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)
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 |
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 matrix of -1,0,1 cluster definitions for each item.
http://personality-project.org/revelle.html
Maintainer: William Revelle revelle@northwestern.edu
http://personality-project.org/r/r.vss.html
cluster.cor
, factor2cluster
, fa
, principal
, ICLUST
## Not run: f <- factanal(x,4,covmat=Harman74.cor$cov) factor2cluster(f) ## End(Not run) # Factor1 Factor2 Factor3 Factor4 #VisualPerception 0 1 0 0 #Cubes 0 1 0 0 #PaperFormBoard 0 1 0 0 #Flags 0 1 0 0 #GeneralInformation 1 0 0 0 #PargraphComprehension 1 0 0 0 #SentenceCompletion 1 0 0 0 #WordClassification 1 0 0 0 #WordMeaning 1 0 0 0 #Addition 0 0 1 0 #Code 0 0 1 0 #CountingDots 0 0 1 0 #StraightCurvedCapitals 0 0 1 0 #WordRecognition 0 0 0 1 #NumberRecognition 0 0 0 1 #FigureRecognition 0 0 0 1 #ObjectNumber 0 0 0 1 #NumberFigure 0 0 0 1 #FigureWord 0 0 0 1 #Deduction 0 1 0 0 #NumericalPuzzles 0 0 1 0 #ProblemReasoning 0 1 0 0 #SeriesCompletion 0 1 0 0 #ArithmeticProblems 0 0 1 0