module Mspire::ErrorRate::Decoy
Public Instance Methods
fppr(num_target, num_decoy, pi_not=1.0)
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the false positive predictive rate (sometimes called the false positive rate). This is 1 - precision
# File lib/mspire/error_rate/decoy.rb, line 33 def fppr(num_target, num_decoy, pi_not=1.0) 1.0 - precision(num_target, num_decoy, pi_not=1.0) end
precision(num_target, num_decoy, pi_not=1.0)
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this is the # true positives (found by estimating the number of false hits using the # decoy) pi_not is the ratio of decoy hits to the estimated false hits in the target set. A data set with a small fraction of true hits will have a pi_not close to 1. A data set where 40% of the hits are correct should have a pi_not of 0.6. For instance, Spivak uses a fixed pi_not of 0.9 in J. Proteome Res., 2009, 8 (7), pp 3737–3745
# File lib/mspire/error_rate/decoy.rb, line 15 def precision(num_target, num_decoy, pi_not=1.0) num_target_f = num_target.to_f num_true_pos = num_target_f - (num_decoy.to_f * pi_not) precision = if num_target_f == 0.0 if num_decoy.to_f > 0.0 0.0 else 1.0 end else num_true_pos/num_target_f end precision end