similar to: Significance of correlation

Dear all, The other day I was reading this post [1] that slightly surprised me: "To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) " (BOB SAMOHYL). To me this implied that in

Hi, I am not sure how to fix the following error. LGD <- c(11.6, 12.3, 15.8, 33.1, 43.5, 51.3, 67.3, 84.9) cor (x=(file [1:47231,3:10]), y= rep (LGD, 47231), method = "pearson") Error in cor(x = (file[1:47231, 3:10]), y = rep(LGD, 47231), method = "pearson") : incompatible dimensions > sessionInfo() R version 2.13.0 (2011-04-13)

I estimated spearman's correlation coefficient using cor(). How do I test for significance? Vikas

Dear R-Help, I am trying to find a function that will give me the significance of the correlation of 2 variables (in the same dimension arrays) correcting for serial autocorrelation. How can I view the function cor.test's code? I would like to know a lot more detail about the function than written in the documentation at http://finzi.psych.upenn.edu/R/library/stats/html/cor.test.html to

Hi there, I got a piece of code for the Iris data which allows to display correlation coefficients for each Iris species in the lower panel (color coded). I would now like to add e.g. a "*" to show the significance of each correlation next to the correlation coefficient. Furthermore I would like to make a t.test between the species "setosa" and "versicolor" for

[Apologies in advance if this is too "statistics" and not enough "R".] I've got an experiment with two sets of treatments. Each subject either received all treatments from set A or all treatments from set B. I can compute the N pairwise correlations for all treatments in either set using cor(). If I take the mean of these N pairwise correlations, I see that the effects

Hi, I would like to generate a correlation matrix with a particular structure. For example, a 3n x 3n matrix : A_(nxn) aI_(nxn) bI_(nxn) aI_(nxn) A_(nxn) cI_(nxn) aI_(nxn) cI_(nxn) A_(nxn) where - A_(nxn) is a *specified* symmetric, positive definite nxn matrix. - I_(nxn) is an identity matrix of order n - a, b, c are (any) real numbers Many attempts have been unsuccessful because a

Hello, is it possible to create two uncorrelated random vectors for a given distribution. In fact, I would like to have something like the function "rnorm" or "rlogis" with the extra property that they are uncorrelated. Thanks for your help, Luba [[alternative HTML version deleted]]

Dear group, I have a matrix like the following: Name Sample1 sample2 sample3 sample4 ..... sample(n) nm1 10.5 13.5 30 31 nm2 8 11 34 29 nm3 9 10.3 27.8 35 nm(j) I want to be able to calculate correlation between all pairs of names. For example (nm1,nm2),

Dear list, has anyone written a package/function in R for computing a point- biserial resp. biserial correlation? Thanks in advance Bernd

Hi there! In my case, cor(d[1:20]) makes me a good correlation matrix. Now I'd like to have it one sided, means only the left bottom side to be printed (the others are the same) and I'd like to have * where the p-value is lower than 0.05 and ** lower than 0.01. How can I do this? And another thing: Is there a way to output that table as a latex table? Thanks, Martin -- Ihr

Hello, after thorough searching of the R help files as well as S+-help, I'm coming to the list: Is there a possibility to compute partial correlation coefficients between multiple variables (correlation between two paired samples with the "effects of all other variables partialled out")? All I seem to find are the standard Pearson correlation coefficients (with cor()) and no clue

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,

Hi, among the many (5) methods that I found in the list to do partial correlation in the following two that I had a look I am getting different t-values. Does anyone have any clues on why is that? The source code is below. Thanks. pcor3 <- function (x, test = T, p = 0.05) { nvar <- ncol(x) ndata <- nrow(x) conc <- solve(cor(x)) resid.sd <- 1/sqrt(diag(conc)) pcc <-

Hello, I used the function cor to calculate the pearson correlation coefficient between variables. However, the resulting values do not correspond to the outcome of my excel-calculations, for which I used the formula Cor(x,y)=Cov(x,y)/(SD(x)*SD(y)) So my question is: How does the function "cor" compute the pearson correlation coefficient? Thank you in advance, Ake Nauta

hello, i am a bit of a statistical neophyte and currently trying to make some sense of confidence intervals for correlation coefficients. i am using the cor.test() function. the documentation is quite terse and i am having trouble tieing up the output from this function with stuff that i have read in the literature. so, for example, i make two sequences and calculate the correlation coefficient:

Hi, I am trying to calculate correlation coefficient for gene expression data. Tab delimited file looks like this Id v1 v2 v3 df 56 90 45 gh 87 98 78 ty 89 78 67 I used this code [code] gse20437 <- read.csv("C:/Users//Desktop/data/GSE20437_matrix.txt",header = TRUE, sep = ",", strip.white = TRUE) gsecor <- cor(gse20437, method

Hi, I wonder whether the following is possible with R, and whether anyone has done that and can share his/her code with me. I have a correlation matrix, and I want to create a correlation table that I can copy to Microsoft Word with a superscript above each correlation, indicating significant differences in the same row. That is, when correlations in the same row do not share superscript, it means

Could somebody program this kind of plot type to R, if none exists, based on mds or correlation tables or some more suitable method? What do you think about idea? Does it work? None similar or better exists? http://weightedassociationmap.blogspot.com/ Atte Tenkanen University of Turku, Finland

Hi, Being a R-newbie I am wondering how to calculate a correlation coefficient (preferably with an associated p-value) for data like: > d[,1] [1] 25.5 25.3 25.1 NA 23.3 21.5 23.8 23.2 24.2 22.7 27.6 24.2 ... > d[,2] [1] 0.0 11.1 0.0 NA 0.0 10.1 10.6 9.5 0.0 57.9 0.0 0.0 ... Apparently corr(d) from the boot-library fails with NAs in the data, also cor.test cannot cope with a