Hmisc (version 4.5-0)

# somers2: Somers' Dxy Rank Correlation

## Description

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.

## 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.

## 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`.

## 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.

## See Also

`rcorr.cens`, `rank`, `wtd.rank`,

## 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)
# }
```