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)
either a matrix of loadings, or the result of a factor analysis/principal components analyis with a loading component
Extract items with absolute loadings > cut
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
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.