kurt: Calculate Univariate Kurtosis for a Vector or Matrix
In fungible: Psychometric Functions from the Waller Lab
kurt R Documentation
Calculate Univariate Kurtosis for a Vector or Matrix
Description
Calculate univariate kurtosis for a vector or matrix (algorithm G2 in Joanes
& Gill, 1998). Note that, as defined in this function, the expected kurtosis of
a normally distributed variable is 0 (i.e., not 3).
Usage
kurt(x)
Arguments
x
Either a vector or matrix of numeric values.
Value
Kurtosis for each column in x.
Author(s)
Niels Waller
References
Joanes, D. N. & Gill, C. A. (1998). Comparing measures of sample
skewness and kurtosis. The Statistician, 47, 183-189.
See Also
skew
Examples
x <- matrix(rnorm(1000), 100, 10)
print(kurt(x))
fungible documentation built on May 29, 2024, 8:28 a.m.
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| kurt | R Documentation |
Calculate Univariate Kurtosis for a Vector or Matrix
Description
Calculate univariate kurtosis for a vector or matrix (algorithm G2 in Joanes & Gill, 1998). Note that, as defined in this function, the expected kurtosis of a normally distributed variable is 0 (i.e., not 3).
Usage
kurt(x)
Arguments
x |
Either a vector or matrix of numeric values. |
Value
Kurtosis for each column in x. |
Author(s)
Niels Waller
References
Joanes, D. N. & Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. The Statistician, 47, 183-189.
See Also
skew
Examples
x <- matrix(rnorm(1000), 100, 10)
print(kurt(x))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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