similar to: spearman rank correlation problem

Hi. I am currently trying to run some Spearman correlations, and have encountered two issues. 1) When using cor.test() with a variable that includes ties, I get the "Cannot compute exact p-values with ties" error. I have read that this function now uses an asymptotic formula that allows for ties, so do not understand why I am getting this error. (I am running version 2.4.0.) I

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, I am not really familiar with Correlation foundations, although I read a lot. So maybe if someone kindly help me to interpret the following results. I had the following R commands: correlation <-cor( vector_CitationProximity , vector_Impact, method = "spearman", use="na.or.complete") cor_test<-cor.test(vector_CitationProximity, vector_Impact,

cor.test and SuppDists give me different P-values for the same Spearman's rho. Which is correct, or am I doing something wrong? > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) > cor.test(x,y,method="spearman") Spearman's rank correlation rho data: x and y S = 48, p-value =

In the ouput below, what is the "S" statistic (S = 96) that is used for Spearman? I don't have easy access to the books cited on the help page. Other texts and web sources that I have found use t or z as a test for Spearman, perhaps inappropriately. Can anyone tell me how S is computed or refer to a web resource? I see from the code for that: q <- as.integer((n^3 - n) * (1

Full_Name: David Simcha Version: 2.10 OS: Windows XP Home Submission from: (NULL) (173.3.208.5) > a <- c(1:10) > b <- c(1:10) > cor.test(a, b, method = "spearman", alternative = "greater", exact = TRUE) Spearman's rank correlation rho data: a and b S = 0, p-value < 2.2e-16 alternative hypothesis: true rho is greater than 0 sample estimates:

Dear HelpeRs, I have some data: "ice" <- structure(c(0.386, 0.374, 0.393, 0.425, 0.406, 0.344, 0.327, 0.288, 0.269, 0.256, 0.286, 0.298, 0.329, 0.318, 0.381, 0.381, 0.47, 0.443, 0.386, 0.342, 0.319, 0.307, 0.284, 0.326, 0.309, 0.359, 0.376, 0.416, 0.437, 0.548, 41, 56, 63, 68, 69, 65, 61, 47, 32, 24, 28, 26, 32, 40, 55, 63, 72, 72, 67, 60, 44, 40, 32, 27, 28, 33,

Greetings. There seems to be a problem with the P-value computation in the cor.test with method="spearman". In R1.8.0 (MS Windows) I seem to be getting intermittently nonsense P-values, but the rho's are OK. I can get this reproducibly with the toy example attached where the first use is OK and subsequent calls with the same data give nonsense. (I have also seen the problem

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 R developers: There is a possible bug in calculating the p-value for Spearman's rank correlation. Line 155 in file R-patched/src/library/stats/R/cor.test.R is as.double(round(q) + lower.tail), I think, it should be as.double(round(q) + 2*lower.tail), The reason is that round(q) is expected to be an even number (the S statistic), so the next feasible value is round(q)+2.

Hi, I have just encountered a strange behaviour from 'cor' with regards to the treatment of NAs when calculating Spearman correlations. I guess it is a subtle bug. If I understand the help page correctly, the two modes 'complete.obs' and 'pairwise.complete.obs' specify how to deal with correlation coefficients when calculating a correlation _matrix_. When calculating

Hallo! Is there an error in cor to calculate Spearman correlation with cor if there are NA's? cor.test gives the correct result. At least there is a difference. Or am I doing something wrong??? Does anybody know something about this? a<-c(2,4,3,NA) b<-c(4,1,2,3) cor(a, b, method="spearman", use="complete.obs") # -0.9819805 cor.test(a, b,

vobolonkin at lic.co.nz wrote: > Full_Name: Vladimir Obolonkin > Version: tested in 2.0 to 2.4.1 > OS: linux, win, mac > Submission from: (NULL) (202.14.96.194) > > {{ Subject shortened manually -- to pass anti-spam filters > > Original-subject: Incorrect matrix of spearman correlations working \ > large (24000 by 425 and 78 by 425 data frames) in 64-bit Linux

Full_Name: Vladimir Obolonkin Version: tested in 2.0 to 2.4.1 OS: linux, win, mac Submission from: (NULL) (202.14.96.194) {{ Subject shortened manually -- to pass anti-spam filters Original-subject: Incorrect matrix of spearman correlations working \ large (24000 by 425 and 78 by 425 data frames) in 64-bit Linux machines;\ the same code gives correct results in 32-bits Win and

Hi, I'm currently trying to find/define a relationship between one dependent and several independant variables. The problem is that i cannot use the normal multiple regression/correlation in Spss because the data is not normal distributed. i calculated the spearman roh and Kendalls tau Correlation and also some partial correlations in R. Now i wanna find out the the multiple correlation

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 =

Hi, A following function was kindly provided by GGally?s maintainer, Barret Schloerke. function(data, mapping, ...) { p <- ggplot(data = data, mapping = mapping) + geom_point(color = I("blue")) + geom_smooth(method = "lm", color = I("black"), ...) + theme_blank() + theme(panel.border=element_rect(fill=NA, linetype =

Hi all, I am trying to figure out the formula used by R's Spearman rho (using cor(method="spearman")) because I can't seem to get the same value as by calculating "by hand". Perhaps I'm using "cor" wrong, but I don't know where. Basically, I am running these commands: > y=read.table(file="tmp",header=TRUE,sep="\t") >

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

Dear all, I tried help.search("karber") and RSiteSearch("karber") and RSiteSearch("*karber*") to find whether the (trimmed) Spearman-Karber method for LD50 evaluation in toxicity data (e.g. according to Hamilton 1977) has been implemented in R. Or does this method feature under a different name? Of course logit and probit are doable, but Spearman-Karber seems to