# somers2

##### Somers' Dxy Rank Correlation

Computes Somers' Dxy rank correlation between a variable `x`

and a
binary (0-1) variable `y`

, and the corresponding receiver operating
characteristic curve area `c`

. Note that `Dxy = 2(c-0.5)`

.
`somers`

allows for a `weights`

variable, which specifies frequencies
to associate with each observation.

- Keywords
- nonparametric

##### Usage

`somers2(x, y, weights=NULL, normwt=FALSE, na.rm=TRUE)`

##### Arguments

- x
typically a predictor variable.

`NA`

s are allowed.- y
a numeric outcome variable coded

`0-1`

.`NA`

s are allowed.- weights
a numeric vector of observation weights (usually frequencies). Omit or specify a zero-length vector to do an unweighted analysis.

- normwt
set to

`TRUE`

to make`weights`

sum to the actual number of non-missing observations.- na.rm
set to

`FALSE`

to suppress checking for NAs.

##### Details

The `rcorr.cens`

function, which although slower than `somers2`

for large
sample sizes, can also be used to obtain Dxy for non-censored binary
`y`

, and it has the advantage of computing the standard deviation of
the correlation index.

##### Value

a vector with the named elements `C`

, `Dxy`

, `n`

(number of non-missing
pairs), and `Missing`

. Uses the formula
`C = (mean(rank(x)[y == 1]) - (n1 + 1)/2)/(n - n1)`

, where `n1`

is the
frequency of `y=1`

.

##### See Also

##### Examples

```
# NOT RUN {
set.seed(1)
predicted <- runif(200)
dead <- sample(0:1, 200, TRUE)
roc.area <- somers2(predicted, dead)["C"]
# Check weights
x <- 1:6
y <- c(0,0,1,0,1,1)
f <- c(3,2,2,3,2,1)
somers2(x, y)
somers2(rep(x, f), rep(y, f))
somers2(x, y, f)
# }
```

*Documentation reproduced from package Hmisc, version 4.3-0, License: GPL (>= 2)*