Pearson Correlation Coefficient

2008-07-01T10:26:00Z

A small Ruby snippet to calculate the Pearson product-moment correlation coefficient:


#!/usr/bin/ruby

class Array 
   def sum
      inject( 0 ) { |sum,x| sum+x }
   end
   def sum_square
      inject( 0 ) { |sum,x| sum+x*x }
   end
   def mean
      sum / size
   end
   def *(other)
      ret = []
      return nil if !other.is_a? Array || size != other.size
      self.each_with_index {|x, i| ret << x * other[i]}
      ret
   end
end


x = Array.new(100) {|i| rand(10) }
y = Array.new(100) {|i| rand(20) }


sumx = x.sum
sumy  = y.sum
num = (x.size * (x*y).sum) - (sumx * sumy)
den = Math.sqrt((x.size * x.sum_square) - sumx * sumx) * Math.sqrt((y.size * y.sum_square) - sumy * sumy)
r = num /den

puts "X= [#{x.join(', ')}]"
puts "Y= [#{y.join(', ')}]"
puts "r= #{r}"

Pearson's Correlation Coefficient

2008-07-01T09:47:54Z


#!/usr/bin/ruby

class Array 
   def sum
      inject( 0 ) { |sum,x| sum+x }
   end
   def sum_square
      inject( 0 ) { |sum,x| sum+x*x }
   end
   def mean
      sum / size
   end
   def *(other)
      ret = []
      return nil if !other.is_a? Array || size != other.size
      self.each_with_index {|x, i| ret << x * other[i]}
      ret
   end
end


x = Array.new(100) {|i| rand(10) }
y = Array.new(100) {|i| rand(20) }


sumx = x.sum
sumy  = y.sum
num = (x.size * (x*y).sum) - (sumx * sumy)
den = Math.sqrt((x.size * x.sum_square) - sumx * sumx) * Math.sqrt((y.size * y.sum_square) - sumy * sumy)
r = num /den

puts "X= [#{x.join(', ')}]"
puts "Y= [#{y.join(', ')}]"
puts "r= #{r}"

Equars People - Pearson Correlation Coefficient Changes

Pearson Correlation Coefficient

Pearson's Correlation Coefficient