# GenDataPopulation: GenDataPopulation In RGenData: Generates Multivariate Nonnormal Data and Determines How Many Factors to Retain

## Description

Simulates multivariate nonnormal data using an iterative algorithm

## Usage

 ```1 2``` ```GenDataPopulation(supplied.data, n.factors, n.cases, max.trials = 5, initial.multiplier = 1, corr.type = "pearson", seed = 0) ```

## Arguments

 `supplied.data` Data supplied by user. `n.factors` Number of factors (scalar). `n.cases` Number of cases (scalar). `max.trials` Maximum number of trials (scalar, default is 5). `initial.multiplier` Value of initial multiplier (scalar, default is 1). `corr.type` Type of correlation (character, default is "pearson", user can also call "spearman"). `seed` seed value (scalar, default is 0).

## Value

dataPopulation of data

John Ruscio

## References

Ruscio & Roche (2011)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# create data matrix x with n = 200 cases, k = 9 variables # 3 variables load onto each of 3 orthogonal factors # all marginal distributions are highly skewed x <- matrix(nrow = 200, ncol = 9) for (i in 1:3) { shared <- rchisq(200, 1) for (j in 1:3) { x[, (i - 1) * 3 + j] <- shared + rchisq(200, 1) } } # generate (finite) population of data reproducing distributions and correlations in x GenDataPopulation(x, n.factors = 3, n.cases = 10000) ```

RGenData documentation built on May 2, 2019, 2:47 p.m.