somers2 | R Documentation |

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.

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

`x` |
typically a predictor variable. |

`y` |
a numeric outcome variable coded |

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

`normwt` |
set to |

`na.rm` |
set to |

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.

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`

.

Frank Harrell

Department of Biostatistics

Vanderbilt University School of Medicine

fh@fharrell.com

`concordance`

, `rcorr.cens`

, `rank`

, `wtd.rank`

,

```
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)
```

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