Class: Statistics::SpearmanRankCoefficient
- Inherits:
-
Object
- Object
- Statistics::SpearmanRankCoefficient
- Defined in:
- lib/statistics/spearman_rank_coefficient.rb
Class Method Summary collapse
-
.coefficient(set_one, set_two) ⇒ Object
Formulas extracted from: statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php.
- .rank(data:, return_ranks_only: true) ⇒ Object
Class Method Details
.coefficient(set_one, set_two) ⇒ Object
Formulas extracted from: statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php
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# File 'lib/statistics/spearman_rank_coefficient.rb', line 33 def self.coefficient(set_one, set_two) raise 'Both group sets must have the same number of cases.' if set_one.size != set_two.size return if set_one.size == 0 && set_two.size == 0 set_one_mean, set_two_mean = set_one.mean, set_two.mean have_tie_ranks = (set_one + set_two).any? { |rank| rank.is_a?(Float) || rank.is_a?(Rational) } if have_tie_ranks numerator = 0 squared_differences_set_one = 0 squared_differences_set_two = 0 set_one.size.times do |idx| local_diff_one = (set_one[idx] - set_one_mean) local_diff_two = (set_two[idx] - set_two_mean) squared_differences_set_one += local_diff_one ** 2 squared_differences_set_two += local_diff_two ** 2 numerator += local_diff_one * local_diff_two end denominator = Math.sqrt(squared_differences_set_one * squared_differences_set_two) numerator / denominator.to_r # This is rho or spearman's coefficient. else sum_squared_differences = set_one.each_with_index.reduce(0) do |memo, (rank_one, index)| memo += ((rank_one - set_two[index]) ** 2) memo end numerator = 6 * sum_squared_differences denominator = ((set_one.size ** 3) - set_one.size) 1.0 - (numerator / denominator.to_r) # This is rho or spearman's coefficient. end end |
.rank(data:, return_ranks_only: true) ⇒ Object
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# File 'lib/statistics/spearman_rank_coefficient.rb', line 3 def self.rank(data:, return_ranks_only: true) descending_order_data = data.sort { |a, b| b <=> a } rankings = {} data.each do |value| # If we have ties, the find_index method will only retrieve the index of the # first element in the list (i.e, the most close to the left of the array), # so when a tie is detected, we increase the temporal ranking by the number of # counted elements at that particular time and then we increase the counter. temporal_ranking = descending_order_data.find_index(value) + 1 # 0-index if rankings.fetch(value, false) rankings[value][:rank] += (temporal_ranking + rankings[value][:counter]) rankings[value][:counter] += 1 rankings[value][:tie_rank] = rankings[value][:rank] / rankings[value][:counter].to_r else rankings[value] = { counter: 1, rank: temporal_ranking, tie_rank: temporal_ranking } end end if return_ranks_only data.map do |value| rankings[value][:tie_rank] end else rankings end end |