Hmisc (version 4.0-3)

rcorr: 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 rcorr print(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.


a numeric vector or matrix which will be concatenated to x. If y is omitted for rcorr, x must be a matrix.


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, and P, the asymptotic P-values. 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.


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.

See Also

hoeffd, cor, combine.levels, varclus, dotchart3, impute, chisq.test, cut2.


Run this code
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)
# }

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