var {externalVector}R Documentation

Correlation, Variance and Covariance (Matrices)


var, cov and cor compute the variance of x and the covariance or correlation of x and y if these are vectors. If x and y are matrices then the covariances (or correlations) between the columns of x and the columns of y are computed.

cov2cor scales a covariance matrix into the corresponding correlation matrix efficiently.


var(x, y, na.rm = FALSE, use)

cov(x, y, use = "all.obs",
    method = c("pearson", "kendall", "spearman"))

cor(x, y, use = "all.obs",
     method = c("pearson", "kendall", "spearman"))



x a numeric vector, matrix or data frame.
y NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient).
na.rm logical. Should missing values be removed?
use an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "all.obs", "complete.obs" or "pairwise.complete.obs".
method a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman", can be abbreviated.
V symmetric numeric matrix, usually positive definite such as a covariance matrix.


For cov and cor one must either give a matrix or data frame for x or give both x and y.

var is just another interface to cov, where na.rm is used to determine the default for use when that is unspecified. If na.rm is TRUE then the complete observations (rows) are used (use = "complete") to compute the variance. Otherwise (use = "all"), var will give an error if there are missing values.

If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by casewise deletion. Finally, if use has the value "pairwise.complete.obs" then the correlation between each pair of variables is computed using all complete pairs of observations on those variables. This can result in covariance or correlation matrices which are not positive semidefinite.

The denominator n - 1 is used which gives an unbiased estimator of the (co)variance for i.i.d. observations. These functions return NA when there is only one observation (whereas S-PLUS has been returning NaN), and fail if x has length zero.

For cor(), if method is "kendall" or "spearman", Kendall's tau or Spearman's rho statistic is used to estimate a rank-based measure of association. These are more robust and have be recommended if the data do not necessarily come from a bivariate normal distribution.
For cov(), a non-Pearson method is unusual but available for the sake of completeness. Note that "spearman" basically computes cor(R(x), R(y)) (or cov(.,.)) where R(u) := rank(u, na.last="keep").

Scaling a covariance matrix into a correlation one can be achieved in many ways, mathematically most appealing by multiplication with a diagonal matrix from left and right, or more efficiently by using sweep(.., FUN = "/") twice. The cov2cor function is even a bit more efficient, and provided mostly for didactical reasons.


For r <- cor(*, use = "all.obs"), it is now guaranteed that all(r <= 1).


Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

cor.test (package ctest) for confidence intervals (and tests).

cov.wt for weighted covariance computation.

sd for standard deviation (vectors).


x <- externalNumeric(10)
x[] <- 1:10
var(x)# 9.166667

var(x[1:5],x[1:5])# 2.5

## Two simple vectors
cor(x,x+1)# == 1

[Package externalVector version 1.0.14 Index]