polychoric.rb in statsample-0.6.4

- old
+ new

@@ -46,37 +46,39 @@
       attr_accessor :name
       # Max number of iterations used on iterative methods. Default to MAX_ITERATIONS
       attr_accessor :max_iterations
       # Debug algorithm (See iterations, for example)
       attr_accessor :debug
-      # Minimizer type. Default GSL::Min::FMinimizer::BRENT
+      # Minimizer type for two step. Default "brent"
       # See http://rb-gsl.rubyforge.org/min.html for reference.  
       attr_accessor :minimizer_type_two_step
       
-      # Minimizer type. Default GSL::Min::FMinimizer::BRENT
+      # Minimizer type for joint estimate. Default "nmsimplex"
       # See http://rb-gsl.rubyforge.org/min.html for reference.  
       attr_accessor :minimizer_type_joint
       
       
       # Method of calculation of polychoric series. 
       # 
-      # :two_step:: two-step ML, based on code by Gegenfurtner(1992)
+      # :two_step:: two-step ML, based on code by Gegenfurtner(1992).
       # :polychoric_series:: polychoric series estimate, using 
-      #                      algorithm AS87 by Martinson and Hamdan (1975)
-      # :joint:              one-step ML, based on R package 'polycor' 
+      #                      algorithm AS87 by Martinson and Hamdan (1975).
+      # :joint::             one-step ML, based on R package 'polycor'
       #                      by J.Fox.
       attr_accessor :method
       # Absolute error for iteration.
       attr_accessor :epsilon
       
       # Number of iterations
       attr_reader :iteration
       
       # Log of algorithm
       attr_reader :log
-      attr_reader :loglike
       
+      
+      attr_reader :loglike_model
+      
       METHOD=:two_step
       MAX_ITERATIONS=300
       EPSILON=0.000001
       MINIMIZER_TYPE_TWO_STEP="brent"
       MINIMIZER_TYPE_JOINT="nmsimplex"
@@ -160,20 +162,19 @@
         end
       end
       
       def loglike_data
         loglike=0
-        @nr.times { |i|
-          @nc.times { |j|
+        @nr.times do |i|
+          @nc.times do |j|
             res=@matrix[i,j].quo(@total)
             if (res==0)
-           #    puts "Correccion"
-            res=1e-16
-          end
+              res=1e-16
+            end
           loglike+= @matrix[i,j]  * Math::log(res )
-          }
-        }
+          end
+        end
         loglike
       end
       def chi_square
         if @loglike_model.nil?
           compute
@@ -344,10 +345,10 @@
           for i in 0...np do
             message+=sprintf("%10.3e ", x[i])
           end
           message+=sprintf("f() = %7.3f size = %.3f\n", minimizer.fval, minimizer.size)+"\n";
         end while status == GSL::CONTINUE and iter < @max_iterations
-        @iteration=@iter
+        @iteration=iter
         @log+=message
         puts message if @debug
         @r=minimizer.x[0]
         @alpha=minimizer.x[1,@nr-1].to_a
         @beta=minimizer.x[@nr,@nc-1].to_a