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

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

Author(s)

John Ruscio

References

Ruscio & Roche (2011)

Examples

# 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.