cor
Correlation, Variance, and Covariance (Matrices)

### Description

Calculates the variance of a vector, the variance-covariance (or correlation) matrix of a data matrix, or covariances between matrices or vectors. Converts a variance-covariance matrix to a correlation matrix.

### Usage

    var(x, y = NULL, na.rm = FALSE, use)
    cov(x, y = NULL, use = "everything", method = c("pearson",
        "kendall", "spearman"))
    cor(x, y = NULL, use = "everything", method = c("pearson",
        "kendall", "spearman"))
    cov2cor(V)

### Arguments

x | a numeric or logical vector, matrix, or data frame. If x is a matrix or data frame, columns represent variables and rows represent observations.
V | a square covariance matrix for the cov2cor function. Missing values (NAs) are allowed but result in missing values in the result.
y | a numeric or logical vector, matrix, or data frame where the same number of observations exist in y as in x. If x is a matrix or data frame, columns represent variables and rows represent observations. In this case, you can set y = NULL. For cor() and cov(), y is required if x is a vector.
na.rm | a logical value. If TRUE, missing values (NAs) are removed before computing. Default value is FALSE. If you specify use, the value of na.rm is ignored.
use | a character string that specifies how missing values (NAs) are handled in the computing the results. The value can be one of the following: all.obs means that all observations must be numeric and that missing values (NA)s are not allowed. An error is returned if there are any missing values (NAs) in x or y. complete.obs means that rows that contain a missing value (NA) are ignored. An error is returned if all rows contain at least one missing value. everything means that values for all pairs of columns are computed, but a missing value (NA) is returned for pairs that contain at least one missing value (NA). This is equivalent to supplying na.rm = FALSE and not specifying a value for use. na.or.complete means that rows that contain a missing values (NA) are ignored in the computations. An error is returned if all rows contain at least one missing value. This is equivalent to supplying na.rm = TRUE and not specifying a value for use. pairwise.complete.obs means variances are computed for each variable using all non-missing values, and covariances or correlations for each pair of variables are computed using observations with no missing data for that pair.
method | a character string that specifies the standard method to employ for the computation of the covariance or correlation. Available methods are pearson (the default), kendall, or spearman. pearson means pearson correlation coefficient. kendall means that kendall's tau statistic is used to compute rank correlation coefficient. spearman means that Spearman's rho statistic is used to compute rank correlation coefficient.

Value

- var returns variances
- cor returns correlations
- cov returns covariances
- cov2cor returns a correlation matrix like V

If x is a vector, the return value is a vector where the length is equal to the number of columns in y. If you do not supply y, the length will be 1.
If x is a matrix or a data frame, the return value is a matrix such that the [i,j] element is the covariance (correlation) of x[,i] and either y[,j] or x[,j].
References
Chan, T., Golub, G., and LeVeque, R. (1983). Algorithms for computing the sample variance: analysis and recommendations. The American Statistician 37: 242-247.
Gnanadesikan, R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. New York: Wiley.
Gnanadesikan, R. and Kettenring, J.R. (1972). Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28: 81-124.
Huber, P.J. (1981). Robust Statistics. New York: Wiley.
Little, R.J.A., and Rubin, D.R. (1987). Statistical Analysis with Missing Data. New York: Wiley.
Schafer, J.L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
mad, mean
Examples

    # 7 by 7 correlation matrix for the longley data
    cor(Sdatasets::longley)
    # The same thing
    cov2cor(cov(Sdatasets::longley))
    cor(Sdatasets::longley, method="pearson")
    cor(Sdatasets::longley, method="kendall")
    cor(Sdatasets::longley, method="spearman")
    cov(Sdatasets::longley, method="pearson")
    cov(Sdatasets::longley, method="kendall")
    cov(Sdatasets::longley, method="spearman")
    var(Sdatasets::longley)

Package stats version 6.1.5-18
Package Index