similar to: how to see inbuilt function(cor.test) & how to get p-value from t-value(test of significance) ?

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

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value

Hi, I am not sure which kind of test is applied to the data if you use cor.test(x, y) ? Is it an unpaired t-Test? Regards -- View this message in context: http://www.nabble.com/cor.test%28x%2Cy%29-tp22492993p22492993.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I'm not quite sure if this is a feature or a bug or if I just fail to understand the documentation: If I use cor() with pairwise.complete.obs and method=pearson, the result is a scalar: ->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson") [1] 0.9819805 The documentation says that " '"pairwise.complete.obs"' only

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

I can't hardly accept the result of cor function with pairwize.colplete.obs or complete.obs insert print statements in cor function, + if (method != "pearson") { + Rank <- function(u) if (is.matrix(u)) + apply(u, 2, rank, na.last = "keep") + else rank(u, na.last = "keep") + x <- Rank(x) +

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

Full_Name: Paul Bailey Version: 2.1.1 OS: OS X 10.3 Submission from: (NULL) (68.252.250.144) ?cor [tells me that it has a na.rm variable] > cor(frame2[1,],frame2[2,],na.rm=T) Error in cor(frame2[1, ], frame2[2, ], na.rm = T) : unused argument(s) (na.rm ...) hmm.

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 everybody, I would like to apply cor.test to a matrix with m rows and n columns and get the results in a list of matrices , one matrix for p.val, one for the statistic, one for the correlation and 2 for upper and lower confidence intervals, something analog with cor() applied to a matrix. I have done my own function to get a matrix of p.values and i suppose i can build similar functions for

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,

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

Hi, How can I make the cor(x, y, method="spearman") call to produce an error when the input to it (x, y) produces an error? Here is a simple example: > a <- c(0, 1, 2) > b <- c(100, 2, 4) ## error: > log(a) [1] -Inf 0.0000000 0.6931472 ## error, as expected: > cor(log(a), log(b), method="pearson") [1] NaN ## not an error any more (not expected): >

Hi, all. Yet another beginner to R : ) I wonder, how it's possible to get the value of a coefficient from the object produced by cor.test() ? > cor.test(a, b, method="spearman") Spearman's rank correlation rho data: a and b S = 21554.28, p-value = 2.496e-11 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.6807955 Warning message: In

I posted this on R help, and a few others responded indicating they too were able to replicate the error as a function of missing data. I believe this should not be the case and hence and reporting it here. ### Code provided on R-Help by Ivar Herfindal # Simulate data testdata <- cbind.data.frame(gr=3Drep(letters[1:4], each=3D5), = aa=3Drnorm(20), bb=3Drnorm(20)) # Introduce some missingness

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,

Arrrrr, I think I've found a bug in the behavior of the stats::cor function when NAs are present, but in case I'm missing something, could you look over this example and let me know what you think: > a = c(1,3,NA,1,2) > b = c(1,2,1,1,4) > cor(a,b,method="spearman", use="complete.obs") [1] 0.8164966 > cor(a,b,method="spearman",

Pardon my ignorance, but is there a difference in cor.test between exact=FALSE and exact=NULL when method=spearman? Take for example: x<-c(1,2,2,3,4,5) y<-c(1,2,2,10,11,12) cor.test(x,y, method="spearman", exact=NULL) This gives an error message, Warning message: Cannot compute exact p-values with ties in: cor.test.default(x, y, method = "spearman", exact = NULL)

Hello, Is there a way to extract output objects from a stats test without viewing the entire output? I am trying to do so in the following: define a vector of length j for( i in 1: length (vector)) { vector[i] = cor.test (datavector1, datavector2[i], method=("spearman")) } I would like the reported Spearman's rho to be saved in a vector. I have tried a few different ways of

Full_Name: Ian Wilson Version: R-1.8.0 OS: Windows (but own compilation) Submission from: (NULL) (139.133.7.38) the p-value is incorrect for cor.test using method "spearman" in R-1.8.0. This was not the case in R-1.7.1. Version R-1.8.0 on Windows > cor.test(rnorm(50),rnorm(50),method="spearman") Spearman's rank correlation rho data: rnorm(50) and rnorm(50) S