# varimax

##### Rotation Methods for Factor Analysis

These functions

- Keywords
- multivariate

##### Usage

```
varimax(x, normalize = TRUE, eps = 1e-5)
promax(x, m = 4)
```

##### Arguments

- x
- A loadings matrix, with $p$ rows and $k < p$ columns
- m
- The power used the target for
`promax`

. Values of 2 to 4 are recommended. - normalize
- logical. Should Kaiser normalization be performed?
If so the rows of
`x`

are re-scaled to unit length before rotation, and scaled back afterwards. - eps
- The tolerance for stopping: the relative change in the sum of singular values.

##### Details

These seek a `x %*% T`

that
aims to clarify the structure of the loadings matrix. The matrix
`T`

is a rotation (possibly with reflection) for `varimax`

,
but a general linear transformation for `promax`

, with the
variance of the factors being preserved.

##### Value

- A list with components
loadings The rotated loadings matrix,`x %*% rotmat`

, of class`"loadings"`

.rotmat The rotation matrix.

##### References

Hendrickson, A. E. and White, P. O. (1964) Promax: a quick method for
rotation to orthogonal oblique structure. *British Journal of
Statistical Psychology*, **17**, 65--70.

Horst, P. (1965) *Factor Analysis of Data Matrices.* Holt,
Rinehart and Winston. Chapter 10.

Kaiser, H. F. (1958) The varimax criterion for analytic rotation in
factor analysis. *Psychometrika* **23**, 187--200.

Lawley, D. N. and Maxwell, A. E. (1971) *Factor Analysis as a
Statistical Method*. Second edition. Butterworths.

##### See Also

##### Examples

`library(stats)`

```
## varimax with normalize = TRUE is the default
fa <- factanal( ~., 2, data = swiss)
varimax(loadings(fa), normalize = FALSE)
promax(loadings(fa))
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*