similar to: Test for equality of complicatedly related average correlations

Dear All, I am currently using R in Windows PC with a 2 GB of RAM. Some pretty large datasets are expected soon, perhaps in an order of several GB. I am facing a similar situation like Ralph, either to get a new PC with a bigger RAM or else. I am just wondering if R is getting faster in other systems like UNIX or Linux. Any suggestions are appreciated. Regards, Jin

Dear R-Users, as I will start a huge simulation in a few weeks, I am about to buy a new and fast PC. I have noticed, that the RAM has been the limiting factor in many of my calculations up to now (I had 2 GB in my "old" system, but Windows still used quite a lot of virtual memory), hence my new computer will have 4 GB of fast DDR2-800 RAM. However, I know that 1.) Windows 32 bit cannot

Dear Prof Ripley and Don, Thank you all for your kind reply. The UNIX system we have is Sparc Solaris v8 (a 64bit version). The Precompiled binary distributions of the base system and contributed packages are only available for Linux, MacOS X and Windows. The only thing available for UNIX seems Source Code for all Platforms. Could you please guide me to the "Prebuilt versions of R are

Dear All, I wonder if there is any implemented statistical test in R to test the equality between many correlations. As an example, let X1, X2, X3 X4 be four random variables. let Phi(X1,X2) , Phi(X1,X3) and Phi(X1,X4) be the corresponding correlations. How to test Phi(X1,X2) = Phi(X1,X3) = P(X1,X4)? Many thanks in advance, Bernard

Dear R-Users, I am working on an Hierarchical Bayes model and tried to replace the inner for-loop (which loops over a list with n.observations elements) with truely vectorized code (where I calculated everything based on ONE dataset over all respondents). However, when comparing the performance of the two alternatives, I found out that the code with the for-loop actually was faster! In order to

Hi, I am turning to you with a (hopefully simple?) stats question. I would like to test equality of two correlation coefficients in a setting with three variables X,Y,Z, i.e. equality of r(X,Y) and r(Z,Y). I have found a formula to transform the "2 dependent correlations difference" to t-distribution with N-3 df: t = (rxy - rzy)* SQRT[{(n - 3)(1 + rxz)}/ {2(1 - rxy^2 - rxz^2 - rzy^2 +

Hi, I have a data frame DATA, which (simplified of course) looks like this: know1 = c("Y","N","N","Y","N","N","Y","Y","N") par1=c(1,4,5,3,3,2,3,3,5) know2 = c("Y","Y","N","Y","N","N","N","Y","Y")

Dear R-users, I am working on a problem that I am currently not able to solve efficiently. It is about multiplying one column of a matrix with only a certain number of rows of another matrix. Let me illustrate my problem with an example: n.obs = 800 n.rowsperobs = 300 n.param = 23 Designmat = matrix(rnorm(n.obs*n.rowsperobs*n.param),ncol=n.param) Betamat =

Thank you, but this now sounds more difficult: what would be the point in having these ready-made functions if I have to do it manually? Anyway, How would I implement the last part? On Thu, Mar 23, 2023 at 1:23?AM Ebert,Timothy Aaron <tebert at ufl.edu> wrote: > > If you are open to other options: > The null hypothesis is that there is no difference. > If I have two equations

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:

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

A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as

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]) }

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

Hello, I have three numerical variables and I would like to test if their correlation is significantly different. I have seen that there is a package that "Test the difference between two (paired or unpaired) correlations". [https://www.personality-project.org/r/html/paired.r.html] However, there is the need to convert the correlations to "z scores using the Fisher r-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 R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental

I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while

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)

I'm hoping there is a simple answer to this - it seems that there should be, but I can't figure it out. I have a matrix/data frame with three variables of interest - V1, V2, V3. One, V1, is a factor with x levels (x may be a large number); I want to calculate the correlation between the other two (i.e. cor(V2,V3)) for each level, and store it as a vector of length x. I should think this