`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 `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)

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

`"everything"`

, `"all.obs"`

, `"complete.obs"`

,
`"na.or.complete"`

, 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.

`r <- cor(*, use = "all.obs")`

, it is now guaranteed that
`all(abs(r) <= 1)`

.`cov`

and `cor`

one must `x`

`x`

and `y`

. The inputs must be numeric (as determined by `is.numeric`

:
logical values are also allowed for historical compatibility): the
`"kendall"`

and `"spearman"`

methods make sense for ordered
inputs but `xtfrm`

can be used to find a suitable prior
transformation to numbers. `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 = "na.or.complete"`

) to
compute the variance. Otherwise, by default `use = "everything"`

. If `use`

is `"everything"`

, `NA`

s will
propagate conceptually, i.e., a resulting value will be `NA`

whenever one of its contributing observations is `NA`

.
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 (and if there are no complete cases, that gives an error). `"na.or.complete"`

is the same unless there are no complete
cases, that gives `NA`

.
Finally, if `use`

has the value `"pairwise.complete.obs"`

then the correlation or covariance 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
semi-definite, as well as `NA`

entries if there are no complete
pairs for that pair of variables. For `cov`

and `var`

,
`"pairwise.complete.obs"`

only works with the `"pearson"`

method.
Note that (the equivalent of) `var(double(0), use = *)`

gives
`NA`

for `use = "everything"`

and `"na.or.complete"`

,
and gives an error in the other cases. 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 been 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")
```

. In the case of missing values, the
ranks are calculated depending on the value of `use`

, either
based on complete observations, or based on pairwise completeness with
reranking for each pair. When there are ties, Kendall's \(\tau_b\) is computed, as
proposed by Kendall (1945). 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.`cor.test`

for confidence intervals (and tests). `cov.wt`

for `sd`

for standard deviation (vectors).var(1:10) # 9.166667 var(1:5, 1:5) # 2.5 ## Two simple vectors cor(1:10, 2:11) # == 1 ## Correlation Matrix of Multivariate sample: (Cl <- cor(longley)) ## Graphical Correlation Matrix: symnum(Cl) # highly correlated ## Spearman's rho and Kendall's tau symnum(clS <- cor(longley, method = "spearman")) symnum(clK <- cor(longley, method = "kendall")) ## How much do they differ? i <- lower.tri(Cl) cor(cbind(P = Cl[i], S = clS[i], K = clK[i])) ## cov2cor() scales a covariance matrix by its diagonal ## to become the correlation matrix. cov2cor # see the function definition {and learn ..} stopifnot(all.equal(Cl, cov2cor(cov(longley))), all.equal(cor(longley, method = "kendall"), cov2cor(cov(longley, method = "kendall")))) ##--- Missing value treatment: <!-- % "everything", "all.obs", "complete.obs", "na.or.complete", "pairwise.complete.obs" --> C1 <- cov(swiss) range(eigen(C1, only.values = TRUE)$values) # 6.19 1921 ## swM := "swiss" with 3 "missing"s : swM <- swiss colnames(swM) <- abbreviate(colnames(swiss), min=6) swM[1,2] <- swM[7,3] <- swM[25,5] <- NA # create 3 "missing" ## Consider all 5 "use" cases : (C. <- cov(swM)) # use="everything" quite a few NA's in cov.matrix try(cov(swM, use = "all")) # Error: missing obs... C2 <- cov(swM, use = "complete") stopifnot(identical(C2, cov(swM, use = "na.or.complete"))) range(eigen(C2, only.values = TRUE)$values) # 6.46 1930 C3 <- cov(swM, use = "pairwise") range(eigen(C3, only.values = TRUE)$values) # 6.19 1938 ## Kendall's tau doesn't change much: symnum(Rc <- cor(swM, method = "kendall", use = "complete")) symnum(Rp <- cor(swM, method = "kendall", use = "pairwise")) symnum(R. <- cor(swiss, method = "kendall")) ## "pairwise" is closer componentwise, summary(abs(c(1 - Rp/R.))) summary(abs(c(1 - Rc/R.))) ## but "complete" is closer in Eigen space: EV <- function(m) eigen(m, only.values=TRUE)$values summary(abs(1 - EV(Rp)/EV(R.)) / abs(1 - EV(Rc)/EV(R.)))