factor2cluster {psych} | R Documentation |
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 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.
a keys list (new style or a matrix of -1,0,1 cluster definitions for each item.
https://personality-project.org/revelle.html
Maintainer: William Revelle revelle@northwestern.edu
https://personality-project.org/r/r.vss.html
cluster.cor
, factor2cluster
, fa
, principal
, ICLUST
make.keys
, keys2list
#matches the factanal example
f4 <- fa(Harman74.cor$cov,4,rotate="varimax")
factor2cluster(f4)