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Random Variables Using the IG method.

Source: R/rig_rs.R
rig_rs.Rd

Generate multivariate random numbers using the IG method.

Usage

rig_rs(
  n = 10,
  sigma = diag(1),
  skew = 0,
  kurt = 0,
  rIG_args = list(),
  pmean = 0,
  psd = 1
)

Arguments

n

The number of cases to generate.

sigma

The population covariance matrix. It is recommended to supply a correlation matrix (with diagonal elements equal to one). The numbers will be rescaled to have population means and SDs specified by pmean and psd, respectively.

skew

A numeric vector of the population skewness coefficients. If it is a scalar, its value will be used as the population skewness of all variables to be generated.

kurt

A numeric vector of the population excess kurtosis coefficients. If it is a scalar, its value will be used as the population excess kurtosis coefficients. of all variables to be generated.

rIG_args

A list of extra arguments to be passed to covsim::rIG().

pmean

A numeric vector population means. If it is a scalar, its value will be used as the population means of all variables to be generated.

psd

A numeric vector of population standard deviations. If it is a scalar, its value will be used as the population standard deviations of all variables to be generated.

Value

A two-dimension matrix of the generated random numbers.

Details

First, random numbers will be generated using covsim::rIG(), with the specified target population marginal skewness and excess kurtosis. The random numbers will then be rescaled with the desired population means and standard deviations.

References

Foldnes, N. and Olson, U. H. (2016). A simple simulation technique for nonnormal data with prespecified skewness, kurtosis, and covariance matrix. Multivariate Behavioral Research, 51(2–3), 207–219. doi:10.1080/00273171.2015.1133274

Examples

set.seed(90870962)
sigma <- matrix(c(1, .3, .3, 1), 2, 2)
sigma
#>      [,1] [,2]
#> [1,]  1.0  0.3
#> [2,]  0.3  1.0
x <- rig_rs(n = 5000,
            sigma = sigma,
            skew = 2,
            kurt = 6
)
colMeans(x)
#> [1] -0.003175567  0.017891561
apply(x, 2, sd)
#> [1] 1.005155 1.026747
hist(x[, 1])