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)
NA
s are allowed.
0-1
.
NA
s are allowed.
TRUE
to make
weights
sum to the actual number of non-missing
observations.
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.
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"]