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