semPower (version 2.1.1)

genData.RK: genData.RK

Description

Generates random data conforming to a population variance-covariance matrix using the approach by Ruscio & Kaczetow (2008) specifying distributions for the marginals.

Usage

genData.RK(
  N = NULL,
  Sigma = NULL,
  nSets = 1,
  distributions = NULL,
  modelH0 = NULL,
  maxIter = 10
)

Value

Returns the generated data

Arguments

N

sample size.

Sigma

population covariance matrix.

nSets

number of data sets to generate

distributions

a list specifying the population distribution and additional arguments in a list either to apply to all variables (e.g. list(rchisq, list(df = 2))) or a list of lists specifying the distributions for each variable. See examples.

modelH0

a lavaan model string, only used to determine the number of factors.

maxIter

maximum number of iterations, defaults to 10.

Details

This function is based on the implementation by Ruscio & Kaczetow (2008).

For details, see Ruscio, J., & Kaczetow, W. (2008). Simulating multivariate nonnormal data using an iterative algorithm. Multivariate Behavioral Research, 43, 355-381.

Examples

if (FALSE) {
distributions <- list(
  list('rchisq', list(df = 2)),
  list('runif', list(min = 0, max = 1)),
  list('rexp', list(rate = 1))
)
data <- genData.ruscio(N = 100, Sigma = diag(3),
                       distributions = distributions, 
                       modelH0 = 'f =~ x1 + x2 + x3')
                       
distributions <- list(
  list('rnorm', list(mean = 0, sd = 10)),
  list('runif', list(min = 0, max = 1)),
  list('rbeta', list(shape1 = 1, shape2 = 2)),
  list('rexp', list(rate = 1)),
  list('rpois', list(lambda = 4)),
  list('rbinom', list(size = 1, prob = .5))
)
data <- genData.ruscio(N = 100, Sigma = diag(6),
                       distributions = distributions, 
                       modelH0 = 'f1=~x1+x2+x3\nf2=~x4+x5+x6')

}