similar to: weighted correlation coefficient

I did a linear correlation of data using glm.fit and stored the output in the object "f": f <- glm.fit(x, y, w) I am intereseted in estimating the quality of the correlation. I am used to do it using pearson correlation coefficient "r" or "r^2". Can I extract this coefficient from the output of glm.fit? Is there another number in the output of glm.fit that

Newsgroup members, I appreciate the help on this topic. David Duffy provided a solution (below) that was quite helpful, and came close to what I needed. It did a great job creating two vectors of dichotomous variables with a known correlation (what I referred to as a phi-coefficient). My situation is a bit more complicated and I'm not sure it is easily solved. The problem is that I must

Hello, I would be grateful if anybody can help me in finding an R function to compute weighted Spearman correlation coefficient? Kind regards, Daniel

Hello, I found an interesting program on Pierre Legendre's webpage: http://www.bio.umontreal.ca/casgrain/en/labo/corr_permute.html With this program one can compute a "Pearson correlation coefficient matrix with permutation test". This is exactly what I need as an R-package because so far I have only analyzed my data with the function cor(). However, I need additional

I tried 2 methods to estimate C.I. of correlation coefficient of variables x and y: > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) #METHOD 1: Pearson's ********************************************************** > cor.test(x, y, method = "pearson", conf.level = 0.95) Pearson's

Hi. I have data which i would want to assess the degree of agreement between two assays, e.g., to evaluate reproducibility or for inter-rater reliability. I have used the Pearson product-moment correlation coefficient. It looks good ranginging between 0.90 to 0.998. Though this looks good. I am told the Concordance correlation coefficient will give a better picture of how reproducible the assay

This is a statistics question not an R question. When calculating the sample correlation coefficient cor(x_t,y_t) between say two variables, x_t and y_t t=1,.....n ( one can assume that the variables are in time but I don't think this really matters for the question ), does someone know where I can find any piece of literature that says that each (x_j,y_j) pair has To be independent from the

I have the following code:   ## to check correlation between the simulated uniform data x2 <- uni[,1] ; x2[1:10] y2 <- uni[,2] ; y2[1:10] result2 <- boot(cbind(x2,y2), f, 20) # get 95% confidence interval boot.ci(result2, type="bca") cor.test(x2,y2, method="pearson", conf.level=0.95)   part of my data:   > x2 <- uni[,1] ; x2[1:10]  [1] 0.63933145 0.71677785

Hi, I am trying to calculate correlation coefficient for gene expression data. Tab delimited file looks like this Id v1 v2 v3 df 56 90 45 gh 87 98 78 ty 89 78 67 I used this code [code] gse20437 <- read.csv("C:/Users//Desktop/data/GSE20437_matrix.txt",header = TRUE, sep = ",", strip.white = TRUE) gsecor <- cor(gse20437, method

Hi! All, To find co-expressed genes from a expression matrix of dimension (9275 X 569), I used rcorr function from library(Hmisc) to calculate pearson correlation coefficient (PCC) and their corresponding p-values. From the correlation matrix (9275 X 9275) and pvalue matrix (9275 X 9275) obtained using rcorr function, I wanted to select those pairs whose PCC's are above 0.8 cut-off and then

Dear Help, I'm trying to calculate a weighted correlation matrix from a data frame with 6 columns (variables) and 297 observations extracted from the regression. The last column is a weight column which I want to apply. $ model :'data.frame': 297 obs. of 6 variables: ..$ VAR1 : num [1:297] 5.21 9.82 8.08 0.33 8.7 6.82 3.94 4 0 5 ... ..$ VAR2 : num [1:297]

Hello, I used the function cor to calculate the pearson correlation coefficient between variables. However, the resulting values do not correspond to the outcome of my excel-calculations, for which I used the formula Cor(x,y)=Cov(x,y)/(SD(x)*SD(y)) So my question is: How does the function "cor" compute the pearson correlation coefficient? Thank you in advance, Ake Nauta

Dear all, The other day I was reading this post [1] that slightly surprised me: "To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) " (BOB SAMOHYL). To me this implied that in

Listers, I have a question regarding correlation matrices. It is fairly straight forward to build a correlation matrix of an entire data frame. I simply use the command cor(MyDataFrame). However, what I would like to do is construct a smaller correlation matrix using just three of the variable out of my data set. When I run this: cor(MyDataFrame$variable1,

Hello, I am running polychoric correlations on a dataset composed of 12 ordinal and binary variables (N =384), using the polycor package. One of the association (between 2 dichotomous variables) is very high using the 2-step estimate (0.933 when polychoric run only between the two variables; but 0.801 when polychoric run on the 12 variables). The same correlation run with ML estimate returns a

How do I get non-squared correlation coefficient in some more sensible way than sqrt(summary(lm(y~x))$r.squared)? Thanks Matej -- Matej Cepl, matej at ceplovi.cz, Finger: 89EF 4BC6 288A BF43 1BAB 25C3 E09F EF25 D964 84AC 138 Highland Ave. #10, Somerville, Ma 02143, (617) 623-1488 The difference between death and taxes is death doesn't get worse every time Congress meets -- Will

Hi Is there a function for computing the standard correlation coefficient (not pearson) in R? Thanks Mick

Hi, there: As you all know, correlation is not a very robust procedure. Sometimes correlation could be driven by a few outliers. There are a few ways to improve the robustness of correlation (pearson correlation), either by outlier removal procedure, or resampling technique. I am wondering if there is any R package or R code that have incorporated outlier removal or resampling procedure in

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers