factor2cluster             package:psych             R Documentation

_E_x_t_r_a_c_t _c_l_u_s_t_e_r _d_e_f_i_n_i_t_i_o_n_s _f_r_o_m _f_a_c_t_o_r _l_o_a_d_i_n_g_s

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     factor2cluster(loads, cut = 0)

_A_r_g_u_m_e_n_t_s:

   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

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     a matrix of -1,0,1 cluster definitions for each item.

_A_u_t_h_o_r(_s):

     <URL: http://personality-project.org/revelle.html> 

     Maintainer: William Revelle    revelle@northwestern.edu           

_R_e_f_e_r_e_n_c_e_s:

     <URL: http://personality-project.org/r/r.vss.html>

_S_e_e _A_l_s_o:

     'cluster.cor', 'factor2cluster', 'factor.pa', 'principal',
     'ICLUST'

_E_x_a_m_p_l_e_s:

     ## 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

