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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/weightedMean.R
\name{weightedMean}
\alias{weightedMean}
\title{Weighted Arithmetic Mean}
\usage{
weightedMean(x, w = NULL, idxs = NULL, na.rm = FALSE, refine = FALSE,
...)
}
\arguments{
\item{x}{An NxK \code{\link[base]{matrix}} or, if \code{dim.} is specified,
an N * K \code{\link[base]{vector}}.}
\item{w}{a vector of weights the same length as \code{x} giving the weights
to use for each element of \code{x}. Negative weights are treated as zero
weights. Default value is equal weight to all values.
If a missing-value weight exists, the result is always a missing value.}
\item{idxs}{A \code{\link[base]{vector}} indicating subset of elements to
operate over. If \code{\link[base]{NULL}}, no subsetting is done.}
\item{na.rm}{If \code{\link[base:logical]{TRUE}}, missing values are
excluded.}
\item{refine}{If \code{\link[base:logical]{TRUE}} and \code{x} is
\code{\link[base]{numeric}}, then extra effort is used to calculate the
average with greater numerical precision, otherwise not.}
\item{...}{Not used.}
}
\value{
Returns a \code{\link[base]{numeric}} scalar. If \code{x} is of
zero length, then \code{NaN} is returned, which is consistent with
\code{\link[base]{mean}}().
}
\description{
Computes the weighted sample mean of a numeric vector.
}
\section{Missing values}{
This function handles missing values consistently with
\code{\link[stats]{weighted.mean}}. More precisely, if \code{na.rm = FALSE},
then any missing values in either \code{x} or \code{w} will give result
\code{NA_real_}. If \code{na.rm = TRUE}, then all \code{(x, w)} data points
for which \code{x} is missing are skipped. Note that if both \code{x} and
\code{w} are missing for a data points, then it is also skipped (by the same
rule). However, if only \code{w} is missing, then the final results will
always be \code{NA_real_} regardless of \code{na.rm}.
}
\examples{
x <- 1:10
n <- length(x)
w <- rep(1, times = n)
m0 <- weighted.mean(x, w)
m1 <- weightedMean(x, w)
stopifnot(identical(m1, m0))
# Pull the mean towards zero
w[1] <- 5
m0 <- weighted.mean(x, w)
m1 <- weightedMean(x, w)
stopifnot(identical(m1, m0))
# Put even more weight on the zero
w[1] <- 8.5
m0 <- weighted.mean(x, w)
m1 <- weightedMean(x, w)
stopifnot(identical(m1, m0))
# All weight on the first value
w[1] <- Inf
m0 <- weighted.mean(x, w)
m1 <- weightedMean(x, w)
stopifnot(identical(m1, m0))
# All weight on the last value
w[1] <- 1
w[n] <- Inf
m0 <- weighted.mean(x, w)
m1 <- weightedMean(x, w)
stopifnot(identical(m1, m0))
# All weights set to zero
w <- rep(0, times = n)
m0 <- weighted.mean(x, w)
m1 <- weightedMean(x, w)
stopifnot(identical(m1, m0))
}
\seealso{
\code{\link[base]{mean}}() and \code{\link[stats]{weighted.mean}}.
}
\author{
Henrik Bengtsson
}
\keyword{robust}
\keyword{univar}
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