## File: somers2.Rd

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hmisc 4.2-0-1
 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677 \name{somers2} \alias{somers2} \title{ Somers' Dxy Rank Correlation } \description{ Computes Somers' Dxy rank correlation between a variable \code{x} and a binary (0-1) variable \code{y}, and the corresponding receiver operating characteristic curve area \code{c}. Note that \code{Dxy = 2(c-0.5)}. \code{somers} allows for a \code{weights} variable, which specifies frequencies to associate with each observation. } \usage{ somers2(x, y, weights=NULL, normwt=FALSE, na.rm=TRUE) } \arguments{ \item{x}{ typically a predictor variable. \code{NA}s are allowed. } \item{y}{ a numeric outcome variable coded \code{0-1}. \code{NA}s are allowed. } \item{weights}{ a numeric vector of observation weights (usually frequencies). Omit or specify a zero-length vector to do an unweighted analysis. } \item{normwt}{ set to \code{TRUE} to make \code{weights} sum to the actual number of non-missing observations. } \item{na.rm}{ set to \code{FALSE} to suppress checking for NAs. }} \value{ a vector with the named elements \code{C}, \code{Dxy}, \code{n} (number of non-missing pairs), and \code{Missing}. Uses the formula \code{C = (mean(rank(x)[y == 1]) - (n1 + 1)/2)/(n - n1)}, where \code{n1} is the frequency of \code{y=1}. } \details{ The \code{rcorr.cens} function, which although slower than \code{somers2} for large sample sizes, can also be used to obtain Dxy for non-censored binary \code{y}, and it has the advantage of computing the standard deviation of the correlation index. } \author{ Frank Harrell \cr Department of Biostatistics \cr Vanderbilt University School of Medicine \cr \email{f.harrell@vanderbilt.edu} } \seealso{ \code{\link{rcorr.cens}}, \code{\link{rank}}, \code{\link{wtd.rank}}, } \examples{ 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) } \keyword{nonparametric} \concept{logistic regression model} \concept{predictive accuracy}