Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
When negative ('left-skewed'): the left tail is longer; the mass of the distribution is concentrated on the right of a histogram. When positive ('right-skewed'): the right tail is longer; the mass of the distribution is concentrated on the left of a histogram. A normal distribution has a skewness of 0.
skewness(x, na.rm = FALSE)
# S3 method for default
skewness(x, na.rm = FALSE)
# S3 method for matrix
skewness(x, na.rm = FALSE)
# S3 method for data.frame
skewness(x, na.rm = FALSE)Arguments
- x
a vector of values, a matrix or a data.frame
- na.rm
a logical value indicating whether
NAvalues should be stripped before the computation proceeds
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, an argument 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 data analysis, the complete documentation of all functions and an example analysis using WHONET data.