Wraps `fa`

function from the `psych`

package;
see `fa`

for more information.

addEFA(mctd, nfactors = length(unique(mctd$AnswerKey$Concept)), n.obs = nrow(mctd$Test.complete), rotate = "oblimin", fm = "minres", ...)

mctd | Existing mcTestAnalysis data object |
---|---|

nfactors | Number of factors to extract, default is 1 |

n.obs | Number of observations used to find the correlation matrix if using a correlation matrix. Used for finding the goodness of fit statistics. Must be specified if using a correlaton matrix and finding confidence intervals. |

rotate | "none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" and "bifactor" are orthogonal rotations. "Promax", "promax", "oblimin", "simplimax", "bentlerQ, "geominQ" and "biquartimin" and "cluster" are possible oblique transformations of the solution. The default is to do a oblimin transformation, although versions prior to 2009 defaulted to varimax. SPSS seems to do a Kaiser normalization before doing Promax, this is done here by the call to "promax" which does the normalization before calling Promax in GPArotation. |

fm | Factoring method fm="minres" will do a minimum residual (OLS), fm="uls" differs very slightly from "minres" in that it minimizes the entire residual matrix using an OLS procedure. fm="wls" will do a weighted least squares (WLS) solution, fm="gls" does a generalized weighted least squares (GLS), fm="pa" will do the principal factor solution, fm="ml" will do a maximum likelihood factor analysis. fm="minchi" will minimize the sample size weighted chi square when treating pairwise correlations with different number of subjects per pair. fm ="minrank" will do a minimum rank factor analysis. |

... | Passed to |