class Statsample::Factor::MAP

Velicer's Minimum Average Partial ↑

“Velicer’s (1976) MAP test involves a complete princi- pal components analysis followed by the examination of a series of matrices of partial correlations. Specifically, on the first step, the first principal component is par- tialed out of the correlations between the variables of in- terest, and the average squared coefficient in the off- diagonals of the resulting partial correlation matrix is computed. On the second step, the first two principal components are partialed out of the original correlation matrix and the average squared partial correlation is again computed. These computations are conducted for k (the number of variables) minus one steps. The average squared partial correlations from these steps are then lined up, and the number of components is determined by the step number in the analyses that resulted in the lowest average squared partial correlation. The average squared coefficient in the original correlation matrix is also com- puted, and if this coefficient happens to be lower than the lowest average squared partial correlation, then no components should be extracted from the correlation ma- trix. Statistically, components are retained as long as the variance in the correlation matrix represents systematic variance. Components are no longer retained when there is proportionately more unsystematic variance than sys- tematic variance.” (O'Connor, 2000, p.397).

Current algorithm is loosely based on SPSS O'Connor algorithm

Reference ↑

Velicer's Minimum Average Partial ↑

“Velicer’s (1976) MAP test involves a complete princi- pal components analysis followed by the examination of a series of matrices of partial correlations. Specifically, on the first step, the first principal component is par- tialed out of the correlations between the variables of in- terest, and the average squared coefficient in the off- diagonals of the resulting partial correlation matrix is computed. On the second step, the first two principal components are partialed out of the original correlation matrix and the average squared partial correlation is again computed. These computations are conducted for k (the number of variables) minus one steps. The average squared partial correlations from these steps are then lined up, and the number of components is determined by the step number in the analyses that resulted in the lowest average squared partial correlation. The average squared coefficient in the original correlation matrix is also com- puted, and if this coefficient happens to be lower than the lowest average squared partial correlation, then no components should be extracted from the correlation ma- trix. Statistically, components are retained as long as the variance in the correlation matrix represents systematic variance. Components are no longer retained when there is proportionately more unsystematic variance than sys- tematic variance.” (O'Connor, 2000, p.397).

Current algorithm is loosely based on SPSS O'Connor algorithm

Reference ↑

Attributes

eigenvalues[R]
fm[R]

Average squared correlations

minfm[R]

Smallest average squared correlation

name[RW]

Name of analysis

number_of_factors[R]

Number of factors to retain

use_gsl[RW]

Public Class Methods

new(matrix, opts=Hash.new) click to toggle source 
# File lib/statsample/factor/map.rb, line 56
def initialize(matrix, opts=Hash.new)
  @matrix=matrix
  opts_default={
    :use_gsl=>true,
    :name=>_("Velicer's MAP")
  }
  @opts=opts_default.merge(opts)
   opts_default.keys.each {|k| send("#{k}=", @opts[k]) }
end
with_dataset(ds,opts=Hash.new) click to toggle source 
# File lib/statsample/factor/map.rb, line 53
def self.with_dataset(ds,opts=Hash.new)
  new(ds.correlation_matrix,opts)
end

Public Instance Methods

compute() click to toggle source 
# File lib/statsample/factor/map.rb, line 65
def compute
  gsl_m=(use_gsl and Statsample.has_gsl?) ? @matrix.to_gsl : @matrix
  klass_m=gsl_m.class
  eigvect,@eigenvalues=gsl_m.eigenvectors_matrix, gsl_m.eigenvalues
  eigenvalues_sqrt=@eigenvalues.collect {|v| Math.sqrt(v)}
  loadings=eigvect*(klass_m.diagonal(*eigenvalues_sqrt))
  fm=Array.new(@matrix.row_size)
  ncol=@matrix.column_size
  
  fm[0]=(gsl_m.mssq - ncol).quo(ncol*(ncol-1))
  
  (ncol-1).times do |m|
    puts "MAP:Eigenvalue #{m+1}" if $DEBUG
    a=loadings[0..(loadings.row_size-1),0..m]
    partcov= gsl_m - (a*a.transpose)
    
    d=klass_m.diagonal(*(partcov.diagonal.collect {|v| Math::sqrt(1/v)}))
    pr=d*partcov*d
    fm[m+1]=(pr.mssq-ncol).quo(ncol*(ncol-1))
  end
  minfm=fm[0]
  nfactors=0
  @errors=[]
  fm.each_with_index do |v,s|
    if defined?(Complex) and v.is_a? ::Complex
      @errors.push(s)
    else
      if v < minfm
        minfm=v
        nfactors=s
      end
    end
  end
  @number_of_factors=nfactors
  @fm=fm
  @minfm=minfm
  
end