FactorAnalysis: FactorAnalysis

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

Description

Analyzes comparison data with known factorial structures

Usage

FactorAnalysis(data, corr.matrix = FALSE, max.iteration = 50,n.factors = 0,
corr.type = "pearson")

Arguments

data	Matrix to store the simulated data (matrix).
corr.matrix	Correlation matrix (default is FALSE)
max.iteration	Maximum number of iterations (scalar, default is 50).
n.factors	Number of factors (scalar, default is 0).
corr.type	Type of correlation (character, default is "pearson", user can also call "spearman").

Value

$loadings	Factor loadings (vector, if one factor. matrix, if multiple factors)
$factors	Number of factors (scalar).

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)
  }
}
# perform factor analysis of data matrix x
FactorAnalysis(x)

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