similarity | R Documentation |
Similarity
similarity(object, ...) index_jaccard(x, y, ...) index_sorenson(x, y, ...) index_bray(x, y, ...) index_morisita(x, y, ...) index_brainerd(x, y, ...) index_binomial(x, y, ...) ## S4 method for signature 'matrix' similarity( object, method = c("brainerd", "bray", "jaccard", "morisita", "sorenson", "binomial") ) ## S4 method for signature 'data.frame' similarity( object, method = c("brainerd", "bray", "jaccard", "morisita", "sorenson", "binomial") ) ## S4 method for signature 'character,character' index_jaccard(x, y) ## S4 method for signature 'logical,logical' index_jaccard(x, y) ## S4 method for signature 'numeric,numeric' index_jaccard(x, y) ## S4 method for signature 'logical,logical' index_sorenson(x, y) ## S4 method for signature 'numeric,numeric' index_sorenson(x, y) ## S4 method for signature 'numeric,numeric' index_bray(x, y) ## S4 method for signature 'numeric,numeric' index_morisita(x, y) ## S4 method for signature 'numeric,numeric' index_brainerd(x, y) ## S4 method for signature 'numeric,numeric' index_binomial(x, y)
object |
A m x p matrix of count data. |
... |
Currently not used. |
x, y |
A length-p |
method |
A |
β-diversity can be measured by addressing similarity between pairs of samples/cases (Brainerd-Robinson, Jaccard, Morisita-Horn and Sorenson indices). Similarity between pairs of taxa/types can be measured by assessing the degree of co-occurrence (binomial co-occurrence).
Jaccard, Morisita-Horn and Sorenson indices provide a scale of similarity from 0-1 where 1 is perfect similarity and 0 is no similarity. The Brainerd-Robinson index is scaled between 0 and 200. The Binomial co-occurrence assessment approximates a Z-score.
binomial
Binomial co-occurrence assessment. This assesses the degree of co-occurrence between taxa/types within a dataset. The strongest associations are shown by large positive numbers, the strongest segregations by large negative numbers.
brainerd
Brainerd-Robinson quantitative index. This is a city-block metric of similarity between pairs of samples/cases.
bray
Sorenson quantitative index (Bray and Curtis modified version of the Sorenson index).
jaccard
Jaccard qualitative index.
morisita
Morisita-Horn quantitative index.
sorenson
Sorenson qualitative index.
similarity()
returns a stats::dist object.
index_*()
return a numeric
vector.
N. Frerebeau
Brainerd, G. W. (1951). The Place of Chronological Ordering in Archaeological Analysis. American Antiquity, 16(04), 301-313. doi: 10.2307/276979.
Bray, J. R. & Curtis, J. T. (1957). An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecological Monographs, 27(4), 325-349. doi: 10.2307/1942268.
Kintigh, K. (2006). Ceramic Dating and Type Associations. In J. Hantman and R. Most (eds.), Managing Archaeological Data: Essays in Honor of Sylvia W. Gaines. Anthropological Research Paper, 57. Tempe, AZ: Arizona State University, p. 17-26.
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi: 10.1007/978-94-015-7358-0.
Robinson, W. S. (1951). A Method for Chronologically Ordering Archaeological Deposits. American Antiquity, 16(04), 293-301. doi: 10.2307/276978.
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
rarefaction()
,
richness()
,
simulate()
,
turnover()
## Data from Huntley 2008 ceramics <- matrix( data = c(16, 9, 3, 0, 1, 13, 3, 2, 0, 0, 9, 5, 2, 5, 0, 14, 12, 3, 0, 0, 0, 26, 4, 0, 0, 1, 26, 4, 0, 0, 0, 11, 3, 13, 0, 0, 0, 17, 0, 16, 0, 0, 18, 0, 14), nrow = 9, byrow = TRUE ) rownames(ceramics) <- c("Atsinna", "Cienega", "Mirabal", "PdMuertos", "Hesh", "LowPesc", "BoxS", "Ojo Bon", "S170") colnames(ceramics) <- c("DLH-1", "DLH-2a", "DLH-2b", "DLH-2c", "DLH-4") ## Brainerd-Robinson measure (C <- similarity(ceramics, "brainerd")) plot_spot(C) ## Data from Magurran 1988, p. 166 birds <- matrix( data = c(1.4, 4.3, 2.9, 8.6, 4.2, 15.7, 2.0, 50, 1, 11.4, 11.4, 4.3, 13.0, 14.3, 8.6, 7.1, 10.0, 1.4, 2.9, 5.7, 1.4, 11.4, 2.9, 4.3, 1.4, 2.9, 0, 0, 0, 2.9, 0, 0, 0, 10, 0, 0, 5.7, 2.5, 5.7, 8.6, 5.7, 2.9, 0, 0, 2.9, 0, 0, 5.7, 0, 2.9, 0, 2.9), nrow = 2, byrow = TRUE ) rownames(birds) <- c("unmanaged", "managed") ## Jaccard measure (presence/absence data) similarity(birds, "jaccard") # 0.46 ## Sorenson measure (presence/absence data) similarity(birds, "sorenson") # 0.63 # Jaccard measure (Bray's formula ; count data) similarity(birds, "bray") # 0.44 # Morisita-Horn measure (count data) similarity(birds, "morisita") # 0.81
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