coefa_fa {coefa} | R Documentation |
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")