similar to: correlation between two 2D point patterns?

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

Dear All, I am studying some process measurement time series in R and trying to identify time delays using cross-correlation function ccf. The results have however been bit confusing. I found a couple of years old message about this issue but unfortunately wasn't able to find it again for a reference. For example, an obvious time shift is observed between the measurements y1 and y2 when the

R-Experts, Here below my R code working without error message but I don't get the results I am expecting. Here is the result I get: [1] "All values of t are equal to 0.28611928397257 \n Cannot calculate confidence intervals" NULL If someone knows how to solve my problem, really appreciate. Best, S ######################################################### # Difference in Spearman

I believe the problem is here: cor1 <- cor(x1, y1, method="spearman") cor2 <- cor(x2, y2, method="spearman") The x's and y's are not looked for in data (i.e. NSE) but in the environment where the function was defined, which is standard evaluation. Change the above to: cor1 <- with(d, cor(x1, y1, method="spearman")) cor2 <- with(d, cor(x2, y2,

Dear all, Has someone implemented in R (or any other language) Knol DL, ten Berge JMF. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika, 1989, 54, 53-61. or any other similar algorithm? Best regards Jens Oehlschl?gel Background: I want to factanal() matrices of polychoric correlations which have negative eigenvalue. I coded Highham 2002

Full_Name: Raymond Salvador Version: R 1.6.2 OS: Windows ME Submission from: (NULL) (131.111.93.195) The mean function applied on individual components of several correlation matrices gives a wrong result (gives the first value instead of the mean). Here there is a simple example x1 <- rnorm(10,1,1) y1 <- rnorm(10,1,1) z1 <- cbind(x1,y1) w1 <- cor(z1) x2 <- rnorm(10,1,1) y2

Hello, R gives us the correlation functions cor(). (Many thanks ;-)) Does it also exist a "moving correlation" coefficient ? (like the moving average). If not, could someone give me some infos or link on how to practically implement such a function in R. (I did search for "moving correlation" on the R homepage but didn't find anything.) Thank you. Vincent

hi all, Suppose I have four variables (X1,X2,Y1,Y2) I want to calculate conditional correlation of (X1,Y1) given (X2, Y2). How can I do it in R? Thanks Ming Chen

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

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

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)

I've been unable to find a R package that provides the means of performing Clarke & Ainsworth's BIO-ENV procedure or something comparable. Briefly, they describe a method for comparing two separate sample ordinations, one from species data and the second from environmental data. The analysis includes selection of the 'best' subset of environmental variables for explaining

Hello all, I believe this can be done using bootstrap, but I am wondering if there is some other way that might be used to tackle this. #Let's say I have two pairs of samples: set.seed(100) s1 <- rnorm(100) s2 <- s1 + rnorm(100) x1 <- s1[1:99] y1 <- s2[1:99] x2 <- x1 y2 <- s2[2:100] #And both yield the following two correlations: cor(x1,y1) # 0.7568969 (cor1) cor(x2,y2)

Dear all, I am quite new to R; facing certain problems: Say, I have a text file( named as "try"): Year C1 C2 C3 C4 C5 C6 Y1 3.5 13.8 9.5 6.8 0.4 24.2 Y2 3.8 13.9 9.9 7.6 0.7 12.8 Y3 4.5 14.5 14.2 9.2 0.6 14.5 Y4 5.9 16.2 24.6 12.7 0.2 24.3 Y5 7.2 20.4 40.6 18.2 0.8 28.2 Y6 5.9 18.6 37.4 14.5 0.3 36.9 Y7 8.0 16.1 88.6 24.1 0.1 34.6 Y8 13.6 21.1 56.3 19.0 0.7 33.3 I wish to import the

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

I have been using the hetcor function in the polycor package. When I don't specify the use option everything runs smoothly. However, when I specify use either as "pairwise.complete.obs" or "complete.obs" I get this error Error in optim(rho, f, control = control, hessian = TRUE, method = "BFGS") : non-finite value supplied by optim Is this an error in

Dear r-help, I am trying to plot several scatter plots with marginal histograms on one page. Ideally, a page is equally divided into 4 figure regions. Within each figure region, a scatter plot with marginal histograms will be plotted. I followed Dr. Paul Murrell's code released online to successfully plot the scatter plot with marginal histograms. The code applies "layout()" to

Hi, I've been struggling with a lattice visualiation. I have a data.frame with 4 columns. What I'd like to have is a set of 3 panels. Ecah panel will have the first column plotted against serial number and then will superimpose the relevant column. My non-lattice version is as follows: x <- data.frame( ... ) par(mfrow=c(3,1)) for (i in 2:4) { plot(x[,1]) points(x[,i]) } Any

I have two pairs of related vectors x1,y1 and x2,y2 I wish to do a test for differences in means of x1 and y1, ditto x2 and y2. I am getting odd results. I am not sure I am using 'pt' properly... I have not included the raw vectors as they are long. I am interested if I am using R properly... > c(length(x1), length(y1), length(x2), length(y2)) [1] 3436 1619 2677 2378 First

Could I suggest the following extension to plot.formula: plot(cbind(y1,y2) ~ x, ...) should plot (y1 against x) and (y2 against x) on the same plot. The default y axis limits would be determined by the range of c(y1,y2). This would be pretty handy sometimes, replacing 4 lines of code. The current plot.formula evaluates cbind(y1,y2), which is a matrix, so plot.formula looks for