correlation

I've been working on some data, available here: Dropbox' csv file (please be kind to use it to replicate the error). When I run the code: t<-read.csv("120.csv") x<-NULL for (i in 1:100){ x<-c(x,cor(t$nitrate,t$sulfate,use="na.or.complete")) } sum(is.nan(x)) I get random values of the last expression, usually around 55 to 60. I expect cor to give repetible results, so I expect x to be a vector of length=100 made of identical values. See, for example, the output of two independent runs: > x<-NULL; for (i in 1:100){x<-c(x,cor(t$nitrate,t$sulfate,use="na.or.complete"))} > sum(is.nan(x)) [1] 62 >

I coundn't find a function that computes a matrix of correlation coefficients for arrays containing observations for more than two variables when there are NaNs in the data. There are functions doing this for pairs of variables (or just masking the arrays using ~is.nan()). But using these functions by looping over a large number of variables, computing the correlation for each pair can be very time consuming. So I tried on my own and soon realized that the complexity of doing this is a question of the proper normalization of the Covariance. I would be very interest in your opinions on how to

There is an image A of fixed size 256*256*3 (RGB). The mathematical formula for covariance between two adjacent pixels values x,y in an image is popularly known to be: cov(x,y) = 1/n summation from i = 1 to n of [E(x_i-E(x))(y_i-E(y))] r_xy = cov(x,y) / (sqrt(D(x)*D(y))) D(x) = 1/n summation from i = 1 to n of square[(x_i - E(x))] E(x) = 1/n summation from i = 1 to n of (x_i) where r_xy is the correlation coefficients between two horizontally, vertically, and diagonally adjacent pixels of these two images. Q1: How to do the above computation in MATLAB? Q2: How to randomly select say 5000 pairs

I have a variable with a given distribution (normale in my below example). set.seed(32) var1 = rnorm(100,mean=0,sd=1) I want to create a variable (var2) that is correlated to var1 with a linear correlation coefficient (roughly or exactly) equals to "Corr". The slope of regression between var1 and var2 should (rougly or exactly) equals 1. Corr = 0.3 How can I achieve this? I wanted to do something like this: decorelation = rnorm(100,mean=0,sd=1-Corr) var2 = var1 + decorelation But of course when running: cor(var1,var2) The result is not close to Corr! I did something similar a while ago. I am

I have the following data frame: y <- data.frame(group = letters[1:5], a = rnorm(5) , b = rnorm(5), c = rnorm(5), d = rnorm(5) ) How to get a data frame which gives me the correlation between columns a,b and c,d for each row? something like: sapply(y, function(x) {cor(x[2:3],x[4:5])}) Thank you, S You could use apply > apply(y[,-1],1,function(x) cor(x[1:2],x[3:4])) [1] -1 -1 1 -1 1 Or ddply (although this might be overkill, and if two rows have the same group it will do the correlation of columns a&b and c&d for both those rows): > ddply(y,.(group),function(x) cor(c(x$a,x$b),c(x$c,x$d))) group