similar to: Pearson corelation and p-value for matrix

Hi, I'm trying to obtain confidence intervals for auto and cross correlation estimates. I've adapted code made available by Stock and Watson that uses the Bartlett Kernel and the delta method. In R it runs really, really slow because of the loops it uses and I have 9 series that I'd like to examine (81 total combinations). It was easy enough to replace one of the while loops with a

Dear R users, Simple question. Can anyone help with the code that would allow me to view only the variables who's correlation output is >0.8? This is the code I'm using to date >cor(data, method="spearman") Kind regards Krys -------------------------------------------- _________________________________________________________________ Save time by

Dear all, Sorry to post my query once again in the list, since I did not get attention from anyone in my previous mail to this list. Now I make it simple here that please give me a code for find out the columns of a dataframe whose correlation coefficient is below a pre-determined threshold. (For detailed query please see my previous message to this list, pasted hereunder) Thanks and regards,

Hello, I'm attempting to create a data frame with correlations between every pair of variables in a data frame, so that I can then sort by the value of the correlation coefficient and see which pairs of variables are most strongly correlated. The sm2vec function in the corpcor library works very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 =

I have a print method for a set of statistical tests, vcdExtra::CMHtest, for which I'd like to have more sensible printing of pvalues, as in print.anova(). [Testing this requires the latest version of vcdExtra, from R-Forge **|install.packages("vcdExtra", repos="http://R-Forge.R-project.org")|** ] With my current print method, I get results like this, but all Prob values

Dear all, I am new to programming in R. I deal with microarray data,which is a data frame object type. I need to carry out a few statistical procedures on this, one of them being the pearson corelation. I need to do this between each row which is a gene. So the desired result is a square matrix with the pearson corelation value between each row. So the first column would be (1,1)=0,(1,2),(1,3)

Dear R users, I am trying to print heatmaps in a loop (with a pause). Idea is to visualize changing correlations over time and for testing I wrote this simple (reproducible) code below. My problem is that levelplot() does not produce any output when I run the code (though heatmap does). Ideally I would like to use levelplot() as it produces a neat index on the side indicating the color and the

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value

Hi, I am experiencing a long delay when using dataframes inside loops and was wordering if this is a bug or not. Example code: > st <- rep(1,100000) > ed <- rep(2,100000) > for(i in 1:length(st)) st[i] <- ed[i] # works fine > df <- data.frame(start=st,end=ed) > for(i in 1:dim(df)[1]) df[i,1] <- df[i,2] #takes for ever R: R 2.0.0 (2004-10-04) OS: Linux, Fedora Core 2

Hi, I have a data.frame with 294 columns and 211 rows. I am calculating correlations between all pairs of columns (excluding column 1) and based on these correlation values I delete one column from any pair that shows a R^2 greater than a cuttoff value. (Rather than directly delete the column all I do is store the column number, and do the deletion later) The code I am using is: ndesc

Dear all, I have wrote a code for a linear regression. I want to write a loop for so, that I can get estimate for pavlues for six predictors. But I am getting for estmate for only last one. How can I get pvalues for all my predictors in a loop?? Anticipating your help Thanks Ales > mat<-matrix(rnorm(36),nrow=6) > mat [,1] [,2] [,3]

How do we get 2-tailed p-values for the rlm summary? I'm using the following: > fit <- rlm(oatRT ~ oatoacData$erp, psi=psi.bisquare, maxit=100, na.action='na.omit') > fitsum <- summary(fit, cor=F) > print(fitsum) Call: rlm(formula = oatRT ~ oatoacData$erp, psi = psi.bisquare, maxit = 100, na.action = "na.omit") Residuals: Min 1Q Median

Hello R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

Dear all, Through the following code, I wanted to perform correlation test repetedly (through loop) on different combinations of variables of a data set. Code: x=read.table("sample.txt",header=T,sep="\t") out="corout.txt" sink(out) nm = names(x) print(nm) nvr=3 # nvr=Total no. of variables in the input data file for (i in 1:(nvr-1)) { for(j in (i+1):nvr) { frm=

Hi there, I wonder if there is a way of efficiently generating a correlation matrix of two expression matrices. I want to correlate miRNA and mRNA expression and used the following code: ##dat.mi miRNA expression matrix, dat.m mRNA expression matrix nc <- nrow(dat.mi) cor.mat <- data.frame(rep(NA,nrow(dat.m))) pval.mat <- data.frame(rep(NA,nrow(dat.m))) for(i in 1:nc) { cr <- vector()

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

Dear, I want to compute coefficient of determination (R-squared) to complement AIC for model selection of multivariable GLM. However, I found this is not a built-in function in glm. neither is it available through reviewing the question in the R-help archive. Please kindly help and thanks a lot. Elaine [[alternative HTML version deleted]]

Hi everybody, I would like to apply cor.test to a matrix with m rows and n columns and get the results in a list of matrices , one matrix for p.val, one for the statistic, one for the correlation and 2 for upper and lower confidence intervals, something analog with cor() applied to a matrix. I have done my own function to get a matrix of p.values and i suppose i can build similar functions for

Hi, I would like to derive p-values for pair-wise comparison (Tukey's) of effects when the response is a count. I am trying a test case where y ~ Po( lambda(x) ). x has three levels : A, B and C with lambda(x) = 10, 20 and 20 respectively. Hence, p-values for the contrast C - B should distribute uniformally. I have implemented this test case as below but do not get uniform

Regarding "financial" data: I have a high frequency (1 minute) measure of supply/demand and I'd like to know if it has any influence on short term price changes (also 1 minute). Question: How do I calculate the correlation between this supply/demand measure and price changes (correctly)? Some facts about that data: The price changes and supply/demand measure are non-normal. An