R: Column-wise kurtosis and skewness coefficients

Column-wise kurtosis and skewness coefficients {Rfast} | R Documentation

## Column-wise kurtosis and skewness coefficients

### Description

Column-wise kurtosis and skewness coefficients.

### Usage

    colkurtosis(x, pvalue = FALSE)

    colskewness(x, pvalue = FALSE)

### Arguments

`x` | A matrix with the data, where the rows denote the samples and the columns are the variables.
`pvalue` | If you want a hypothesis test that the skewness or kurtosis are significant set this to TRUE. This checks whether the skewness is significantly different from 0 and whether the kurtosis is significantly different from 3.

### Details

The skewness and kurtosis coefficients are calculated. For the skewness coefficient we use the sample unbiased version of the standard deviation. For the kurtosis, we do not subtract 3.

### Value

If "pvalue" is FALSE, a vector with the relevant coefficient. Otherwise a matrix with two columns. The kurtosis or skewness coefficient and the p-value from the hypothesis test that they are significantly different from 3 or 0 respectively.

### Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr> and Manos Papadakis <papadakm95@gmail.com>.

### See Also

`skew, skew.test2, colMedians, colmeans, colVars, sftests`

### Examples

    ## 200 variables, hence 200 F-tests will be performed
    x = matrix( rnorm(200 * 50), ncol = 50 )
    ## 200 observations in total
    system.time( colkurtosis(x) )
    system.time( colskewness(x) )
    x <- NULL

[Package Rfast version 2.0.4 Index]