Matrix of Correlations and P-values
rcorr Computes a matrix of Pearson's r or Spearman's rho rank correlation coefficients for all possible pairs of columns of a matrix. Missing values are deleted in pairs rather than deleting all rows of x having any missing variables. Ranks are computed using efficient algorithms (see reference 2), using midranks for ties.
rcorr(x, y, type=c("pearson","spearman")) ## S3 method for class 'rcorr' print(x, ...)
x: a numeric matrix with at least 5 rows and at least 2 columns (if y is absent). For print, x is an object produced by rcorr.y: a numeric vector or matrix which will be concatenated to x. If y is omitted for rcorr, x must be a matrix.type: specifies the type of correlations to compute. Spearman correlations are the Pearson linear correlations computed on the ranks of non-missing elements, using midranks for ties....: argument for method compatiblity.rcorr returns a list with elements r, the matrix of correlations, n the matrix of number of observations used in analyzing each pair of variables, P, the asymptotic P-values, and type. Pairs with fewer than 2 non-missing values have the r values set to NA. The diagonals of n are the number of non-NAs for the single variable corresponding to that row and column.
Uses midranks in case of ties, as described by Hollander and Wolfe. P-values are approximated by using the t or F distributions.
Frank Harrell
Department of Biostatistics
Vanderbilt University
Hollander M. and Wolfe D.A. (1973). Nonparametric Statistical Methods. New York: Wiley.
Press WH, Flannery BP, Teukolsky SA, Vetterling, WT (1988): Numerical Recipes in C. Cambridge: Cambridge University Press.
hoeffd, cor, combine.levels, varclus, dotchart3, impute, chisq.test, cut2.
x <- c(-2, -1, 0, 1, 2) y <- c(4, 1, 0, 1, 4) z <- c(1, 2, 3, 4, NA) v <- c(1, 2, 3, 4, 5) rcorr(cbind(x,y,z,v))