coefa_fa {coefa}

PCA and EFA

Description

Choosing an appropriate method to extract factor from the aggregated co-occurrence matrix.

Usage

coefa_fa(R, nfactors, methodcoefa, rotate, fm)

Arguments

R

a matrix. R is the pooled co-occurrence matrix obtained from the last step.

nfactors

Number of factors could be extracted.

methodcoefa

The method choosing factor model(i.e.PCA or EFA)."PCA" is principal component analysis,"EFA" is exploratory factor analysis.

rotate

The rotate parameter has many options to be chosen. They are: "none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" and "bifactor" are orthogonal rotations. "Promax", "promax", "oblimin", "simplimax", "bentlerQ, "geominQ" and "biquartimin" and "cluster".According to Shafer (2005, 2006), the only correct choice is “varimax”, because the aggregated co-occurrence matrix is different from a correlation matrix or a covariance matrix in essence. The default is to do a oblimin transformation.

fm

This parameter also has many options.When fm="minres", it will do a minimum residual, as will fm="uls". Both of them use a first derivative. What should be noted is that the fm="uls" is recommend by experts because it don’t request the normality and positive as two prerequisites.The fm="ols" differs very slightly from "minres" in that it minimizes the entire residual matrix using an OLS procedure but uses the empirical first derivative. This will be slower.The fm="wls" will do a weighted least squares (WLS) solution; the fm="gls" does a generalized weighted least squares (GLS); and the fm="pa" will do the principal factor solution, fm="ml" will do a maximum likelihood factor analysis. The fm="minchi" will minimize the sample size weighted chi square when treating pairwise correlations with different number of subjects per pair. The fm ="minrank" will do a minimum rank factor analysis. The "old.min" will do minimal residual the way it was done prior to April, 2017. The fm="alpha" will starts an alpha factor analysis as described in Kaiser and Coffey (1965).

Details

It should be noted that we should be alert to the positive definiteness of the aggregated matrix. If the matrix is non-positive definite, we should choose the factor extraction method carefully or we should take other solutions (remove questions appropriately, or smooth the matrix).

Value

a data frame

Examples

#Choosing EFA method to extract factor.
coefa_fa(matrices_acm,nfactors=6,methodcoefa="EFA",rotate="varimax",fm="ml")