R/dist.diversity.R
dist.diversity.RdThis function computes all distance values between two probability density functions that are available in getDistMethods
and returns a vector storing the corresponding distance measures. This vector is named distance diversity vector.
dist.diversity(x, p, test.na = FALSE, unit = "log2")a numeric data.frame or matrix (storing probability vectors) or a numeric data.frame or matrix storing counts (if est.prob is specified).
power of the Minkowski distance.
a boolean value indicating whether input vectors should be tested for NA values. Faster computations if test.na = FALSE.
a character string specifying the logarithm unit that should be used to compute distances that depend on log computations. Options are:
unit = "log"
unit = "log2"
unit = "log10"
dist.diversity(rbind(1:10/sum(1:10), 20:29/sum(20:29)), p = 2, unit = "log2")
#> Metric: 'euclidean' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'manhattan' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'minkowski' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'chebyshev' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'sorensen' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'gower' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'soergel' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'kulczynski_d' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'canberra' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'lorentzian' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'intersection' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'non-intersection' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'wavehedges' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'czekanowski' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'motyka' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'kulczynski_s' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'tanimoto' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'ruzicka' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'inner_product' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'harmonic_mean' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'cosine' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'hassebrook' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'jaccard' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'dice' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'fidelity' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'bhattacharyya' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'hellinger' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'matusita' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'squared_chord' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'squared_euclidean' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'pearson' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'neyman' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'squared_chi' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'prob_symm' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'divergence' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'clark' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'additive_symm' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'kullback-leibler' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'jeffreys' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'k_divergence' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'topsoe' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'jensen-shannon' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'jensen_difference' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'taneja' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'kumar-johnson' with unit: 'log2'; comparing: 2 vectors
#> Metric: 'avg' with unit: 'log2'; comparing: 2 vectors
#> euclidean manhattan minkowski chebyshev
#> 0.12807130 0.35250464 0.12807130 0.06345083
#> sorensen gower soergel kulczynski_d
#> 0.17625232 0.03525046 0.29968454 0.42792793
#> canberra lorentzian intersection non-intersection
#> 2.09927095 0.49712136 0.82374768 0.17625232
#> wavehedges czekanowski motyka kulczynski_s
#> 3.16657887 0.17625232 0.58812616 2.33684211
#> tanimoto ruzicka inner_product harmonic_mean
#> 0.29968454 0.70031546 0.10612245 0.94948528
#> cosine hassebrook jaccard dice
#> 0.93427641 0.86613103 0.13386897 0.07173611
#> fidelity bhattacharyya hellinger matusita
#> 0.97312397 0.03930448 0.32787819 0.23184489
#> squared_chord squared_euclidean pearson neyman
#> 0.05375205 0.01640226 0.16814418 0.36742465
#> squared_chi prob_symm divergence clark
#> 0.10102943 0.20205886 1.49843905 0.86557468
#> additive_symm kullback-leibler jeffreys k_divergence
#> 0.53556883 0.13926288 0.31761069 0.04216273
#> topsoe jensen-shannon jensen_difference taneja
#> 0.07585498 0.03792749 0.03792749 0.04147518
#> kumar-johnson avg
#> 0.62779644 0.20797774