similar to: Help in error removal

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

Regarding "financial" data: I have a high frequency (1 minute) measure of supply/demand and I'd like to know if it has any influence on short term price changes (also 1 minute). Question: How do I calculate the correlation between this supply/demand measure and price changes (correctly)? Some facts about that data: The price changes and supply/demand measure are non-normal. An

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): >

Dear R-users: I am runing R 1.6.2 with Windows XP. I try to calculate Pearson correlation and Spearman correlation of any pairwise columns of 8000 x 80 data matrix with missing values and randomize the matix 1000 times and calculate this two correlations again. The code bellow for Pearson is working fine but for Spearman got the error bellow for randomized data matrix and R console is stop

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',

Dear all, Coercing a logical vector to a numeric one is easy. The as.numeric function is used. However what do we use when we have a matrix or an array? Sumona

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix

Hi all, Is there a convenient way to calculate Spearman correlation coefficients in R? Nick Nianqing Xiao, Ph.D NCI Center for Bioinformatics, NIH SAIC/Advanced Systems Group > 6116 EXECUTIVE BLVD 4026J > MSC 8335 > BETHESDA MD 20852 Phone: 301-451-6357 Fax: 301-480-4222 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi there, I have a multiple for loop over a list of data frames for ( i in 1:(N-1) ) { for ( j in (i+1):N ) { for ( p in 1:M ) { v_i[p] = alist[[p]][i,"v"] v_j[p] = alist[[p]][j,"v"] } rho_s = cor(v_i, v_j, method = "spearman") rho_p = cor(v_i, v_j, method = "pearson"

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

Dear R-helpers, I would like to do a Spearman rank order test, and used the cor() function with the method "spearman". It gives me a number (correlation coefficient?) , but how can I get the p-value? Thank you for the help in advance! Regards, Anne-Katrin -- [[alternative HTML version deleted]]

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

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"))

Hi The cor function in the stats package calculates the correlation between columns of data, does anyone know if it is at all possible to calculate the correlation between rows instead ? Or is there an appropriate package or function that is more appropriate I'd like to calculate spearman & pearson correlations between rows. Many thanks Jason -- --------------------------------

I generated a heatmap in R using the following commands: > mydata <- read.csv(file="Data.csv", header=TRUE, sep=",") > mydata <- mydata[rowSums(mydata[,-1]^2) >0, ] > rownames(mydata)=mydata$Name > mydata <- mydata[,2:253] > mydatamatrix <- data.matrix(mydata) > mydatascale <- t(scale(t(mydatamatrix))) > hr <-

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

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

Hi, I am using R 1.8.1 on WinXP. I encounter a problem when trying to compute a Spearman correlation under certain conditions (at least I think there is a problem, but maybe this is the normal behavior). > X<-array(0,c(20,2)) > > X[,1]<-c(runif(10),rep(NA,10)) > X[,2]<-c(runif(10),rep(NA,10)) > > Y<-X[1:10,] > >

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 generated a heatmap in R using the following commands: > mydata <- read.csv(file="Data.csv", header=TRUE, sep=",") > mydata <- mydata[rowSums(mydata[,-1]^2) >0, ] > rownames(mydata)=mydata$Name > mydata <- mydata[,2:253] > mydatamatrix <- data.matrix(mydata) > mydatascale <- t(scale(t(mydatamatrix))) > hr <-