lehmer_mean: Lehmer mean

In marberts/gpindex: Generalized Price and Quantity Indexes

Lehmer mean

Description

Calculate a weighted Lehmer mean.

Usage

lehmer_mean(r)

contraharmonic_mean(x, w = NULL, na.rm = FALSE)

Arguments

r	A finite number giving the order of the Lehmer mean.
x	A strictly positive numeric vector.
w	A strictly positive numeric vector of weights, the same length as x. The default is to equally weight each element of x.
na.rm	Should missing values in x and w be removed? By default missing values in x or w return a missing value.

Details

The function lehmer_mean() returns a function to compute the Lehmer mean of order r of x with weights w, which is calculated as the arithmetic mean of x with weights wx^{r-1}. This is also called the counter-harmonic mean or generalized anti-harmonic mean. See Bullen (2003, p. 245) for details.

The Lehmer mean of order 2 is sometimes called the contraharmonic (or anti-harmonic) mean. The function contraharmonic_mean() simply calls lehmer_mean(2)(). See von der Lippe (2015) for more details on the use of these means for making price indexes.

Value

lehmer_mean() returns a function:

function(x, w = NULL, na.rm = FALSE){...}

contraharmonic_mean() returns a numeric value for the Lehmer mean of order 2.

Note

lehmer_mean() can be defined on the extended real line, so that r = -Inf / Inf returns min()/max(), to agree with the definition in, e.g., Bullen (2003). This is not implemented, and r must be finite.

References

Bullen, P. S. (2003). Handbook of Means and Their Inequalities. Springer Science+Business Media.

von der Lippe, P. (2015). Generalized Statistical Means and New Price Index Formulas, Notes on some unexplored index formulas, their interpretations and generalizations. Munich Personal RePEc Archive paper no. 64952.

See Also

Other means: extended_mean(), generalized_mean(), nested_mean()

Examples

x <- 2:3
w <- c(0.25, 0.75)

# The Pythagorean means are special cases of the Lehmer mean.

all.equal(lehmer_mean(1)(x, w), arithmetic_mean(x, w))
all.equal(lehmer_mean(0)(x, w), harmonic_mean(x, w))
all.equal(lehmer_mean(0.5)(x), geometric_mean(x))

# When r < 1, the generalized mean is larger than the corresponding
# Lehmer mean.

lehmer_mean(-1)(x, w) < generalized_mean(-1)(x, w)

# The reverse is true when r > 1.

lehmer_mean(3)(x, w) > generalized_mean(3)(x, w)

# This implies the contraharmonic mean is larger than the quadratic
# mean, and therefore the Pythagorean means.

contraharmonic_mean(x, w) > arithmetic_mean(x, w)
contraharmonic_mean(x, w) > geometric_mean(x, w)
contraharmonic_mean(x, w) > harmonic_mean(x, w)

# ... and the logarithmic mean

contraharmonic_mean(2:3) > logmean(2, 3)

# The difference between the arithmetic mean and contraharmonic mean
# is proportional to the variance of x.

weighted_var <- function(x, w) {
  arithmetic_mean((x - arithmetic_mean(x, w))^2, w)
}

arithmetic_mean(x, w) + weighted_var(x, w) / arithmetic_mean(x, w)
contraharmonic_mean(x, w)

marberts/gpindex documentation built on June 10, 2025, 5:06 a.m.