similar to: calculating correlation of a Supply/Demand measure and price change (in high frequency time series data)

Dear all, I am new to programming in R. I deal with microarray data,which is a data frame object type. I need to carry out a few statistical procedures on this, one of them being the pearson corelation. I need to do this between each row which is a gene. So the desired result is a square matrix with the pearson corelation value between each row. So the first column would be (1,1)=0,(1,2),(1,3)

I would like to create a new dataframe from the DateTime column of an existing dataframe and a numeric vector. When I do cbind(x[,1], y) the result is: [1,] 1199370600 12.500 [2,] 1199371200 69.375 [3,] 1199371800 23.750 where the first column you see used to look like: "2008-01-03 08:30:00 Central Standard Time" "2008-01-03 08:40:00 Central Standard Time"

Hello, This code works fine but is so fast I can't see anything but the last plot. for (i in nrow(X)){ plot(as.numeric(d[i,])) } I'd like to view a plot every 500 milliseconds, nrow(X) = 400. How? Thanks -- View this message in context: http://www.nabble.com/Plot-timer-in-a-for-loop-tp17741975p17741975.html Sent from the R help mailing list archive at Nabble.com.

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

Full_Name: Marek Ancukiewicz Version: 2.10.1 OS: Linux Submission from: (NULL) (74.0.49.2) Both cor() and cor.test() incorrectly handle ordered variables with method="kendall", cor() incorrectly handles ordered variables for method="spearman" (method="person" always works correctly, while method="spearman" works for cor.test, but not for cor()). In

A search of the archives did not reveal an answer: For basic tests of association, where one has no a priori knowledge of the form of the relation or of the distributions of the variables, rank correlation seems like a good start. Why is cor.test() with Kendall and Spearman options relegated to the ctest package, rather than in the base package? Does this suggest that the developers consider

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

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 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.

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'm sure this is a simple task, but how to do it has escaped me. I have imported data from two separate files (each file contains the results from an information retrieval algorithm) organized into a list. They are organized by File,Query, and Rank (in that order): [[1]] Doc Query Rank 5 1 1 9 1 2 7 1 3 5 2 1 7 2 2 9 2 3 [[2]]

Dear SIr, In case of looking at the codes of the fuction, "cov", we find all the codings below. But, incase of "mean", we don't find the contents. Please show me the way to look at all the codings of any functions. Best regards, Kei ----------------------- > cov function (x, y = NULL, use = "all.obs", method = c("pearson",

Hi Is there a function for computing the standard correlation coefficient (not pearson) in R? Thanks Mick

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

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, R! Question1: I am trying to correlate two vectors of numbers (two columns of microarray signal values) by using the non-parametric Spearman's rank correlation coefficient rho: > cor.test(V2.Signal,V3.Signal,method="spearman") but I get the error message: Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value

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. Has any further action been taken regarding implementing David Simcha's fast Kendall tau code (now found in the package pcaPP as cor.fk) into R-base? It is literally hundreds of times faster, although I am uncertain as to whether he wrote code for testing the significance of the parameter. The last mention I have seen of this was in 2010

Hi, I wanted to know how to compute the correlation ratio (eta) between two variables using R. Is there any function to compute the correlation ratio. Any help will be very much appreciated. Thanks, Suman [[alternative HTML version deleted]]