similar to: R's Spearman

[[this bounced first, because it has 'help' in the Subject line ... -- Martin Maechler ]] I am a complete novice R programmer. (Though I know C quite well) I am trying to write some R code to do the following simulation. There is a 2-frame "movie" of noise and signal dots. the noise dots have random positions in each frame. The signal dots are placed randomly in frame 1,

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

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))

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:

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 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,

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,

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

I have a data set where I want the correlations between 2 variables conditional on a students grade level. This code works just fine. by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete', method='pearson') However, this generates an error by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete',

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 =

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

* Kill the A8+A8 hack. Recent enough X servers (>=1.7) fall back to ARGB glyphs for drivers not supporting A8 render targets. * Kill all the global state. It doesn't matter a lot yet but it might if we get multicard working at some point. * Other random clean-ups with no functional changes. Some numbers from x11perf -aa10text -aa24text -comppixwin10 -comppixwin500: * Before, with A

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]]

If anyone has a few extra CPU cycles to spare, I'd appreciate it if you could verify a problem that I have encountered. Run the code below and tell me if it crashes your R before completion. library(lme4) data(bdf) dump<-sapply( 1:50000, function(i) { fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf, random = ~ IQ.ver.cen | schoolNR); cat("

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

Hello, Does anyone know if the cor function, when method = "spearman", returns a correlation coefficient corrected for any ties in the ranks of the data? I have data with quite a few ties and am thinking that I should use a calculation of the coefficient corrected for ties, but before I try and code this calculation myself, I thought I should check whether or not cor() automatically

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.

Dear R, I'm looking for exact p-values for Spearman's rank correlation in the presence of ties. This is available in StatXact and SPSS, but I haven't yet found it in R. Has anyone implemented this? -- Paul H Artes Assist Prof, Ophth Vis Sci Dalhousie University, QEII Eye Care Centre Halifax, NS, CANADA

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 =