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Calculate bias-adjusted sample skewness

Source: R/calcSkewness.R
calcSkewness.Rd

Part of the Genetic Value Analysis

Usage

calcSkewness(x, na.rm = TRUE)

Arguments

x

A numeric vector.

na.rm

Logical; remove NA values before computing. Default TRUE.

Value

The bias-adjusted sample skewness, a single number; NA when degenerate (see Details).

Details

The bias-adjusted Fisher-Pearson standardized skewness coefficient, G1 = g1 * sqrt(n * (n - 1)) / (n - 2), where g1 = m3 / m2^1.5 and m2/m3 are the second/third central sample moments of x – the "Method 2" adjustment of Joanes and Gill (1998), the same convention SPSS, SAS, and Excel report by default and the type = 2 option in the moments/e1071 CRAN packages. A positive value indicates a longer right tail; negative, a longer left tail; 0, a symmetric distribution.

Returns NA when x has fewer than 3 non-NA values (n <= 2, the adjustment term divides by n - 2) or has zero variance (all remaining values identical) – both would otherwise divide by zero.

References

Joanes, D.N. and Gill, C.A. (1998) "Comparing measures of sample skewness and kurtosis." Journal of the Royal Statistical Society: Series D (The Statistician), 47(1), 183-189.

See also

calcKurtosis

Other genetic value analysis: calcA(), calcFE(), calcFEFG(), calcFG(), calcFGSE(), calcGU(), calcGUSE(), calcGeneDiversity(), calcKurtosis(), calcNeSexRatio(), calcNeVariance(), calcRetention()

Examples

calcSkewness(c(1, 2, 3, 4, 100)) # 2.232396, a long right tail
#> [1] 2.232396
calcSkewness(c(1, 2, 3, 4, 5)) # 0, symmetric
#> [1] 0