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

## Description

Analyzes comparison data with known factorial structures

## Usage

 ```1 2``` ```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).

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

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