Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 | 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ## 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
|
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