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(Weighted) Arithmetic Mean

Source: R/meanX.r
meanX.Rd

Generic function for the (trimmed) arithmetic mean, possibly with given weights.

Usage

meanX(x, ...)

# S3 method for class 'Freq'
meanX(x, breaks, ...)

# Default S3 method
meanX(x, weights = NULL, trim = 0, na.rm = FALSE, ...)

Arguments

x

an object. Currently there are methods for numeric/logical vectors and date, date-time and time interval objects. Complex vectors are allowed for trim = 0 only.

...

further arguments passed to or from other methods

breaks

breaks for calculating the mean for classified data as composed by freq()

weights

non-negative numeric vector of weights of the same length as x, interpreted as frequency (replication) weights. Observations with larger weights contribute more strongly to the empirical distribution.

trim

the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds

Value

if trim is zero (the default), the arithmetic mean of the values in x is computed, as a numeric or complex vector of length one. If x is not logical (coerced to numeric), numeric (including integer) or complex, NA_real_ is returned, with a warning.

If trim is non-zero, a symmetrically trimmed mean is computed with a fraction of trim observations deleted from each end before the mean is computed.

Details

The argument weights is interpreted as frequency (replication) weights. Conceptually, this corresponds to computing the statistic on a reweighted empirical distribution, where observations with larger weights represent a higher frequency in the population.

**Note:** Analytic (precision) weights, which assume observation-specific error variances or likelihood-based weighting, are deliberately not supported. This design ensures that all weighted statistics remain well-defined for ordinal, robust, and distribution-based measures such as medians, quantiles, and measures of dispersion.

trim and weights can't be used together at the same time.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See also

weighted.mean(), mean.POSIXct(), colMeans() for row and column means.

Other location: gmean(), hmean(), hodgesLehmann(), huberM(), medianX(), modeX(), tukeyBiweight()

Examples


x <- c(0:10, 50)
xm <- meanX(x)
c(xm, meanX(x, trim = 0.10))
#> [1] 8.75 5.50