similar to: multiple operations on indexed columns

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

Full_Name: Dirk Koschuetzki Version: 2.1.1 OS: source code Submission from: (NULL) (194.94.136.34) Hello, >From the source code (R-2.1.1, file: .../R-2.1.1/src/library/stats/R/) ****************************** cor.test.default <- function(x, y, alternative = c("two.sided", "less", "greater"), method = c("pearson", "kendall",

Dear R developers, Currently many (all?) test functions in R describe the alternative hypothesis, but not the the null hypothesis being tested. For example, cor.test: > require(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="kendall")) Kendall's rank correlation tau data: mpg and wt z = -5.7981, p-value = 0.000000006706 alternative hypothesis: true tau is not

Dear R maintainers, R is great. Now that I have that out of the way, I believe I have encountered a bug, or at least an inconsistency, in how Spearman and Kendall rank correlations are handled. Specifically, cor() and cor.test() do not produce the same answer when the data contain NAs. cor() treats the NAs as data, while cor.test() eliminates them. The option use="complete.obs" has

Hi all, I would like to ask a question related to Kendall package. I ran Kendall (x,y) and saw the results. But I am not sure which tau values R reported. I have ties in my data set, so I want tau-b. Can anybody tell how Kendall package is calculating tau values? I have looked at the package PDF, but I could not find any useful information. As long as I see from the following link, there

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

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

Hi all, I am trying to write a script that will compute Kendall's tau for a 75 time series (using the Kendall package) and will then write the tau and p values from the Kendall test to a text file table that can be read into Excel. I am having no problem calculating Kendall's tau and the associated p value for each time series, but I am having trouble figuring out how aggregate the

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

> library(Kendall) > Kendall(1:3,1:3) WARNING: Error exit, tauk2. IFAULT = 12 <<<<<< tau = 1, 2-sided pvalue =1 I believe Kendall tau is well-defined for this case and the reported value is correct; isn't it a bug to give a warning? (And if, e.g., the pvalue is not well-defined in this case, wouldn't it be better to return NA or NaN or something?) Also,

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

Hello, I wuold like to propose my modifications of the original cor.test to you : I tried to calcolate the correct p-value for Spearman and Kendall's test with ties. Let me know what you think. Thanks you for your time. Antonietta di Salvatore test <- function(x, ...) UseMethod("test") test.default <- function(x, y, alternative = c("two.sided",

hi, i have 2 lists of ranks for which i'd like to compute kendall tau. there are ties in the ranks which (to the best of my knowledge) means i cant use tau a but rather b or c. how does R handle that? are ties automatically detected (using corr.test()) and is tau b/c computed instead of tau a? also kendall does not work when values in list 1 do not occur in list 2 (and vice versa) - how does

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

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)

R: I got a warning message when running the cor.test function using both Spearman and Kendall rank correlations saying that the p-value may be incorrect due to ties in the data. My data has 35 obs and one series has 6 pairs of ties. Does anyone know if this would likely have a great effect on the p-values calculated.. The values look good; tau = -0.68 with p-value = 8e-9 and rho = =0.84

Hi guys, I have a large number of columns of a dataframe that I want to apply a common factorization to; the columns are all numeric and the factorization collapses some of these values into common groups (labels). How can I do this systematically? Is there an analog to sapply that can pass columns as vectors to FUN? Thanks in advance, Gregory Gentlemen ---------------------------------

Dear expeRts, I would like to create a scatter plot matrix with splom(). The lower panel should contain some additional information about the samples shown in the upper panel plot, see the splom() call below. Now two questions came up: (1) The lower panels show "tau" and "alpha" on top of each other. How can I plot *three* expressions on top of each other? I tried several

Hi, I am running a correlation analysis on a temporal dataset. I was wondering if you would receive the same tau and p values running the function: MannKendall(x), where x is the dependant variable that changes with time as you would running: Kendall(d,x), where x is the exact same dataset as the x entered into MannKendall and d is the date on which the observation was made (assuming that

Im trying to ascertain whether or not the facilities of R are sufficient for solving an optimization problem I've come accross. Because of my limited experience with R, I would greatly appreciate some feedback from more frequent users. The problem can be delineated as such: A utility function, we shall call g is a function of x, n ... g(x,n). g has the properties: n > 0, x lies on the