similar to: splitting items into groups according to correlations

Hi everyone. I have a question about a work on R I have to do for my job. I have temperature data coming from 70 weather stations. One data file corresponds to one station for one year (so 70 files for one year). Each file looks like this (important: each file contains NAs): time data 01/01/2008 00:00 -0.25 01/01/2008 00:15 -0.18 01/01/2008 00:30 -0.25 01/01/2008

Hi, I'm turning my wheels on this and keep coming around to the same wrong solution - please have a look and give a hand ... The premise is: a DF like so > loremIpsum <- "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque leo ipsum, ultricies scelerisque volutpat non, volutpat et nulla. Curabitur consequat ullamcorper tellus id imperdiet. Duis semper malesuada

Jens I'm not sure what you intend by "predefined assumptions". 1. If you merely want to conduct an exploratory rather than confirmatory analysis for the relevant paths, there are ways within SEM to do this. (In this case you could use John Fox's SEM package). 2. If you do not wish to assume multivariate normality, then you may use a variety of alternative (to maximum likelihood)

I've seen that this question has been asked before, in the archives, but I haven't been able to find a workable answer. This may be a failure to understand the statistics! The problem is that, while I can easily get partial correlation values out of corpcor, how do I get significance values? Surely the significance of a partial correlation between two variables isn't the

I am an R newbie, and I am facing what is for me a challenging problem. I am trying to write functions to solve the following problems. It seems as though R offers so many options I am not sure how to approach the problem. I have a data frame with dates in the first column and returns in the remaining columns. I would like to give a user a variety of options. I designate Series1 as the

Hello, I am wondering whether there is a way to test whether two non-independent correlation coefficients are significantly different, in R? I have an experimentally measure variable Y, and two different variables X1, and X2, which are predictions of Y that were predicted using two different computational models. I would like to see whether the correlation of Y and X1, and Y and X2 is

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

Can someone point me to literature and/or R software to solve the following problem: Assume n true scores t measured as x with uncorrelated errors e , i.e. x = t + e and assume each true score to a have a certain amount of correlation with some of the other true scores. The correlation matrix cx of x will have its off-diagonal entries reduced by measurement error compared to the true

Hi, from what I understand about the canonical correlation function 'cancor', it looks for correlations in two sets of variables, each represented in matrix form. Right? Sounds exactly like what I need. I have tried the following but I am not sure how to interpret the results. AudioPCs <- c(ArTHarF0PCA$x[,2], ArTHarF1PCA$x[,2], ArTHarF2PCA$x[,2], ArTHarF3PCA$x[,2],

I am doing an analysis that requires me to calculate correlations for a matrix of 15,000 rows x 50 columns. For each row I want to calculate the correlation to all other rows and then for each row, find the n (say 10) most correlated rows. If read in the 15,000 x 50 data from file and pass it to 'cor', this function quite appropriately (and very quickly) calculates all possible row by

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,

R-helpers: I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is: fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont) I would like to measure the correlation b/t the variable "distance" at

Dear list, I am trying to re-analyse something. I do have two time series, one of which (ts.mar) might help explaining the other (ts.anr). In the original analysis, no-one seems to have cared about the data being time-series and they just did OLS. This yielded a strong positive correlation. I want to know if this correlation is still as strong when the autocorrelations are taken into account.

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'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've a vector of pairwise correlations in the order low-index element precedes the high-index element, say: corr(1,2)=0.1, corr(1,3)=0.2, corr(2,3)=0.3, corr(3,4)=0.4 How can I construct the corresponding correlation matrix? I tried using the "combn"-function in "combinat" package: library(combinat) combn(c(0.1,0.2,0.3,0.4),2) , but to no avail... Thank you for your

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 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

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

Karl, If you use one of the specialized packages to calculate your ICC, make sure that you know what you're getting. (I haven't checked the packages out myself, so I don't know either.) You might want to read David Futrell's article in the May 1995 issue of Quality Progress where he describes six different ways to calculate ICCs from the same data set, all with different