similar to: Determining the correlation coefficient?

Hallo, I'd like to write a GUI (first choice with GTK+). I've surfed through the R- an Omegahat-Pages, because I'd like to use RGTK2, GTK 2.10.11 in combination with glade on Windows XP (perhaps later Unix, Mac). I've found a lot of different information. Because of the information I'm not sure, if this combination is running on Windows XP and I'm unsure how it works. Is

Hi All, I have got matrix 'data' of dimension 22000x600. I want to make 50 independent samples of dimension 22000x300 from the original matrix 'data'. And then want to calculate pearsons CC for each of the obtained 50 matrices. It seems it is possible to do this using 'boot' function from library boot but I am not able to figure out how? I am really stuck. Please help!

I need to create artificial datasets with specific correlation coefficients (i.e. a dataset that returns r = 0.30, etc.) as examples for a lab I am teaching this summer. Is there a way to do that in R? Thanks. Jim Milks Graduate Student Environmental Sciences Ph.D. Program 136 Biological Sciences Wright State University 3640 Colonel Glenn Hwy Dayton, OH 45435 [[alternative HTML version

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

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

Dear all How does print.htest display the p-value in scientific notation? > (x <- cor.test(iris[[1]], iris[[3]])) Pearson's product-moment correlation data: iris[[1]] and iris[[3]] t = 21.65, df = 148, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.8270 0.9055 sample estimates: cor 0.8718 Above the p-value comes

Newsgroup members, I appreciate the help on this topic. David Duffy provided a solution (below) that was quite helpful, and came close to what I needed. It did a great job creating two vectors of dichotomous variables with a known correlation (what I referred to as a phi-coefficient). My situation is a bit more complicated and I'm not sure it is easily solved. The problem is that I must

Dear all, I tried a simple naive Bayes classification on an artificial dataset, but I have troubles getting the predict function to work with the type="class" specification. With type= "raw", it works perfectly, but with type="class" I get following error : Error in as.vector(x, mode) : invalid 'mode' argument Data : mixture.train is a training set with 100

Looking for help with the following problem. Given a sample of zeros and ones, for example: > VECTOR1<-rep(c(1,0),c(15,10)) > VECTOR1 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 How would I create a new sample (VECTOR2) also containing zeros and ones, in which the phi-coefficient between the two sample vectors was drawn from a population with a known

Hello, I would like to simulate randomly from a multivariate normal distribution using a correlation matrix, rho. I do not have sigma. I have searched the help archive and the R documentation as well as doing a standard google search. What I have seen is that one can either use rmvnorm in the package: mvtnorm or mvrnorm in the package: MASS. I believe I read somewhere that the latter was

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

Dear all, In MATLAB, to generate multivariate F with specified correlation matrix Pn I can use the code such as Z = mvnrnd([0 0 0 0 0], Pn, N); U = normcdf(Z,0,1); X = [finv(U(:,1),5,15) finv(U(:,2),5,15) finv(U(:,3),5,15) finv(U(:,4),5,15) finv(U(:,5),5,15)]; Is there something similar in R? Thank you for your time.

The function heatmap() allows to specify row/column labels via the options labRow/labCol. From the code of heatmap(), I understand that when no labels are specified, the row/column labels (or indices) of the input matrix are taken as labels and re-ordered together with the rows and columns of the matrix before plotting, whereas labels supplied via labRow/labCol are plotted in the original order.

Full_Name: Anja von Heydebreck Version: 1.9.1 OS: Linux Submission from: (NULL) (155.250.128.25) I obtained a negative p-value from the fisher.test() function: > fisher.test(matrix(c(14576,3023,89,68),2), alternative="g")$p.value [1] -8.426593e-13 With R 1.8.1, I got a p-value of -6.239231e-12 for this example. Anja von Heydebreck

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

Newsgroup members, Does anyone have a clever way to simulate a correlation matrix such that each column contains dichotomous variables (0,1) and where each column has different prevalence rates. For instance, I would like to simulate the following correlation matrix: > CORMAT[1:4,1:4] PUREPT PTCUT2 PHQCUT2T ALCCUTT2 PUREPT 1.0000000 0.5141552 0.1913139 0.1917923 PTCUT2

Full_Name: Anja von Heydebreck Version: 1.3.0 OS: Alpha Unix Submission from: (NULL) (141.14.19.61) Hi, I repeatedly obtained meaningless results from the function 'cutree' in the 'mva' package, when the argument 'h' was greater or equal to the maximum height occuring: > library('mva') > y [,1] [,2] [,3] [,4] [1,] 0 1 -1 1 [2,] 0 -1

Hello, I want to use pearson's correlation as distance between observations and then use any centroid based linkage distance (ex. Ward's distance) When linkage distances are formed as the Lance-Williams recursive formulation, they just require the initial distance between observations. See here: http://en.wikipedia.org/wiki/Ward%27s_method It is said that you have to use euclidean

Dear all Is there an easy way to display only one half (top-right or bottom-left) of a correlation matrix? > require(Hmisc) > rcorr(as.matrix(mtcars[ , 1:4])) mpg cyl disp hp mpg 1.00 -0.85 -0.85 -0.78 cyl -0.85 1.00 0.90 0.83 disp -0.85 0.90 1.00 0.79 hp -0.78 0.83 0.79 1.00 n= 32 P mpg cyl disp hp mpg 0 0 0 cyl 0 0 0 disp 0 0

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),