powered by
Compute statistical significance of Jaccard/Tanimoto similarity coefficients.
jaccard.test.mca( x, y, px = NULL, py = NULL, accuracy = 1e-05, error.type = "average", verbose = TRUE )
jaccard.test.mca returns a list consisting of
jaccard.test.mca
centered Jaccard/Tanimoto similarity coefficient
p-value
expectation
a binary vector (e.g., fingerprint)
probability of successes in x (optional)
x
probability of successes in y (optional)
y
an error bound on approximating a multinomial distribution
an error type on approximating a multinomial distribution ("average", "upper", "lower")
whether to print progress messages
set.seed(1234) x = rbinom(100,1,.5) y = rbinom(100,1,.5) jaccard.test.mca(x,y,accuracy = 1e-05)
Run the code above in your browser using DataLab