somers2 {Hmisc} | 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. 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.
|
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
f.harrell@vanderbilt.edu
set.seed(1) predicted <- runif(200) dead <- sample(0:1, 200, TRUE) roc.area <- somers2(predicted, dead)["C"]