# VSS.plot

##### Plot VSS fits

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.

- Keywords
- multivariate, models

##### Usage

`VSS.plot(x, title = "Very Simple Structure", line = FALSE)`

##### Arguments

- x
output from VSS

- title
any title

- line
connect different complexities

##### Details

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

##### Value

A plot window showing the VSS criterion varying as the number of factors and the complexity of the items.

##### References

##### See Also

##### Examples

```
# NOT RUN {
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
# }
```

*Documentation reproduced from package psych, version 1.8.12, License: GPL (>= 2)*