| cor {base} | R Documentation |
Correlation, Variance and Covariance (Matrices)
Description
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
covariance (correlation) between the columns of x and the
columns of y are computed.
Usage
var(x, y = NULL, na.rm = FALSE, use)
cor(x, y = NULL, use = "all.obs")
cov(x, y = NULL, use = "all.obs")
Arguments
x |
a numeric vector, matrix or data frame. |
y |
|
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
|
Details
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.
See Also
cov.wt for weighted covariance computation.
Examples
var(1:10)# 9.166667
var(1:5,1:5)# 2.5
## Two simple vectors
cor(1:10,2:11)# == 1
## var() & cov() are "really the same":
stopifnot(var(1:5,0:4) == cov(1:5))
## Correlation Matrix of Multivariate sample:
data(longley)
(Cl <- cor(longley))
## Graphical Correlation Matrix:
symnum(Cl) # highly correlated
##--- Missing value treatment:
data(swiss)
C1 <- cov(swiss)
range(eigen(C1, only=T)$val) # 6.19 1921
swiss[1,2] <- swiss[7,3] <- swiss[25,5] <- NA # create 3 "missing"
## Not run:
C2 <- cov(swiss) # Error: missing obs...
## End(Not run)
C2 <- cov(swiss, use = "complete")
range(eigen(C2, only=T)$val) # 6.46 1930
C3 <- cov(swiss, use = "pairwise")
range(eigen(C3, only=T)$val) # 6.19 1938