VSS.plot {psych} | R Documentation |
The Very Simple Structure criterion ( 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)
x |
output from VSS |
title |
any title |
line |
connect different complexities |
Item-factor models differ in their "complexity". Complexity 1 means that all except the greatest (absolute) loading for an item are ignored. Basically a cluster model (e.g., 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 https://personality-project.org/r/r.vss.html
A plot window showing the VSS criterion varying as the number of factors and the complexity of the items.
Maintainer: William Revelle revelle@northwestern.edu
https://personality-project.org/r/r.vss.html
VSS
, ICLUST
, omega
test.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 window