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Contents
module Mspire
module ErrorRate
module Decoy
module_function
# 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
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
# the false positive predictive rate (sometimes called the false
# positive rate). This is 1 - precision
def fppr(num_target, num_decoy, pi_not=1.0)
1.0 - precision(num_target, num_decoy, pi_not=1.0)
end
extend(self)
end
end
end
Version data entries
39 entries across 39 versions & 1 rubygems