search for: correlate

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

Dear R experts, I am trying to simulate correlated binary data and have stumbled upon the following problem: With the help of "binarySimCLF" or "mvpBinaryEp" I have been able to simulate correlating binary vectors given certain mean values and a desired correlation. My problem is that these procedures do not allow you to speci...

...fied 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 should not be correlated to each other. Anyone can help me? Regards, Phil -- View this message in context: http://r.789695.n4.nabble.com/Generation-of-correlated-variables-tp4475799p4475799.html Sent from the R help mailing list archive at Nabble.com.

Hi All: I would like to generate a binomial random variable that correlates with a normal random variables with a specified correlation. Off course, the correlation coefficient would not be same at each run because of randomness. I greatly appreciate your input. Ashraf

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

...matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID, and cor is the correlation of ID and ID2. I know I can use two for loops to do it but it is very time consuming considering the correlation matrix is generated for each month of the past 20 years. Is there a better way to do it? Regards, Richard [[alternative HTML version d...

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, 8, 9, 12.34, 56.7, 1015245698745859) > var2<- c(11, 222, 333, 4444, 55555, 1.1, 2.2, 3.3, 4.4, .55) > var3 <-c(22.2, 66.7, 99.9, 1000008, 123123, .1, .2, .3, .4, .5) > var4<- c(.000001,.00...

I was on vacation the last week and wrote some code to run a 500-day correlation between the Nasdaq tracking stock (QQQ) and 191 currency pairs for 500 days. The initial run took 9 hours(!) and I'd like to make it faster. So, I'm including my code below, in hopes that somebody will be able to figure out how to make it faster, either through parallelisation, or by making changes. I've

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

...ly answer my question, which is more about what confidence i can assign to the measured correlation coefficient (0.5198252). an alternative question would be: given two sequences and a calculated correlation coefficient, with what probability could i assert that the underlying processes are indeed correlated and that the calculated correlation coefficient does not simply arise by chance. please forgive my ignorance. any help will be vastly appreciated. thanks! best regards, andrew. ---------------------------------------------------------------------- Get a free email account with anti spam protect...

I'm looking to create a correlation matrix, but I have already obtained the correlations, which are stored in a vector. (Basically, I'm running a simulation which requires a correlation matrix, but I am simulating the various correlations.) My aim is to create a function that can take the vector, and fit the values into their respective locations in a correlation matrix. (The correlations

Hi, I wanted to know how to compute the correlation ratio (eta) between two variables using R. Is there any function to compute the correlation ratio. Any help will be very much appreciated. Thanks, Suman [[alternative HTML version deleted]]

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, Is there any R library/package that calculates tetrachoric correlations from given marginals and Pearson correlations among ordinal variables? Inputs to polychor function in polycor package are either contingency tables or ordinal data themselves. I am looking for something that takes marginal distributions and Pearson correlation as inputs. For example, Y1=(1,2,3) with P(Y1=1)=0.3,

...someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix Mouse 430 2.0 arrays, so I've got about 45,000 probesets (rows; with 1st column containing identifiers) and 30 biological replicates (columns; with the top row containing the header information). I've looked through se...

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, R! Question1: I am trying to correlate two vectors of numbers (two columns of microarray signal values) by using the non-parametric Spearman's rank correlation coefficient rho: > cor.test(V2.Signal,V3.Signal,method="spearman") but I get the error message: Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tai...

Hi, there: As you all know, correlation is not a very robust procedure. Sometimes correlation could be driven by a few outliers. There are a few ways to improve the robustness of correlation (pearson correlation), either by outlier removal procedure, or resampling technique. I am wondering if there is any R package or R code that have incorporated outlier removal or resampling procedure in

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

Hello, I am running polychoric correlations on a dataset composed of 12 ordinal and binary variables (N =384), using the polycor package. One of the association (between 2 dichotomous variables) is very high using the 2-step estimate (0.933 when polychoric run only between the two variables; but 0.801 when polychoric run on the 12 variables). The same correlation run with ML estimate returns a