calc_moments: Find Standardized Cumulants of Data by Method of Moments

In SimMultiCorrData: Simulation of Correlated Data with Multiple Variable Types

Description

This function uses the method of moments to calculate the mean, standard deviation, skewness, standardized kurtosis, and standardized fifth and sixth cumulants given a vector of data. The result can be used as input to find_constants or for data simulation.

Usage

calc_moments(x)

Arguments

x	a vector of data

Value

A vector of the mean, standard deviation, skewness, standardized kurtosis, and standardized fifth and sixth cumulants

References

Headrick TC (2002). Fast Fifth-order Polynomial Transforms for Generating Univariate and Multivariate Non-normal Distributions. Computational Statistics & Data Analysis, 40(4):685-711. doi: 10.1016/S0167-9473(02)00072-5. (ScienceDirect)

Headrick TC, Kowalchuk RK (2007). The Power Method Transformation: Its Probability Density Function, Distribution Function, and Its Further Use for Fitting Data. Journal of Statistical Computation and Simulation, 77, 229-249. doi: 10.1080/10629360600605065.

Headrick TC, Sheng Y, & Hodis FA (2007). Numerical Computing and Graphics for the Power Method Transformation Using Mathematica. Journal of Statistical Software, 19(3), 1 - 17. doi: 10.18637/jss.v019.i03.

Kendall M & Stuart A (1977). The Advanced Theory of Statistics, 4th Edition. Macmillan, New York.

See Also

calc_fisherk, calc_theory, find_constants

Examples

x <- rgamma(n = 10000, 10, 10)
calc_moments(x)

Example output

Attaching package: 'SimMultiCorrData'

The following object is masked from 'package:stats':

    poly

     mean        sd      skew  kurtosis     fifth     sixth 
0.9995045 0.3203714 0.6263213 0.5772957 0.6167336 0.3310216 

SimMultiCorrData documentation built on May 2, 2019, 9:50 a.m.