Simulating Univariate Data from Fleishman Power Normal Transformations
Simulates univariate non-normal data by using Fleishman power transformations (Fleishman, 1978; Demirtas & Hedeker, 2007).
fleishman_sim(N=1, coef=NULL, mean=0, sd=1, skew=0, kurt=0) fleishman_coef(mean=0, sd=1, skew=0, kurt=0)
N |
Number of simulated values |
coef |
Optional list containing coefficients of Fleishman polynomial estimated
by |
mean |
Mean |
sd |
Standard deviation |
skew |
Skewness |
kurt |
(Excess) kurtosis |
For Z \sim N(0,1), the Fleishman power normal variable X is defined as X=a + bZ + cZ^2 + d Z^3.
Vector of simulated values (fleishman_sim) or list of coefficients
(fleishman_coef).
Demirtas, H., & Hedeker, D. (2008). Imputing continuous data under some non-Gaussian distributions. Statistica Neerlandica, 62(2), 193-205. doi: 10.1111/j.1467-9574.2007.00377.x
Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43(4), 521-532. doi: 10.1007/BF02293811
See also the BinOrdNonNor::Fleishman.coef.NN function in the
BinOrdNonNor package.
See the nnig_sim function for simulating a non-normally distributed
multivariate variables.
############################################################################# # EXAMPLE 1: Simulating values with Fleishman polynomial ############################################################################# #* define mean, standard deviation, skewness and kurtosis mean <- .75 sd <- 2 skew <- 1 kurt <- 3 #* compute coefficients of Fleishman polynomial coeff <- miceadds::fleishman_coef(mean=mean, sd=sd, skew=skew, kurt=kurt) print(coeff) # sample size N <- 1000 set.seed(2018) #* simulate values based on estimated coefficients X <- miceadds::fleishman_sim(N=N, coef=coeff) #* simulate values based on input of moments X <- miceadds::fleishman_sim(N=N, mean=mean, sd=sd, skew=skew, kurt=kurt)
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