similar to: correlation matrix in Hmisc

Dear all, in package Hmisc `rcorr' computes a matrix of Spearman's `rho' rank correlation coefficients for all possible pairs of columns of a matrix. What if I want a matrix of rank correlation coefficients for pair of columns of two different matrices? I have the same question about distance metrics in package Vegan. The function 'vegdist' computes distance indexes for all

Greetings. There seems to be a problem with the P-value computation in the cor.test with method="spearman". In R1.8.0 (MS Windows) I seem to be getting intermittently nonsense P-values, but the rho's are OK. I can get this reproducibly with the toy example attached where the first use is OK and subsequent calls with the same data give nonsense. (I have also seen the problem

Dear list, I get the following message when I use rcorr in library "Hmisc" ------------------------------------------------------ > rcorr(lskPox0t30, type=c("spearman")) Error in "[<-.data.frame"(*tmp*, is.na(x), value = 1e+30) : matrix subscripts not allowed in replacement ------------------------------------------------------ I do not understand

Hi, I've just run an rcorr on some data in Spearman's mode and it's just produced the following values; [,1] [,2] [1,] 1.00 -0.55 [2,] -0.55 1.00 n= 46 P [,1] [,2] [1,] 0 [2,] 0 I presume this means the p-value is lower than 0.00005, but is there any way of increasing the number of significant figures used? How should I interpret this value? Cheers Jim

Hi, I have a dataset with four categories of data, the number of samples are not the same in each category. I want to find the Spearaman's Rho. Let me give an example. x=(14.22770439,26.49420624,46.7277932,19.02550707,23.37379361,16.97789862,19.77100085,23.11270162,13.72929843,33.54430621,14.4756979,70.15811106,11.22789833,NA,NA,NA)

Hi, Is there a limit on the number of observations for using cor.test. For example, > library(ctest) > cor.test(rnorm(3000), rnorm(3000), method="spearman") Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value where logical needed In addition: Warning message: NAs introduced by coercion I mainly want to calculate

Hello R-User! I have a data.frame with 82 variables (columns) and 290 rows. The variables are set to classes factor, ordered factor and numeric. I used the following code Matrix.My.data<-as.matrix(Df.My.Data[2:82]) Matrix.My.data.rcorr<-rcorr(Matrix.My.data, type="spearman") and got the following message: Fehler in storage.mode(x) <- if (.R.) "double" else

Dear all ?Hmisc::rcorr states that it takes as main argument "a numeric matrix". But is it normal that it fails in such an ugly way on a data frame? (See below.) If the function didn't attempt any conversion to a matrix, I would have expected it to state that in the error message that it didn't accept 'data.frame' objects in its input. Also, I vaguely remember having used

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 R-help, This is an example in the {Hmisc} manual under rcorr.cens function: > set.seed(1) > x <- round(rnorm(200)) > y <- rnorm(200) > round(rcorr.cens(x, y, outx=F),4) C Index Dxy S.D. n missing uncensored Relevant Pairs Concordant Uncertain 0.4831 -0.0338 0.0462 200.0000

Hallo, does anybody know if there is an implementation of the Spearman rank correlation in R that gives a correct (or at least 'safe') p-value in the case of ties?? I have browsed the R-help archives but I found nothing. Thanks a lot in advance for any help, Antonino Casile

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

Hello R gurus, I want to calculate the Spearman rho between two ranked lists. I am getting results with cor.test that differ in comparison to my own spearman function: > my.spearman function(l1, l2) { if(length(l1) != length(l2)) stop("lists must have same length") r1 <- rank(l1) r2 <- rank(l2) dsq <- sapply(r1-r2,function(x) x^2) 1 - ((6 * sum(dsq))

Hello all, I've run a Spearman's Rank test to discern relationships between landscape characteristics and a specific aspect of river behaviour. I've executed a correlation matrix between the one dependent variable and all of the predictors, which gives me a nice output of Spearman's Rho values. However, I also need to somehow find the "p" value, to assess the strength

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

Hi. I am currently trying to run some Spearman correlations, and have encountered two issues. 1) When using cor.test() with a variable that includes ties, I get the "Cannot compute exact p-values with ties" error. I have read that this function now uses an asymptotic formula that allows for ties, so do not understand why I am getting this error. (I am running version 2.4.0.) I

I have two data matrices that I want to make the correlation between each column from data1 and each column from data 2 and also calculate the p-value Matrices dont have the same size and I tried such a script. > bg <- read.table (file.choose(), header=T, row.names) > bg > Otu00022 Otu00029 Otu00039 Otu00042 Otu00101 Otu00105 Otu00125 Otu00131 Otu00137 Otu00155 Otu00158 Otu00172

Frank Harrell suggested I re-post with information about the version of R Heres's the information: > > version > _ > platform powerpc-apple-darwin6.8 > arch powerpc > os darwin6.8 > system powerpc, darwin6.8 > status > major 2 > minor 0.1 > year 2004 > month 11 > day 15 > language R Here's what happens

Dear R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not

Hi, I am not really familiar with Correlation foundations, although I read a lot. So maybe if someone kindly help me to interpret the following results. I had the following R commands: correlation <-cor( vector_CitationProximity , vector_Impact, method = "spearman", use="na.or.complete") cor_test<-cor.test(vector_CitationProximity, vector_Impact,