Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. A normal distribution has a kurtosis of 3 and a excess kurtosis of 0.
kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)
Arguments
| x | a vector of values, a |
|---|---|
| na.rm | a logical to indicate whether |
| excess | a logical to indicate whether the excess kurtosis should be returned, defined as the kurtosis minus 3. |
Stable lifecycle
The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
Read more on our website!
On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!