# rcorr

##### Matrix of Correlations and Generalized Spearman Rank Correlation

`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.

`spearman2`

computes the square of Spearman's rho rank correlation
and a generalization of it in which `x`

can relate
non-monotonically to `y`

. This is done by computing the Spearman
multiple rho-squared between `(rank(x), rank(x)^2)`

and `y`

.
When `x`

is categorical, a different kind of Spearman correlation
used in the Kruskal-Wallis test is computed (and `spearman2`

can do
the Kruskal-Wallis test). This is done by computing the ordinary
multiple `R^2`

between `k-1`

dummy variables and
`rank(y)`

, where `x`

has `k`

categories. `x`

can
also be a formula, in which case each predictor is correlated separately
with `y`

, using non-missing observations for that predictor.
`print`

and `plot`

methods allow one to easily print or plot
the results of `spearman2(formula)`

. The adjusted `rho^2`

is
also computed, using the same formula used for the ordinary adjusted
`R^2`

. The `F`

test uses the unadjusted R2. For `plot`

,
a dot chart is drawn which by default shows, in sorted order, the
adjusted `rho^2`

.

`spearman`

computes Spearman's rho on non-missing values of two
variables. `spearman.test`

is a simple version of `spearman2.default`

.

- Keywords
- htest, nonparametric

##### Usage

`rcorr(x, y, type=c("pearson","spearman"))`## S3 method for class 'rcorr':
print(x, ...)

spearman2(x, ...)

## S3 method for class 'default':
spearman2(x, y, p=1, minlev=0, exclude.imputed=TRUE, ...)

## S3 method for class 'formula':
spearman2(x, p=1,
data, subset, na.action, minlev=0, exclude.imputed=TRUE, ...)

## S3 method for class 'spearman2.formula':
print(x, ...)

## S3 method for class 'spearman2.formula':
plot(x, what=c('Adjusted rho2','rho2','P'),
sort.=TRUE, main, xlab, ...)

spearman(x, y)

spearman.test(x, y, p=1)

##### Arguments

- x
- a numeric matrix with at least 5 rows and at least 2 columns (if
`y`

is absent). For`spearman2`

, the first argument may be a vector of any type, including character or factor. The first argument may also be a formula, in which cas - 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.
- y
- a numeric vector or matrix which will be concatenated to
`x`

. If`y`

is omitted for`rcorr`

,`x`

must be a matrix. - p
- for numeric variables, specifies the order of the Spearman
`rho^2`

to use. The default is`p=1`

to compute the ordinary`rho^2`

. Use`p=2`

to compute the quadratic rank generalization to allow non-monotonicity - data, subset, na.action
- the usual options for models. Default for
`na.action`

is to retain all values, NA or not, so that NAs can be deleted in only a pairwise fashion. - minlev
- minimum relative frequency that a level of a categorical predictor
should have before it is pooled with other categories (see
`combine.levels`

) in`spearman2`

. The default,`minlev=0`

causes no pooling. - exclude.imputed
- set to
`FALSE`

to include imputed values (created by`impute`

) in the calculations. - what
- specifies which statistic to plot
- sort.
- set
`sort.=FALSE`

to suppress sorting variables by the statistic being plotted - main
- main title for plot. Default title shows the name of the response variable.
- xlab
- x-axis label. Default constructed from
`what`

. - ...
- other arguments that are passed to
`dotchart2`

##### Details

Uses midranks in case of ties, as described by Hollander and Wolfe.
P-values are approximated by using the `t`

distribution.

##### Value

`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.`spearman2.default`

(the function that is called for a single`x`

, i.e., when there is no formula) returns a vector of statistics for the variable.`spearman2.formula`

returns a matrix with rows corresponding to predictors.

##### References

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.

##### See Also

##### Examples

```
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))
spearman2(x, y)
plot(spearman2(z ~ x + y + v, p=2))
```

*Documentation reproduced from package Hmisc, version 2.0-0, License: GPL version 2 or newer*

### Community examples

**catherine.crp@gmail.com**at Oct 9, 2019 Hmisc v4.2-0

```{r} #install.packages("corrplot") library(corrplot) library(Hmisc) matriz <-rcorr(as.matrix(df), type=c("spearman")) matriz ```