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VSS) for estimating the optimal number of factors is plotted as a function of the increasing complexity and increasing number of factors.VSS.plot(x, title = "Very Simple Structure", line = FALSE)ICLUST). Complexity 2 implies all except the greatest two, etc. Different complexities can suggest different number of optimal number of factors to extract. For personality items, complexity 1 and 2 are probably the most meaningful.
The Very Simple Structure criterion will tend to peak at the number of factors that are most interpretable for a given level of complexity. Note that some problems, the most interpretable number of factors will differ as a function of complexity. For instance, when doing the Harman 24 psychological variable problems, an unrotated solution of complexity one suggests one factor (g), while a complexity two solution suggests that a four factor solution is most appropriate. This latter probably reflects a bi-factor structure.
For examples of VSS.plot output, see
VSS, ICLUST, omegatest.data <- Harman74.cor$cov
my.vss <- VSS(test.data) #suggests that 4 factor complexity two solution is optimal
VSS.plot(my.vss,title="VSS of Holzinger-Harmon problem") #see the graphics windowRun the code above in your browser using DataLab