similar to: Kendall rank correlation

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",

Hello R gurus, I want to calculate the Spearman rho between two ranked lists. I am getting results with cor.test that differ in comparison to my own spearman function: > my.spearman function(l1, l2) { if(length(l1) != length(l2)) stop("lists must have same length") r1 <- rank(l1) r2 <- rank(l2) dsq <- sapply(r1-r2,function(x) x^2) 1 - ((6 * sum(dsq))

hello help, i''ve searched through the manual pages and the only reference i can find to spearman rank correlation is cor.test, which only seems to give the significance value of the correlation. is there any way to get the actual value of rho? david. [[alternate HTML version deleted]]

Hello, I would like to do a PCA with dudi.pca or PCA, but also with the use of Spearman or Kendall correlations Is it possible ? Otherwise, how can I do, according to you ? Thanking you in advance Eric Bourgade RTE France [[alternative HTML version deleted]]

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

Dear all, Suppose my data frame is as follows: id price distance 1 2 4 1 3 5 ... 2 4 8 2 5 9 ... n 3 7 n 8 9 I would like to calculate the rank-order correlation between price and distance for each id. cor(price,distance,method = "spearman") calculate a correlation for all. Then I tried to use apply(data,list='id',cor(price , distance , method =

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 If I get a p-value less than 0.05 does that mean there is a significant relation between the 2 ranked lists? Sometimes I get a low correlation such as 0.3 or even 0.2 and the p-value is so low , such as 0.000001 , does that mean it is significant also? and would that be interpreted as significant low positive correlation or significant moderate positive correlation? Also,can R calculate the

Hi, I have the following two measurements stored in mat: > print(mat) [,1] [,2] [1,] -14.80976 -265.786 [2,] -14.92417 -54.724 [3,] -13.92087 -58.912 [4,] -9.11503 -115.580 [5,] -17.05970 -278.749 [6,] -25.23313 -219.513 [7,] -19.62465 -497.873 [8,] -13.92087 -659.486 [9,] -14.24629 -131.680 [10,] -20.81758 -604.961 [11,] -15.32194 -18.735 To calculate the ranking

Dear all, in package Hmisc `rcorr' computes a matrix of Spearman's `rho' rank correlation coefficients for all possible pairs of columns of a matrix. What if I want a matrix of rank correlation coefficients for pair of columns of two different matrices? I have the same question about distance metrics in package Vegan. The function 'vegdist' computes distance indexes for all

Hallo, does anybody know if there is an implementation of the Spearman rank correlation in R that gives a correct (or at least 'safe') p-value in the case of ties?? I have browsed the R-help archives but I found nothing. Thanks a lot in advance for any help, Antonino Casile

Full_Name: Petr Savicky Version: 2.7.2, 2.8.1, 2.9.0 OS: Linux Submission from: (NULL) (147.231.6.9) The p-value of Spearman's rank correlation test is calculated in cor.test(x, y, method="spearman") using algorithm AS 89. However, the way how AS 89 is used incures error, which may be an order of magnitude larger than the error of the original algorithm. The paper, which

Hi R-Masters I need use a sequential approach in serie of cases, but may data is not normal. If data is normal distribution is very easy create analysis using likelihood ratio like of Wald test. But in my case I need use a non-parametric test (Mann-Whitney). I was use: RSiteSearch("sequential rank test") but not solve my problem. Do you know routine or package implement

Hi All: help(cor.test) claims For Spearman's test, p-values are computed using algorithm AS 89. Algorithm AS 89 was introduced by the paper D. J. Best & D. E. Roberts (1975), Algorithm AS 89: The Upper Tail Probabilities of Spearman's rho. Applied Statistics, Vol. 24, No. 3, 377-379. Table 1(a) in this paper presents maximum absolute error |\Delta_m|, of the approximation for

Hi All, a colleague asked me if R has a function producing a Partial Rank Correlation Coefficient, sensu Blower and Dowlatabadi 1994 [1]. I personally would not have a clue, and I could not find something like that on the search page... although I would not be surprised if it's there under a different name. Incidentally, unfortunately the function, assuming it exists, is to be fed to some

I"ve got a data frame with a selection of columns I want to compute a rank-order correlation matrix from without disturbing the original data frame. foo[,c("a","b","d","f","g")] What I wanted to do, intuitively, was: > cor(rank(foo[,c("a","b","d","f","g")])) but rank in that context

> 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,

Hello my big problem is, i can´t find the phi-correlation instruction in the R - programm. (correlation method= spearman, pearson, kendall, I have found) I also cant find the transform instruction which I can transform rational vector into nominal vectors (binary) Transforming into ordinaI I have found with the “rank” instruction, but I have no found into nominal dates. Please help

Hi, I have two ranks of labels (strings) representing user preferences of colors. For instance, here is a simple example with 4 preferences for each user: > rank1 [1] "red" "blue" "green" "black" > rank2 [1] "white" "gray" "black" "blue" How can I compute Kendall's Tau for this scenario? Thanks in

Dear R-helpers, I'm fighting with the following problem : I want to do bootstrapping on a Kendall correlation with the following code : > cor.function <- function(data,i) cor(data[i, 1], data[i, 2],method="kendall") > boot.ci <- boot.ci(boot.cor <- boot(cbind(x,y),cor.function, R=1000),conf=c(0.95,0.99)) However, I've got problems because I've got ties