kurt: Calculate Univariate Kurtosis for a Vector or Matrix

In fungible: Psychometric Functions from the Waller Lab

Calculate Univariate Kurtosis for a Vector or Matrix

Description

Calculate univariate kurtosis for a vector or matrix (algorithm G2 in Joanes & Gill, 1998). Note that, as defined in this function, the expected kurtosis of a normally distributed variable is 0 (i.e., not 3).

Usage

kurt(x)

Arguments

x	Either a vector or matrix of numeric values.

Value

Kurtosis for each column in x.

Author(s)

Niels Waller

References

Joanes, D. N. & Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. The Statistician, 47, 183-189.

See Also

skew

Examples

x <- matrix(rnorm(1000), 100, 10)
print(kurt(x))

fungible documentation built on March 31, 2023, 5:47 p.m.