similar to: R function implementation

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000

Hello, Here is a general question that I think many of you will have insight to. I have 40 independent dataframes. Each has a dimension of 1359 x 15. For example >dim(meanAnnualWaterDepth) > 1539 15 I need to calculate the average value for each dataframe but only for columns 7: 15. I can do this with apply as follows: meanAnnualWaterDepth$Average <-

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",

Dear list, Following up on an earlier post, I would like to reorder a dataset and compute pairwise correlations. But I'm having some real problems getting this done. My data looks something like: Participant Stimulus Measurement p1 s`1 5 p1 s`2 6.1 p1 s`3 7 p2 s`1 4.8 p2

I'm an R novice, so I hope my question is a valid one. I'm trying to run the following script in the current version of R. for (i in 1:1640){for (j in (i+1):1641){ if (i == 1 && j == 2){x <- cor(sage[i,],sage[j,],method="spearman"); y <- cor(frie[i,],frie[j,],method="spearman")} if (i != 1 || j != 2){x <-

[Apologies in advance if this is too "statistics" and not enough "R".] I've got an experiment with two sets of treatments. Each subject either received all treatments from set A or all treatments from set B. I can compute the N pairwise correlations for all treatments in either set using cor(). If I take the mean of these N pairwise correlations, I see that the effects

Hi, I calculating the output of a function when applied to pairs of row from a single matrix or dataframe similar to how cor() and pairs() work. This is the code that I have been using: pairwise.apply <- function(x, FUN, ...){ n <- nrow(x) r <- rownames(x) output <- matrix(NA, nc=n, nr=n, dimnames=list(r, r)) for(i in 1:n){ for(j

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

Arrrrr, I think I've found a bug in the behavior of the stats::cor function when NAs are present, but in case I'm missing something, could you look over this example and let me know what you think: > a = c(1,3,NA,1,2) > b = c(1,2,1,1,4) > cor(a,b,method="spearman", use="complete.obs") [1] 0.8164966 > cor(a,b,method="spearman",

Hi guys, I am using the cor() function to see if there are correlations between March cortisol levels and December cortisol levels and I'm trying to figure out if the function is doing what I want it to do. Each sample has it's own separate row in the CSV file that I'm working out of. March Cort and December Cort are different columns and they come from separate samples, therefore

Hi there, I would like to solve a simple equation in R a^2 - a = 8.313 There is no real solution to this problem but I would like to get an approximate numerical solution. Can someone suggest how I can set this up? Thanks in advance, Diviya [[alternative HTML version deleted]]

Hi Sorry for the rather long message. I am trying to use the cfa command in the lavaan package to run a CFA however I am unsure over a couple of issues. I have @25 dichotomous variables, 300 observations and an EFA on a training dataset suggests a 3 factor model. After defining the model I use the command fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",

I tried to use gpuCor function in the gputools package of R to calcuate the pairwise correlations of a matrix of 40,000 columns. Becuase there would be memory issues if I use the whole matrix at a time, I splitted the matrix into submatrix of 10,000 columns and then calculate the pairwise correlation of different submatrices. There are altogether 4 submatrices, so I need to calculate the pearson

Hello! I am running principle components analysis using princomp function in pacakge psych. mypc <- princomp(mydataforpc, cor=TRUE) Question: I'd like to use pairwise deletion of missing cases when correlations are calculated. I.e., I'd like to have a correlation between any 2 variables to be based on all cases that have valid values on both variables. What should my na.action be in

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:

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")

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

> I am trying to produce a heatmap of pairwise correlations, but since the matrix is > symmetric, I only need either the upper or the lower triangle. I have scoured the > web and R documentation, but I have not been able to find a way to produce such a > figure. Is there a simple way to produce a heat map with only the part above or > below the diagonal? You might want to check

Dear useRs, First off, sorry about the long post. Figured it's better to give context to get good answers (I hope!). Some time ago I wrote an R function that will get all pairwise interactions of variables in a data frame. This worked fine at the time, but now a colleague would like me to do this with a much larger dataset. They don't know how many variables they are going to have in the