similar to: Correlations by subgroups

Hi, How can I accomplish this in R. Example: I have the following data.frame: data <- data.frame(x=c(1,2,3,4,5,6,5,3,7,1,0,4,8),y=c(1,2,1,2,2,2,1,1,1,2,2,2,2),z=c(5,8,4,3,4,1,6,3,3,6,3,5,7)) Supposing that data$y is a factor, I would like to find the Spearman correlation between data$x and data$z indexing it by data$y. To be more specific, I want to find two correlations: between x and z

I have the following problem. I want to use pairs function and get a matrix of scatterplots with the correlations in the upper panel and the ordinary scatterplots in the lower panel. Moreover, I want to have points colored in five differet ways in the lower panel, because I have five subgroups. In order to do that I tried to combine examples on pairs function help. I got a colored matrix

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

Hello, I've searched for an answer to no avail. I am wondering if anyone knows how to constrain certain correlations to be equal. I have family data with 2 twins per family plus up to 2 siblings. I would like to somehow constrain all the sibling correlations (twin-sib and sib-sib) to be the same while allowing the twin-twin correlation to be different. Here is some simulated code:

I am trying to generate a fourth vector,z, given three known and fixed vectors, x1,x2,x3 with corresponding known and fixed correlations with themeselves and with z. That is, all correlations are known and prespecified. How can I do this? Thank you, ben

I'm aware that S N Krishna asked the same question. However, I have failed to implement the posted solution for running rank order correlations on multiple subsets of data using the by() function. Here is my problem: Take a set of data from two subjects, who provided numerical infant mortality (IM) estimates for five countries: sub <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)

Recently, I was working with some lagged designs where a vector of observations at one time was used to predict a vector of observations at another time using a lag 1 design. In the work, I noticed a lot of negative correlations, so I ran a simple simulation with 2 matched points. The crude simulation example below shows that the correlation can be -1 or +1, but interestingly if you do this

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

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

Dear R-Users, I am currently looking for a way to test the equality of two correlations that are related in a very special way. Let me describe the situation with an example. - There are 100 respondents, and there are 2 points in time, t=1 and t=2. - For each of the respondents and at each of the time points, I have information on 10 X-variables and on 10 Y-variables. - Based on this

Dear R-users, I want to multiple impute missing scores, but only for a few subgroups in my data (variable 'subgroups': only impute for subgroups 2 and 3). Does anyone knows how to do this in MICE? This is my script for the multiple imputation: imp <- mice(data, m=20, predictorMatrix=pred, post=post, method=c("", "", "", "",

Hello, I'm looking for an efficient function for calculating partial correlations. I'm currently using the pcor.test () function, which is equivalent to the cor.test() function, and can receive only single vectors as input. I'm looking for something which is equivalent to the cor() function, and can receive matrixes as input (which should make the calculations much more efficient).

I have two variables, minutereturnsa which can be thought of as my independent variable and minutereturnsb which can be thought of as my dependent variable. When I run correlations on the two variables, depending on which of the three methods I use, I get values of between -.15 through -.19. Then, when I do a regression, I get an rsquared of .004 which is more in line with my intuition. In other

Hi there, When I make two correlations matrices of the same size from a <- cor(el[1:20]) b <- cor(fl[1:20]) how can I compare if this matrices are equal? Thanks, Martin -- Ihr Partner f?r Webdesign, Webapplikationen und Webspace. http://www.roomandspace.com/ Martin Kaffanke +43 650 4514224 -------------- next part -------------- A non-text attachment was scrubbed... Name: not available

Hello, I'm using the rmeta package to perform a meta analysis using summary statistics rather than raw data, and would like to analyze the effects in three different subgroups of my data. Furthermore, I'd like to plot this on one forest plot, with corresponding summary weighted averages of the effects displayed beneath each subgroup. I am able to generate the subgroup analyses by simply

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",

I am in need of 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

Howdy! I need to calculate partial correlations and I just can't find out how to do that with R. Can anybody help? Ragnar -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !)

Hi there, I would like to draw 10 correlations from a bivariate population - but every draw should be done with a different sample size. I thought I could to this with a loop: r=numeric(10) #Goal vector N = c(1000,100,80,250,125,375,90,211,160,540) #Sample size vector for(i in 1:10) { data <- mvrnorm(n=N,mu=c(0,0),Sigma=matrix(c(1,.3,.3,1),2)) r[i] <- cor(data[,1],data[,2]) }

Hello, I would like to do a PCA with dudi.pca or PCA, but also with the use of Spearman or Kendall correlations Is it possible ? Otherwise, how can I do, according to you ? Thanking you in advance Eric Bourgade RTE France [[alternative HTML version deleted]]