fk) into R-base? It is literally hundreds of times faster, although I am uncertain as to whether he wrote code for testing the significance of the parameter. The last mention I have seen of this was in 2010

Hi, I am a new user of R. I have 2 series of nominal data (2 series of answers for the same question) and I want to calculate the correlation between these 2 series. I've tried to use the correlation function (corr, ...) but all are for numeric data... Does anyone know what function should I use for the nominal data? Thanks a lot, Bianca

Dear Contributors, thanks for collaboration. I am trying to reorganize data frame, that looks like this: n1.Index Date PX_LAST n2.Index Date.1 PX_LAST.1 n3.Index Date.2 PX_LAST.2 1 NA 04/02/07 1.34 NA 04/02/07 1.36 NA 04/02/07 1.33 2 NA 04/09/07 1.34 NA 04/09/07

Colleagues, I ran some nonparametric regressions in R (run in RedHat Linux), then a colleague repeated the analyses in SAS. When we obtained different results, I tested S-Plus (same Linux box). And, got yet different results. I replicated the results with a small dataset: DATA: 37.5 23 37.5 13 25 16 25 12 100 15 12.5 19 50 20 100 13 100 10 100 10 100 16 50 10 87.5

Hi there, > > I am using the function pgls in the CAPER package, everything seems to run > fine except for one of my variables. > When using this variable as continuous the anova works without problem, > but if using the same variable as factor (in fact, this is what it is), the > program returns this answer: > > Error in terms.formula(formula, data = data) : >

Full_Name: Dan Field Version: 1.6.2 OS: N/A Submission from: (NULL) (209.115.168.187) In kendall.c (library is ctest), the limits for the first loop in routine kendall_tau run from 0 through n-1, and the inner loop runs from 0 through i-1. This causes the each pair at index i to be compared with itself; my understanding is that there should only be n*(n-1)/2 pairs under consideration for

A search of the archives did not reveal an answer: For basic tests of association, where one has no a priori knowledge of the form of the relation or of the distributions of the variables, rank correlation seems like a good start. Why is cor.test() with Kendall and Spearman options relegated to the ctest package, rather than in the base package? Does this suggest that the developers consider

Hi, R! Question1: I am trying to correlate two vectors of numbers (two columns of microarray signal values) by using the non-parametric Spearman's rank correlation coefficient rho: > cor.test(V2.Signal,V3.Signal,method="spearman") but I get the error message: Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate

> From: Siegfried.Macho > > Dear R-helpers, > > 3 questions: > 1. Is there a package that contains a routine for computing > Kendall's W > (coefficient of concordance), with and without ties ? Is that the same as Kendall's tau, as in cor(..., method="kendall")? > 2. Is there a package that contains a routine for computing > Goodman' s

Dear R users I want to compute Kendall's Tau between two vectors x and y. But x and y may have zeros in the same position(s) and I wrote the following function to be sure to drop out those "double zeros" "cor.kendall" <- function(x,y) { nox <- c() noy <- c() # for (i in 1:length(x)) if (x[i]!= 0 | y[i] != 0) nox[length(nox)+1]<- x[i] for (i in

Hello, I thougth that there is a function which does the kendall test in R, I writed on the console apropos("kendall") and I didn't found anything can you tell me how could I do to use the kendall test? Thanks. _____________________________________________________________________________ l [[alternative HTML version deleted]]