Package jaccard. R topics documented: June 14, Type Package
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1 Tpe Package Package jaccard June 14, 2018 Title Test Similarit Between Binar Data using Jaccard/Tanimoto Coefficients Version Date Author Neo Christopher Chung Błażej Miasojedow Michał Startek Anna Gambin Maintainer Neo Christopher Chung Calculate statistical significance of Jaccard/Tanimoto similarit coefficients for binar data. biocviews License GPL-2 Encoding UTF-8 LazData true Imports Rcpp (>= ), qvalue, dplr, magrittr LinkingTo Rcpp NeedsCompilation es SstemRequirements C++11 RogenNote Repositor CRAN Date/Publication :53:00 UTC R topics documented: jaccard jaccard.ev jaccard.rahman jaccard.test
2 2 jaccard jaccard.test.asmptotic jaccard.test.bootstrap jaccard.test.eact jaccard.test.mca jaccard.test.pairwise Inde 11 jaccard Compute a Jaccard/Tanimoto similarit coefficient Compute a Jaccard/Tanimoto similarit coefficient jaccard(,, center = FALSE, p = NULL, p = NULL) center p p whether to center the Jaccard/Tanimoto coefficient b its jaccard returns a Jaccard/Tanimoto coefficient. = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard(,)
3 jaccard.ev 3 jaccard.ev Compute an epected Jaccard/Tanimoto similarit coefficient under independence Compute an epected Jaccard/Tanimoto similarit coefficient under independence jaccard.ev(,, p = NULL, p = NULL) p p jaccard.ev returns an epected value. = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard.ev(,) jaccard.rahman Compute p-value using an etreme value distribution Rahman et al. (2014) proposes a method to compute a p-value of a Jaccard/Tanimoto coefficient using an etreme value distribution. Their paper provides the following description: The mean (mu) and s.d. (sigma) of the similarit scores are used to define the z score, z = (Tw - mu)/sigma. For the purpose of calculating the P value, onl hits with T > 0 are considered. The P value w is derived from the z score using an etreme value distribution P = 1 - ep(-e-z*pi/sqrt(6) - G (1)), where the Euler=Mascheroni constant G (1)= jaccard.rahman(j)
4 4 jaccard.test j a numeric vector of observed Jaccard coefficients (uncentered) jaccard.rahman returns a numeric vector of p-values References Rahman, Cuesta, Furnham, Hollida, and Thornton (2014) EC-BLAST: a tool to automaticall search and compare enzme reactions. Nature Methods, 11(2) journal/v11/n2/full/nmeth.2803.html jaccard.test Test for Jaccard/Tanimoto similarit coefficients Compute statistical significance of Jaccard/Tanimoto similarit coefficients between binar vectors, using four different methods. jaccard.test(,, method = "mca", p = NULL, p = NULL, = TRUE,...) method p p Details a method to compute a p-value ("mca", "bootstrap", "asmptotic", or "eact")... optional arguments for specific computational methods There eist four methods to compute p-values of Jaccard/Tanimoto similarit coefficients: mca, bootstrap, asmptotic, and eact. This is simpl a wrapper function for corresponding four functions in this package: jaccard.test.mca, jaccard.test.bootstrap, jaccard.test.asmptotic, and jaccard.test.eact. We recommand using either mca or bootstrap methods, since the eact solution is slow for a moderatel large vector and asmptotic approimation ma be inaccurate depending on the input vector size. The bootstrap method uses resampling with replacement binar vectors to compute a
5 jaccard.test 5 p-value (see optional arguments). The mca method uses the measure concentration algorithm that estimates the multinomial distribution with a known error bound (specified b an optional argument accurac). jaccard.test returns a list mainl consisting of pvalue centered Jaccard/Tanimoto similarit coefficient p-value Optional arguments for method="bootstrap" fi whether to fi (i.e., not resample) and/or B a total bootstrap iteration seed a seed for a random number generator Optional arguments for method="mca" accurac an error bound on approimating a multinomial distribution error.tpe an error tpe on approimating a multinomial distribution ("average", "upper", "lower") seed a seed for the random number generator. See Also jaccard.test.bootstrap jaccard.test.mca jaccard.test.eact jaccard.test.asmptotic = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard.test(,,method="bootstrap") jaccard.test(,,method="mca") jaccard.test(,,method="eact") jaccard.test(,,method="asmptotic")
6 6 jaccard.test.asmptotic jaccard.test.asmptotic Compute p-value using an asmptotic approimation Compute statistical significance of Jaccard/Tanimoto similarit coefficients. jaccard.test.asmptotic(,, p = NULL, p = NULL, = TRUE) p p jaccard.test.asmptotic returns a list consisting of pvalue centered Jaccard/Tanimoto similarit coefficient p-value = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard.test.asmptotic(,)
7 jaccard.test.bootstrap 7 jaccard.test.bootstrap Compute p-value using the bootstrap procedure Compute statistical significance of Jaccard/Tanimoto similarit coefficients. jaccard.test.bootstrap(,, p = NULL, p = NULL, = TRUE, fi = "", B = 1000, seed = NULL) p p fi B seed whether to fi (i.e., not resample) and/or a total bootstrap iteration a seed for a random number generator jaccard.test.bootstrap returns a list consisting of pvalue centered Jaccard/Tanimoto similarit coefficient p-value = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard.test.bootstrap(,,b=500)
8 8 jaccard.test.mca jaccard.test.eact Compute p-value using the eact solution Compute statistical significance of Jaccard/Tanimoto similarit coefficients. jaccard.test.eact(,, p = NULL, p = NULL, = TRUE) p p jaccard.test.eact returns a list consisting of pvalue centered Jaccard/Tanimoto similarit coefficient p-value = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard.test.eact(,) jaccard.test.mca Compute p-value using the Measure Concentration Algorithm Compute statistical significance of Jaccard/Tanimoto similarit coefficients. jaccard.test.mca(,, p = NULL, p = NULL, accurac = 1e-05, error.tpe = "average", = TRUE)
9 jaccard.test.pairwise 9 p p accurac error.tpe an error bound on approimating a multinomial distribution an error tpe on approimating a multinomial distribution ("average", "upper", "lower") jaccard.test.mca returns a list consisting of pvalue centered Jaccard/Tanimoto similarit coefficient p-value = rbinom(100,1,.5) = rbinom(100,1,.5) jaccard.test.mca(,,accurac = 1e-05) jaccard.test.pairwise Pair-wise tests for Jaccard/Tanimoto similarit coefficients Given a data matri, it computes pair-wise Jaccard/Tanimoto similarit coefficients and p-values among rows (variables). For fine controls, use "jaccard.test". jaccard.test.pairwise(dat, method = "mca", = TRUE, compute.qvalue = TRUE,...) dat method a data matri a method to compute a p-value ("mca", "bootstrap", "asmptotic", or "eact") compute.qvalue whether to compute q-values... optional arguments for specific computational methods
10 10 jaccard.test.pairwise jaccard.test.pairwise returns a list of matrices pvalues qvalues Jaccard/Tanimoto similarit coefficients p-values q-values See Also jaccard.test
11 Inde jaccard, 2 jaccard.ev, 3 jaccard.rahman, 3 jaccard.test, 4, 10 jaccard.test.asmptotic, 4, 5, 6 jaccard.test.bootstrap, 4, 5, 7 jaccard.test.eact, 4, 5, 8 jaccard.test.mca, 4, 5, 8 jaccard.test.pairwise, 9 11
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APPENDIX C Additional Topics in Differential Equations APPENDIX C. Eact First-Order Equations Eact Differential Equations Integrating Factors Eact Differential Equations In Chapter 6, ou studied applications
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