skewness: Skewness and Kurtosis

Description

The function skewness computes the skewness, the function kurtosis computes the kurtosis.

Usage

skewness(..., data = NULL, as.na = NULL, check = TRUE)
kurtosis(..., data = NULL, as.na = NULL, check = TRUE)

Value

Returns the estimated skewness or kurtosis of x.

Arguments

...: a numeric vector. Alternatively, an expression indicating the variable names in data e.g., skewness(x1, data = dat).
data: a data frame when specifying the variable in the argument .... Note that the argument is NULL when specifying a numeric vector for the argument ....
as.na: a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis.
check: logical: if TRUE (default), argument specification is checked.

Author

Takuya Yanagida takuya.yanagida@univie.ac.at

Details

The same method for estimating skewness and kurtosis is used in SAS and SPSS. Missing values (NA) are stripped before the computation. Note that at least 3 observations are needed to compute skewness and at least 4 observations are needed to compute excess kurtosis.

References

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. New York: John Wiley & Sons.

Examples

Run this code

# Set seed of the random number generation
set.seed(123)
# Generate random numbers according to N(0, 1)
x <- rnorm(100)

# Example 1: Compute skewness
skewness(x)

# Example 2: Compute excess kurtosis
kurtosis(x)

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