# monte1: Simulate Multivariate Non-normal Data by Vale & Maurelli... In fungible: Psychometric Functions from the Waller Lab

## Description

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

## Usage

 `1` ```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.

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.

`monte`, `summary.monte`, `summary.monte1`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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) ```