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\name{mad}\title{Median Absolute Deviation}\usage{mad(x, center = median(x), constant = 1.4826, na.rm = FALSE,low = FALSE, high = FALSE)}\alias{mad}\arguments{\item{x}{a numeric vector.}\item{center}{Optionally, the centre: defaults to the median.}\item{constant}{scale factor.}\item{na.rm}{if \code{TRUE} then \code{NA} values are strippedfrom \code{x} before computation takes place.}\item{low}{if \code{TRUE}, compute the \dQuote{lo-median}, i.e., for evensample size, do not average the two middle values, but take thesmaller one.}\item{high}{if \code{TRUE}, compute the \dQuote{hi-median}, i.e., take thelarger of the two middle values for even sample size.}}\description{Compute the median absolute deviation, i.e., the (lo-/hi-) median ofthe absolute deviations from the median, and (by default) adjust by afactor for asymptotically normal consistency.}\details{The actual value calculated is \code{constant * cMedian(abs(x - center))}with the default value of \code{center} being \code{median(x)}, and\code{cMedian} being the usual, the \dQuote{low} or \dQuote{high} median, seethe arguments description for \code{low} and \code{high} above.The default \code{constant = 1.4826} (approximately\eqn{1/\Phi^{-1}(\frac 3 4)}{1/ Phi^(-1)(3/4)} = \code{1/qnorm(3/4)})ensures consistency, i.e.,\deqn{E[mad(X_1,\dots,X_n)] = \sigma}for \eqn{X_i} distributed as \eqn{N(\mu,\sigma^2)} and large \eqn{n}.If \code{na.rm} is \code{TRUE} then \code{NA}values are stripped from \code{x} before computation takes place.If this is not done then an \code{NA} value in\code{x} will cause \code{mad} to return \code{NA}.}\seealso{\code{\link{IQR}} which is simpler but less robust,\code{\link{median}}, \code{\link{var}}.}\examples{mad(c(1:9))print(mad(c(1:9), constant=1)) ==mad(c(1:8,100), constant=1) # = 2 ; TRUEx <- c(1,2,3, 5,7,8)sort(abs(x - median(x)))c(mad(x, co=1), mad(x, co=1, lo = TRUE), mad(x, co=1, hi = TRUE))}\keyword{univar}\keyword{robust}