similar to: Estimate correlation with bootstrap

Hello. I have got two problems in bootstrapping from dependent data sets. Given two time-series x and y. Both consisting of n observations with x consisting of dependent and y consisting of independent observations over time. Also assume, that the optimal block-length l is given. To obtain my bootstrap sample, I have to draw pairwise, but there is the problem of dependence of the x-observations

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Dear All, I am not familiar with R yet I want to use it to perform some task, hence my posting here. I hope someone can help. I have a set of data, genes (rows) and samples (columns). I want to do a Pearson correlation on all the possible pairwise combinations of all the genes (2000). Does anyone have an idea of how to execute this in R? Thanks in advance. -- View this message in context:

[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 have recently been using the R program and encountered a recurring problem. I have been trying calculate the correlation of a 16 column table. Everytime I type in cor(test), where test is data that I uploaded into R using the read.table function, I get an error: Error in cor(test) : missing observations in cov/cor In addition: Warning message: In cor(test) : NAs introduced by coercion

Hello, I am quite new to R and I am trying to get a round correlation from a table with dozens of columns. However, all the columns contain several blank places which show to me as NAs. Then, when I type round(cor(data),2), I get no results - everything (except correlation of one column with the same one, of course) is NA. I do not want to replace NA with zero, because it would ruin the results. I

I tried 2 methods to estimate C.I. of correlation coefficient of variables x and y: > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) #METHOD 1: Pearson's ********************************************************** > cor.test(x, y, method = "pearson", conf.level = 0.95) Pearson's

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

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

Dear all, I have a problem. I have m variables each of which has n observations. I want to calculate pairwise correlation among the m variables and store the values in a m x m matrix. It is extremely slow to use nested 'for' loops if m and n are large. Is there any efficient alternative to do this? Many thanks for your suggestions!! Bill

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,] > >

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Hi, I have a data.frame with 294 columns and 211 rows. I am calculating correlations between all pairs of columns (excluding column 1) and based on these correlation values I delete one column from any pair that shows a R^2 greater than a cuttoff value. (Rather than directly delete the column all I do is store the column number, and do the deletion later) The code I am using is: ndesc

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",

Hi everybody. I'm trying to do a correlation matrix in a list of files. Each file contains 2 columns: "capt1" and "capt2". For the example, I merged all in one data.frame. My data also contains many missing data. The aim is to do a correlation matrix for the same data for course (one correlation matrix for capt1 and another for capt2). For the moment, I have a correlation

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

Is there a simple way to run cor.test on for a matrix of correlations? Of course, cor on a data frame produces a correlation matrix, but cor.test will only take two variables at a time. Is there a way to get behavior similar to that of cor with cor.test? I suppose the programming alternative would be to run two for loops with the number of items and cor test embedded accessing the columns of

Hi, I have got four correlation matrix. They are the same set of variables under different conditions. Is there a way to test whether the correlation matrix are significently different among each other? Could anyone give me some advice? -- View this message in context: http://www.nabble.com/correlation-matrix-difference-tf4073868.html#a11578046 Sent from the R help mailing list archive at

Hi, I am currently working with a data set which contains a list of julian dates of phenological (flowering, leaf growth etc.) I obtained a correlation matrix by simply using the cor function with the dataset cor(dataset,use="complete.obs") that gives me a correlation matrix but the correlations are somehow different from when I run individual correlations using the cor function and

Dear list, Following up on an earlier post, I would like to reorder a dataset and compute pairwise correlations. But I'm having some real problems getting this done. My data looks something like: Participant Stimulus Measurement p1 s`1 5 p1 s`2 6.1 p1 s`3 7 p2 s`1 4.8 p2