Calculate skew and kurtosis — skew

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.

Usage

skew_kurtosis(
  x,
  verbose = FALSE,
  se = FALSE,
  pearson = FALSE,
  corrected = TRUE,
  ...
)

Arguments

x: An object for which a method exists.
verbose: Logical. Whether or not to print messages to the console, Default: FALSE
se: Logical. Whether or not to return the standard errors, Default: FALSE
pearson: Logical. Whether or not to return the Pearson's kurtosis alongside excess kurtosis, Default: FALSE
corrected: Logical. Whether or not to correct for bias in skew and kurtosis (Joanes & Gill, 1998). Corrects both the estimates and their standard errors. Default: TRUE
...: Additional arguments to pass to and from functions.

Value

A matrix of skew and kurtosis statistics for x. The columns labeled _2se contain the test statistic divided by two times its standard error.

References

Joanes, D. N. & 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. doi:10.1111/1467-9884.00122

Examples

skew_kurtosis(datasets::anscombe)
#>           skew    skew_2se       kurt   kurt_2se
#> x1  0.00000000  0.00000000 -1.2000000 -0.4689640
#> x2  0.00000000  0.00000000 -1.2000000 -0.4689640
#> x3  0.00000000  0.00000000 -1.2000000 -0.4689640
#> x4  3.31662479  2.50998008 11.0000000  4.2988371
#> y1 -0.06503555 -0.04921809 -0.5348977 -0.2090398
#> y2 -1.31579829 -0.99577966  0.8461232  0.3306678
#> y3  1.85549520  1.40421552  4.3840886  1.7133166
#> y4  1.50681818  1.14034112  3.1513149  1.2315445