similar to: cor vs cor.test

Hi r lovers! I 'd like to apply the cor() function to a matrix which have some missing values As a matter of fact and quite logically indeed it doesn't work Is there a trick to replace the missing value by the mean of each variable or by any other relevant figures ? Or should I apply a special derivate of the cor() function, (I don't have any idea if it exists and have some trouble to

Friends I am new to R (and statistics) so am struggling a bit. Briefly... I am interested in getting the P-Value from cor(X) where X is a matrix. I have found cor.test. Verbosely... I have 4 vectors and can generate the corellation matrix... > cor(cbind(X1, X2, X3, X4)) X1 X2 X3 X4 X1 1.00000000 -0.06190365 -0.156972795 0.182547517 X2

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 a dataset with four categories of data, the number of samples are not the same in each category. I want to find the Spearaman's Rho. Let me give an example. x=(14.22770439,26.49420624,46.7277932,19.02550707,23.37379361,16.97789862,19.77100085,23.11270162,13.72929843,33.54430621,14.4756979,70.15811106,11.22789833,NA,NA,NA)

i can see source code of function > cor function (x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman")) { na.method <- pmatch(use, c("all.obs", "complete.obs", "pairwise.complete.obs", "everything", "na.or.complete"))

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) +

Hi, For computing correlation among variables in a matrix, I use cor( ), but for computing the p-values I'm using cor.test in the following way: cor.p <- function(X) { res <- matrix(0, ncol(X), ncol(X)) for (i in 1:ncol(X)) for (j in 1:ncol(X)) res[i, j]<- cor.test(X[, i], X[, j])$p.value rownames(res) <- colnames(res) <- colnames(X) res } I'm just wondering if there is a

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000

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",

Dear R users, I have not found anything on this in the archives. Does anyone know whehther the parameter use= is not functioning in cor or enlighten me what it is supposed to do? My R version is "R version 1.8.1, 2003-11-21" on Windows 2000. I am hoping to be able to update to 1.9.1 as soon as it has appeared (we are not allowed here to install software on our own and thus I am trying

Hello to all of you, R-expeRts! I am trying to compute the cor.test for a matrix that i labelled mydata according to mydata=read.csv... then I converted my csv file into a matrix with the mydata=as.matrix(mydata) NOW, I need to get the p-values from the correlations... I can successfully get the spearman's correlation matrix with: cor(mydata, method="s",

Full_Name: Marek Ancukiewicz Version: 1.8.1 OS: Linux Submission from: (NULL) (132.183.12.87) Function cor() incorrectly handles missing observation with method="spearman": > x <- c(1,2,3,NA,5,6) > y <- c(4,NA,2,5,1,3) > cor(x,y,use="complete.obs",method="s") [1] -0.1428571 >

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

Hola, tengo una serie de datos datExpr, al usar cor() : cor(datExpr ,method = "pearson", use ="pairwise.complete.obs") me da el siguiente error allocMatrix: too many elements specified Trate con "complete.obs", "na.or.complete", y el resto de las opciones para "use", pero siempre me da algun error.  ¿Alguna idea de como puedo hacer que cor() lea

Hi, Is there a limit on the number of observations for using cor.test. For example, > library(ctest) > cor.test(rnorm(3000), rnorm(3000), method="spearman") Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value where logical needed In addition: Warning message: NAs introduced by coercion I mainly want to calculate

Hello, I'm trying to do cor(x1,x2) and I get the following error: Error in cor.default(x1, x2) : missing observations in cov/cor A few things: 1. I've used cor() many times and have never encountered this error. 2. length(x1) = length(x2) 3. is.numeric(x1) = is.numeric(x2) = TRUE 4. which(is.na(x1)) = which(is.na(x2)) = integer(0) {the same goes for is.nan()} 5. I also try

Has anyone successfully created a PNG file for a dendrogram? I am able to successfully launch and view a dendrogram in Quartz. However, the dendrogram is quite large (too large to read on a computer screen), so I am trying to save it to a file (1000x4000 pixels) for viewing in other apps. However, whenever I try to initiate a PNG device, I get a "need finitite 'xlim' values"

With a data frame containing some X & Y variables I can get the between set correlations with cor(X,Y): > cor(NLSY[,1:2], NLSY[3:6]) antisoc hyperact income educ math 0.043381307 -0.07581733 0.25487753 0.2876875 read -0.003735785 -0.07555683 0.09114299 0.1884101 Is there somewhere an analog of pairs(X,Y) that will produce the pairwise plots of each X against each

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)

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