Description Usage Arguments Value Author(s) References Examples

Comparison data

1 2 | ```
EFACompData(data, f.max, n.pop = 10000, n.samples = 500, alpha = .30, graph = FALSE,
corr.type = "pearson")
``` |

`data` |
Matrix to store the simulated data (matrix). |

`f.max` |
Largest number of factors to consider (scalar). |

`n.pop` |
Size of finite populations of comparison data (scalar, default is 10,000 cases). |

`n.samples` |
Number of samples drawn from each population (scalar, default is 500). |

`alpha` |
Alpha level when testing statistical significance of improvement with additional factor (scalar, default is .30) |

`graph` |
Whether to plot the fit of eigenvalues to those for comparison data (default is FALSE). |

`corr.type` |
Type of correlation (character, default is "pearson", user can also call "spearman"). |

Nothing, displays number of factors on screen.

John Ruscio

Ruscio & Roche (2011)

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)
}
}
# empirically determine number of factors in data matrix x
EFACompData(x, f.max = 5)
``` |

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