Calculate skew and kurtosis, standard errors for both, and the estimates divided by two times the standard error. If this latter quantity exceeds an absolute value of 1, the skew/kurtosis is significant. With very large sample sizes, significant skew/kurtosis is common.

skew_kurtosis(x, verbose = FALSE, se = FALSE, ...)

Arguments

x

An object for which a method exists.

verbose

Logical. Whether or not to print messages to the console, Default: FALSE

se

Whether or not to return the standard errors, Default: FALSE

...

Additional arguments to pass to and from functions.

Value

A matrix of skew and kurtosis statistics for x.

Examples

skew_kurtosis(datasets::anscombe)
#>           skew   skew_2se       kurt   kurt_2se
#> x1  0.00000000  0.0000000 -1.5289256 -0.5975093
#> x2  0.00000000  0.0000000 -1.5289256 -0.5975093
#> x3  0.00000000  0.0000000 -1.5289256 -0.5975093
#> x4  2.46691100  1.8669273  4.5206612  1.7666896
#> y1 -0.04837355 -0.0366085 -1.1991228 -0.4686212
#> y2 -0.97869294 -0.7406626 -0.5143191 -0.2009976
#> y3  1.38012040  1.0444578  1.2400439  0.4846133
#> y4  1.12077386  0.8481876  0.6287512  0.2457181