`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, …)

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,
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.

`hoeffd`

, `cor`

, `combine.levels`

,
`varclus`

, `dotchart3`

, `impute`

,
`chisq.test`

, `cut2`

.

# NOT RUN { 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)) # }