Given a factor or principal components loading matrix, assign each item to a cluster corresponding to the largest (signed) factor loading for that item. Essentially, this is a Very Simple Structure approach to cluster definition that corresponds to what most people actually do: highlight the largest loading for each item and ignore the rest.

`factor2cluster(loads, cut = 0,aslist=FALSE)`

loads

either a matrix of loadings, or the result of a factor analysis/principal components analyis with a loading component

cut

Extract items with absolute loadings > cut

aslist

if TRUE, Return a keys list, else return a keys matrix (old style)

a keys list (new style or a matrix of -1,0,1 cluster definitions for each item.

A factor/principal components analysis loading matrix is converted to a cluster (-1,0,1) definition matrix where each item is assigned to one and only one cluster. This is a fast way to extract items that will be unit weighted to form cluster composites. Use this function in combination with cluster.cor to find the corrleations of these composite scores.

A typical use in the SAPA project is to form item composites by clustering or factoring (see `ICLUST`

, `principal`

), extract the clusters from these results (`factor2cluster`

), and then form the composite correlation matrix using `cluster.cor`

. The variables in this reduced matrix may then be used in multiple R procedures using mat.regress.

The input may be a matrix of item loadings, or the output from a factor analysis which includes a loadings matrix.

`cluster.cor`

, `factor2cluster`

, `fa`

, `principal`

, `ICLUST`

`make.keys`

, `keys2list`

```
# NOT RUN {
#matches the factanal example
f4 <- fa(Harman74.cor$cov,4,rotate="varimax")
factor2cluster(f4)
# }
```

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