monte1: Simulate Multivariate Non-normal Data by Vale & Maurelli...

In fungible: Psychometric Functions from the Waller Lab

Simulate Multivariate Non-normal Data by Vale & Maurelli (1983) Method

Description

Function for simulating multivariate nonnormal data by the methods described by Fleishman (1978) and Vale & Maurelli (1983).

Usage

monte1(seed, nvar, nsub, cormat, skewvec, kurtvec)

Arguments

seed	An integer to be used as the random number seed.
nvar	Number of variables to simulate.
nsub	Number of simulated subjects (response vectors).
cormat	The desired correlation matrix.
skewvec	A vector of indicator skewness values.
kurtvec	A vector of indicator kurtosis values.

Value

data	The simulated data.
call	The call.
nsub	Number of subjects.
nvar	Number of variables.
cormat	The desired correlation matrix.
skewvec	The desired indicator skewness values.
kurtvec	The desired indicator kurtosis values.
seed	The random number seed.

Author(s)

Niels Waller

References

Fleishman, A. I (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-532.

Olvera Astivia, O. L. & Zumbo, B. D. (2018). On the solution multiplicity of the Fleishman method and its impact in simulation studies. British Journal of Mathematical and Statistical Psychology, 71 (3), 437-458.

Vale, D. C., & Maurelli, V. A. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48, 465-471.

See Also

monte, summary.monte, summary.monte1

Examples

## Generate dimensional data for 4 variables. 
## All correlations = .60; all variable
## skewness = 1.75; 
## all variable kurtosis = 3.75
 
cormat <- matrix(.60,4,4)
diag(cormat) <- 1

nontaxon.dat <- monte1(seed = 123, nsub = 100000, nvar = 4, skewvec = rep(1.75, 4),
               kurtvec = rep(3.75, 4), cormat = cormat)
 
print(cor(nontaxon.dat$data), digits = 3)
print(apply(nontaxon.dat$data, 2, skew), digits = 3)
print(apply(nontaxon.dat$data, 2, kurt), digits = 3)               

fungible documentation built on March 31, 2023, 5:47 p.m.