Generate non-normal data with Vale & Maurelli's (1983) method
Generate multivariate non-normal distributions using the third-order polynomial method described by Vale & Maurelli (1983). If only a single variable is generated then this function is equivalent to the method described by Fleishman (1978).
rValeMaurelli( n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)), skew = rep(0, nrow(sigma)), kurt = rep(0, nrow(sigma)) )
n |
number of samples to draw |
mean |
a vector of k elements for the mean of the variables |
sigma |
desired k x k covariance matrix between bivariate non-normal variables |
skew |
a vector of k elements for the skewness of the variables |
kurt |
a vector of k elements for the kurtosis of the variables |
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations
with the SimDesign Package. The Quantitative Methods for Psychology, 16
(4), 248-280.
doi: 10.20982/tqmp.16.4.p248
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
Carlo simulation. Journal of Statistics Education, 24
(3), 136-156.
doi: 10.1080/10691898.2016.1246953
Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-532.
Vale, C. & Maurelli, V. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48(3), 465-471.
set.seed(1) # univariate with skew nonnormal <- rValeMaurelli(10000, mean=10, sigma=5, skew=1, kurt=3) # psych::describe(nonnormal) # multivariate with skew and kurtosis n <- 10000 r12 <- .4 r13 <- .9 r23 <- .1 cor <- matrix(c(1,r12,r13,r12,1,r23,r13,r23,1),3,3) sk <- c(1.5,1.5,0.5) ku <- c(3.75,3.5,0.5) nonnormal <- rValeMaurelli(n, sigma=cor, skew=sk, kurt=ku) # cor(nonnormal) # psych::describe(nonnormal)
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