Class: Statsample::Factor::MAP

Inherits:
Object
  • Object
show all
Includes:
DirtyMemoize, Summarizable
Defined in:
lib/statsample/factor/map.rb

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods included from Summarizable

#summary

Constructor Details

#initialize(matrix, opts = Hash.new) ⇒ MAP

Returns a new instance of MAP.



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# 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

Instance Attribute Details

#eigenvalues ⇒ Object (readonly)

Returns the value of attribute eigenvalues.



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# File 'lib/statsample/factor/map.rb', line 44

def eigenvalues
  @eigenvalues
end

#fm ⇒ Object (readonly)

Average squared correlations



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# File 'lib/statsample/factor/map.rb', line 48

def fm
  @fm
end

#minfm ⇒ Object (readonly)

Smallest average squared correlation



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# File 'lib/statsample/factor/map.rb', line 50

def minfm
  @minfm
end

#name ⇒ Object

Name of analysis



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# File 'lib/statsample/factor/map.rb', line 43

def name
  @name
end

#number_of_factors ⇒ Object (readonly)

Number of factors to retain



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# File 'lib/statsample/factor/map.rb', line 46

def number_of_factors
  @number_of_factors
end

#use_gsl ⇒ Object

Returns the value of attribute use_gsl.



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# File 'lib/statsample/factor/map.rb', line 52

def use_gsl
  @use_gsl
end

Class Method Details

.with_dataset(ds, opts = Hash.new) ⇒ Object 



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# File 'lib/statsample/factor/map.rb', line 53

def self.with_dataset(ds,opts=Hash.new)
  new(ds.correlation_matrix,opts)
end

Instance Method Details

#compute ⇒ Object 



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# 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=use_gsl ? loadings[0..(loadings.row_size-1),0..m] : 
      loadings.minor(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

#report_building(g) ⇒ Object

:nodoc:



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# File 'lib/statsample/factor/map.rb', line 104

def report_building(g) #:nodoc:
  g.section(:name=>@name) do |s|
    s.table(:name=>_("Eigenvalues"),:header=>[_("Value")]) do |t|
      eigenvalues.each_with_index do |e,i|
          t.row([@errors.include?(i) ? "*" : "%0.6f" % e])
      end
    end
    s.table(:name=>_("Velicer's Average Squared Correlations"), :header=>[_("number of components"),_("average square correlation")]) do |t|
      fm.each_with_index do |v,i|
        t.row(["%d" % i, @errors.include?(i) ? "*" : "%0.6f" % v])
      end
    end
    s.text(_("The smallest average squared correlation is : %0.6f" % minfm))
    s.text(_("The number of components is : %d" % number_of_factors))
  end
end