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 |
... |
Currently not used. |
x, y |
A length- |
method |
A |
\beta
-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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/276978")}.
Other diversity measures:
heterogeneity()
,
occurrence()
,
rarefaction()
,
richness()
,
simulate()
,
turnover()
## Data from Huntley 2004, 2008
data("pueblo")
## Brainerd-Robinson measure
(C <- similarity(pueblo, "brainerd"))
plot_spot(C)
## Data from Magurran 1988, p. 166
data("aves")
## Jaccard measure (presence/absence data)
similarity(aves, "jaccard") # 0.46
## Sorenson measure (presence/absence data)
similarity(aves, "sorenson") # 0.63
# Jaccard measure (Bray's formula ; count data)
similarity(aves, "bray") # 0.44
# Morisita-Horn measure (count data)
similarity(aves, "morisita") # 0.81
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