rIG: Simulation of non-normal data

In covsim: VITA, IG and PLSIM Simulation for Given Covariance and Marginals

Simulation of non-normal data

Description

Using the IG method to simulate non-normal data

Usage

rIG(N, sigma.target, skewness, excesskurtosis, reps = 1, typeA = "triang")

Arguments

N	Number of observations to simulate.
sigma.target	Target population covariance matrix
skewness	Target skewness
excesskurtosis	Target excess kurtosis
reps	Number of simulated samples
typeA	Symmetrical or triangular (default) A matrix

Value

A list of simulated samples

Author(s)

Njål Foldnes ([e-mail])

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

Examples

set.seed(1234)
model <- '
 # measurement model
   ind60 =~ x1 + x2 + x3
   dem60 =~ y1 + y2 + y3 + y4
   dem65 =~ y5 + y6 + y7 + y8
 # regressions
   dem60 ~ ind60
   dem65 ~ ind60 + dem60
 # residual correlations
   y1 ~~ y5
   y2 ~~ y4 + y6
   y3 ~~ y7
   y4 ~~ y8
   y6 ~~ y8'
fit  <- lavaan::sem(model, data=lavaan::PoliticalDemocracy)
population.sigma <- lavaan::lavInspect(fit, "sigma.hat")
population.skew  <- c(0, 0, 0, 0, 1, 1, 1, 1, 2,2,2 )
population.excesskurt <- c( 1 , 1, 1, 1, 3, 3, 3, 3, 15, 15, 15)
my.samples <- rIG(N=10^3, sigma=population.sigma,
        skewness=population.skew,
        excesskurt=population.excesskurt,
        reps=5)

covsim documentation built on June 22, 2024, 9:32 a.m.