moments: Skewness and (excess) Kurtosis of a vector of values
In propagate: Propagation of Uncertainty

These functions calculate skewness and excess kurtosis of a vector of values. They were taken from the package 'moments'.

1 2	skewness(x, na.rm = FALSE) kurtosis(x, na.rm = FALSE)

`x`	a numeric vector, matrix or data frame.
`na.rm`	logical. Should missing values be removed?

Skewness:

\frac{\frac{1}{n} ∑_{i=1}^n (x_i-\overline{x})^3}{≤ft(\frac{1}{n} ∑_{i=1}^n (x_i-\overline{x})^2\right)^{3/2}}

(excess) Kurtosis:

\frac{\frac{1}{n} ∑_{i=1}^n (x_i - \overline{x})^4}{≤ft(\frac{1}{n} ∑_{i=1}^n (x_i - \overline{x})^2\right)^2} - 3

The skewness/kurtosis values.

Andrej-Nikolai Spiess

1
2
3

X <- rnorm(100, 20, 2)
skewness(X)
kurtosis(X)

propagate documentation built on May 2, 2019, 5:39 a.m.

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