Title: | Analysis and Visualization of Archaeological Count Data |
---|---|
Description: | An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc. |
Authors: | Nicolas Frerebeau [aut, cre] (<https://orcid.org/0000-0001-5759-4944>, Université Bordeaux Montaigne), Brice Lebrun [ctb] (<https://orcid.org/0000-0001-7503-8685>, Logo designer), Matthew Peeples [ctb] (<https://orcid.org/0000-0003-4496-623X>, Arizona State University), Ben Marwick [ctb] (<https://orcid.org/0000-0001-7879-4531>, University of Washington), Anne Philippe [ctb] (<https://orcid.org/0000-0002-5331-5087>, Université de Nantes), Jean-Baptiste Fourvel [ctb] (<https://orcid.org/0000-0002-1061-4642>, CNRS), Université Bordeaux Montaigne [fnd], CNRS [fnd] |
Maintainer: | Nicolas Frerebeau <[email protected]> |
License: | GPL (>= 3) |
Version: | 3.1.1 |
Built: | 2024-10-06 05:19:22 UTC |
Source: | https://github.com/tesselle/tabula |
A dataset of birds species and abundances in managed and unmanaged areas along the River Wye (UK).
aves
aves
A data.frame
with 2 rows and 26 variables (bird species).
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Other datasets:
cantabria
,
pueblo
,
woodland
Samples randomly from the elements of object
with replacement.
## S4 method for signature 'DiversityIndex' bootstrap(object, n = 1000, f = NULL)
## S4 method for signature 'DiversityIndex' bootstrap(object, n = 1000, f = NULL)
object |
An R object (typically a DiversityIndex object). |
n |
A non-negative |
f |
A |
If f
is NULL
(the default), bootstrap()
returns a named numeric
vector with the following elements:
original
The observed value of do
applied to object
.
mean
The bootstrap estimate of mean of do
.
bias
The bootstrap estimate of bias of do
.
error
he bootstrap estimate of standard error of do
.
If f
is a function
, bootstrap()
returns the result of f
applied to
the n
values of do
.
N. Frerebeau
Other resampling methods:
jackknife()
,
resample()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Shannon diversity index (h <- heterogeneity(cantabria, method = "shannon")) ## Bootstrap resampling bootstrap(h, f = NULL) bootstrap(h, f = summary) quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Shannon diversity index (h <- heterogeneity(cantabria, method = "shannon")) ## Bootstrap resampling bootstrap(h, f = NULL) bootstrap(h, f = summary) quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant)
A dataset of design elements in engraved bones from Cantabrian Spain.
cantabria
cantabria
A data.frame
with 5 rows and 44 variables (designs).
Conkey, M. W. (1980). The Identification of prehistoric hunter-gatherer aggregation sites: The case of Altamira. Current Anthropology, 21(5), 609-630.
Kintigh, K. W. (1989). Sample Size, Significance, and Measures of Diversity. In Leonard, R. D. and Jones, G. T., Quantifying Diversity in Archaeology. New Directions in Archaeology. Cambridge: Cambridge University Press, p. 25-36.
Other datasets:
aves
,
pueblo
,
woodland
Coerce to a Data Frame
## S4 method for signature 'DiversityIndex' as.data.frame(x, row.names = NULL, optional = FALSE, ...)
## S4 method for signature 'DiversityIndex' as.data.frame(x, row.names = NULL, optional = FALSE, ...)
x |
An object. |
row.names , optional
|
Currently not used. |
... |
Currently not used. |
A data.frame
.
N. Frerebeau
Other mutators:
mutators
heterogeneity()
computes an heterogeneity or dominance index.
evenness()
computes an evenness measure.
heterogeneity(object, ...) evenness(object, ...) ## S4 method for signature 'matrix' heterogeneity( object, ..., method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson") ) ## S4 method for signature 'data.frame' heterogeneity( object, ..., method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson") ) ## S4 method for signature 'matrix' evenness( object, ..., method = c("shannon", "brillouin", "mcintosh", "simpson") ) ## S4 method for signature 'data.frame' evenness( object, ..., method = c("shannon", "brillouin", "mcintosh", "simpson") )
heterogeneity(object, ...) evenness(object, ...) ## S4 method for signature 'matrix' heterogeneity( object, ..., method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson") ) ## S4 method for signature 'data.frame' heterogeneity( object, ..., method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson") ) ## S4 method for signature 'matrix' evenness( object, ..., method = c("shannon", "brillouin", "mcintosh", "simpson") ) ## S4 method for signature 'data.frame' evenness( object, ..., method = c("shannon", "brillouin", "mcintosh", "simpson") )
object |
A |
... |
Further arguments to be passed to internal methods (see below). |
method |
A |
evenness |
A |
Diversity measurement assumes that all individuals in a specific taxa are equivalent and that all types are equally different from each other (Peet 1974). A measure of diversity can be achieved by using indices built on the relative abundance of taxa. These indices (sometimes referred to as non-parametric indices) benefit from not making assumptions about the underlying distribution of taxa abundance: they only take relative abundances of the species that are present and species richness into account. Peet (1974) refers to them as indices of heterogeneity.
Diversity indices focus on one aspect of the taxa abundance and emphasize either richness (weighting towards uncommon taxa) or dominance (weighting towards abundant taxa; Magurran 1988).
Evenness is a measure of how evenly individuals are distributed across the sample.
heterogeneity()
returns an HeterogeneityIndex object.
evenness()
returns an EvennessIndex object.
The following heterogeneity index and corresponding evenness measures are available (see Magurran 1988 for details):
berger
boone
brillouin
mcintosh
shannon
simpson
The berger
, mcintosh
and simpson
methods return a dominance index,
not the reciprocal or inverse form usually adopted, so that an increase in
the value of the index accompanies a decrease in diversity.
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Peet, R. K. (1974). The Measurement of Species Diversity. Annual Review of Ecology and Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441.
index_berger()
, index_boone()
, index_brillouin()
,
index_mcintosh()
, index_shannon()
, index_simpson()
Other diversity measures:
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Shannon diversity index (h <- heterogeneity(cantabria, method = "shannon")) (e <- evenness(cantabria, method = "shannon")) plot(h) as.data.frame(h)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Shannon diversity index (h <- heterogeneity(cantabria, method = "shannon")) (e <- evenness(cantabria, method = "shannon")) plot(h) as.data.frame(h)
Abundance-based Coverage Estimator
index_ace(x, ...) ## S4 method for signature 'numeric' index_ace(x, k = 10, na.rm = FALSE, ...)
index_ace(x, ...) ## S4 method for signature 'numeric' index_ace(x, k = 10, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
k |
A length-one |
na.rm |
A |
A numeric
vector.
N. Frerebeau
Chao, A. & Lee, S.-M. (1992). Estimating the Number of Classes via Sample Coverage. Journal of the American Statistical Association, 87(417), 210-217. doi:10.1080/01621459.1992.10475194.
Other alpha diversity measures:
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Baxter's Rarefaction
index_baxter(x, ...) ## S4 method for signature 'numeric' index_baxter(x, sample, ...)
index_baxter(x, ...) ## S4 method for signature 'numeric' index_baxter(x, sample, ...)
x |
A |
... |
Currently not used. |
sample |
A length-one |
A numeric
vector.
N. Frerebeau
Baxter, M. J. (2001). Methodological Issues in the Study of Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184.
Other alpha diversity measures:
index_ace()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Berger-Parker Dominance Index
index_berger(x, ...) ## S4 method for signature 'numeric' index_berger(x, na.rm = FALSE, ...)
index_berger(x, ...) ## S4 method for signature 'numeric' index_berger(x, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
na.rm |
A |
The Berger-Parker index expresses the proportional importance of the most abundant type. This metric is highly biased by sample size and richness, moreover it does not make use of all the information available from sample.
This is a dominance index, so that an increase in the value of the index accompanies a decrease in diversity.
A numeric
vector.
N. Frerebeau
Berger, W. H. & Parker, F. L. (1970). Diversity of Planktonic Foraminifera in Deep-Sea Sediments. Science, 168(3937), 1345-1347. doi:10.1126/science.168.3937.1345.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Binomial Co-Occurrence Assessment
index_binomial(x, y, ...) ## S4 method for signature 'numeric,numeric' index_binomial(x, y)
index_binomial(x, y, ...) ## S4 method for signature 'numeric,numeric' index_binomial(x, y)
x , y
|
A |
... |
Currently not used. |
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.
A numeric
vector.
N. Frerebeau
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.
Other beta diversity measures:
index_brainerd()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Boone Heterogeneity Measure
index_boone(x, ...) ## S4 method for signature 'matrix' index_boone(x, j = NULL, na.rm = FALSE, ...)
index_boone(x, ...) ## S4 method for signature 'matrix' index_boone(x, j = NULL, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
j |
An |
na.rm |
A |
A numeric
vector.
N. Frerebeau
Boone, J. L. (1987). Defining and Measuring Midden Catchment. American Antiquity, 52(2), 336-45. doi:10.2307/281785.
Kintigh, K. W. (1989). Sample Size, Significance, and Measures of Diversity. In Leonard, R. D. and Jones, G. T., Quantifying Diversity in Archaeology. New Directions in Archaeology. Cambridge: Cambridge University Press, p. 25-36.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Brainerd-Robinson Quantitative Index
index_brainerd(x, y, ...) ## S4 method for signature 'numeric,numeric' index_brainerd(x, y)
index_brainerd(x, y, ...) ## S4 method for signature 'numeric,numeric' index_brainerd(x, y)
x , y
|
A |
... |
Currently not used. |
A city-block metric of similarity between pairs of samples/cases.
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.
Robinson, W. S. (1951). A Method for Chronologically Ordering Archaeological Deposits. American Antiquity, 16(04), 293-301. doi:10.2307/276978.
Other beta diversity measures:
index_binomial()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Bray and Curtis modified version of the Sorenson index.
index_bray(x, y, ...) ## S4 method for signature 'numeric,numeric' index_bray(x, y)
index_bray(x, y, ...) ## S4 method for signature 'numeric,numeric' index_bray(x, y)
x , y
|
A |
... |
Currently not used. |
A numeric
vector.
N. Frerebeau
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.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Brillouin Diversity Index.
index_brillouin(x, ...) ## S4 method for signature 'numeric' index_brillouin(x, evenness = FALSE, na.rm = FALSE, ...)
index_brillouin(x, ...) ## S4 method for signature 'numeric' index_brillouin(x, evenness = FALSE, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
evenness |
A |
na.rm |
A |
The Brillouin index describes a known collection: it does not assume random sampling in an infinite population. Pielou (1975) and Laxton (1978) argues for the use of the Brillouin index in all circumstances, especially in preference to the Shannon index.
A numeric
vector.
Ramanujan approximation is used for computation if
.
N. Frerebeau
Brillouin, L. (1956). Science and information theory. New York: Academic Press.
Laxton, R. R. (1978). The measure of diversity. Journal of Theoretical Biology, 70(1), 51-67. doi:10.1016/0022-5193(78)90302-8.
Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. doi:10.4319/lo.1977.22.1.0174b
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Chao1 Estimator
index_chao1(x, ...) ## S4 method for signature 'numeric' index_chao1(x, unbiased = FALSE, improved = FALSE, na.rm = FALSE, ...)
index_chao1(x, ...) ## S4 method for signature 'numeric' index_chao1(x, unbiased = FALSE, improved = FALSE, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
unbiased |
A |
improved |
A |
na.rm |
A |
A numeric
vector.
N. Frerebeau
Chao, A. (1984). Nonparametric Estimation of the Number of Classes in a Population. Scandinavian Journal of Statistics, 11(4), 265-270.
Chiu, C.-H., Wang, Y.-T., Walther, B. A. & Chao, A. (2014). An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.1111/biom.12200.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Chao2 Estimator
index_chao2(x, ...) ## S4 method for signature 'matrix' index_chao2(x, unbiased = FALSE, improved = FALSE, ...)
index_chao2(x, ...) ## S4 method for signature 'matrix' index_chao2(x, unbiased = FALSE, improved = FALSE, ...)
x |
A |
... |
Currently not used. |
unbiased |
A |
improved |
A |
A numeric
vector.
N. Frerebeau
Chao, A. (1987). Estimating the Population Size for Capture-Recapture Data with Unequal Catchability. Biometrics 43(4), 783-791.
Chiu, C.-H., Wang, Y.-T., Walther, B. A. & Chao, A. (2014). An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.2307/2531532.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Cody Measure
index_cody(x, ...) ## S4 method for signature 'matrix' index_cody(x)
index_cody(x, ...) ## S4 method for signature 'matrix' index_cody(x)
x |
A |
... |
Currently not used. |
This assumes that the order of the matrix rows (from to
)
follows the progression along the gradient/transect.
A numeric
vector.
N. Frerebeau
Cody, M. L. (1975). Towards a theory of continental species diversity: Bird distributions over Mediterranean habitat gradients. In M. L. Cody & J. M. Diamond (Eds.), Ecology and Evolution of Communities. Cambridge, MA: Harvard University Press, p. 214-257.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Hurlbert's unbiased estimate of Sander's rarefaction.
index_hurlbert(x, ...) ## S4 method for signature 'numeric' index_hurlbert(x, sample, ...)
index_hurlbert(x, ...) ## S4 method for signature 'numeric' index_hurlbert(x, sample, ...)
x |
A |
... |
Currently not used. |
sample |
A length-one |
A numeric
vector.
N. Frerebeau
Hurlbert, S. H. (1971). The Nonconcept of Species Diversity: A Critique and Alternative Parameters. Ecology, 52(4), 577-586. doi:10.2307/1934145.
Sander, H. L. (1968). Marine Benthic Diversity: A Comparative Study. The American Naturalist, 102(925), 243-282.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Incidence-based Coverage Estimator
index_ice(x, ...) ## S4 method for signature 'matrix' index_ice(x, k = 10, ...)
index_ice(x, ...) ## S4 method for signature 'matrix' index_ice(x, k = 10, ...)
x |
A |
... |
Currently not used. |
k |
A length-one |
A numeric
vector.
N. Frerebeau
Chao, A. & Chiu, C.-H. (2016). Species Richness: Estimation and Comparison. In Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, B., Ruggeri, F. & Teugels, J. L. (Eds.), Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd., 1-26. doi:10.1002/9781118445112.stat03432.pub2
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Jaccard Index
index_jaccard(x, y, ...) ## 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)
index_jaccard(x, y, ...) ## 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)
x , y
|
A |
... |
Currently not used. |
A numeric
vector.
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_cody()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Margalef Richness Index
index_margalef(x, ...) ## S4 method for signature 'numeric' index_margalef(x, na.rm = FALSE, ...)
index_margalef(x, ...) ## S4 method for signature 'numeric' index_margalef(x, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
na.rm |
A |
A numeric
vector.
N. Frerebeau
Margalef, R. (1958). Information Theory in Ecology. General Systems, 3, 36-71.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
McIntosh Dominance Index.
index_mcintosh(x, ...) ## S4 method for signature 'numeric' index_mcintosh(x, evenness = FALSE, na.rm = FALSE, ...)
index_mcintosh(x, ...) ## S4 method for signature 'numeric' index_mcintosh(x, evenness = FALSE, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
evenness |
A |
na.rm |
A |
The McIntosh index expresses the heterogeneity of a sample in geometric
terms. It describes the sample as a point of a -dimensional
hypervolume and uses the Euclidean distance of this point from the origin.
This is a dominance index, so that an increase in the value of the index accompanies a decrease in diversity.
A numeric
vector.
N. Frerebeau
McIntosh, R. P. (1967). An Index of Diversity and the Relation of Certain Concepts to Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Menhinick Richness Index
index_menhinick(x, ...) ## S4 method for signature 'numeric' index_menhinick(x, na.rm = FALSE, ...)
index_menhinick(x, ...) ## S4 method for signature 'numeric' index_menhinick(x, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
na.rm |
A |
A numeric
vector.
N. Frerebeau
Menhinick, E. F. (1964). A Comparison of Some Species-Individuals Diversity Indices Applied to Samples of Field Insects. Ecology, 45(4), 859-861. doi:10.2307/1934933.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_shannon()
,
index_simpson()
,
index_squares()
,
observed()
Morisita-Horn Quantitative Index
index_morisita(x, y, ...) ## S4 method for signature 'numeric,numeric' index_morisita(x, y)
index_morisita(x, y, ...) ## S4 method for signature 'numeric,numeric' index_morisita(x, y)
x , y
|
A |
... |
Currently not used. |
A numeric
vector.
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Routledge Measures
index_routledge1(x, ...) index_routledge2(x, ...) index_routledge3(x, ...) ## S4 method for signature 'matrix' index_routledge1(x) ## S4 method for signature 'matrix' index_routledge2(x) ## S4 method for signature 'matrix' index_routledge3(x)
index_routledge1(x, ...) index_routledge2(x, ...) index_routledge3(x, ...) ## S4 method for signature 'matrix' index_routledge1(x) ## S4 method for signature 'matrix' index_routledge2(x) ## S4 method for signature 'matrix' index_routledge3(x)
x |
A |
... |
Currently not used. |
This assumes that the order of the matrix rows (from to
)
follows the progression along the gradient/transect.
A numeric
vector.
N. Frerebeau
Routledge, R. D. (1977). On Whittaker's Components of Diversity. Ecology, 58(5), 1120-1127. doi:10.2307/1936932.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_sorenson()
,
index_whittaker()
,
index_wilson()
Shannon-Wiener Diversity Index
index_shannon(x, ...) ## S4 method for signature 'numeric' index_shannon( x, evenness = FALSE, unbiased = FALSE, ACE = FALSE, base = exp(1), na.rm = FALSE, ... )
index_shannon(x, ...) ## S4 method for signature 'numeric' index_shannon( x, evenness = FALSE, unbiased = FALSE, ACE = FALSE, base = exp(1), na.rm = FALSE, ... )
x |
A |
... |
Currently not used. |
evenness |
A |
unbiased |
A |
ACE |
A |
base |
A positive |
na.rm |
A |
The Shannon index assumes that individuals are randomly sampled from an infinite population and that all taxa are represented in the sample (it does not reflect the sample size). The main source of error arises from the failure to include all taxa in the sample: this error increases as the proportion of species discovered in the sample declines (Peet 1974, Magurran 1988). The maximum likelihood estimator (MLE) is used for the relative abundance, this is known to be negatively biased by sample size.
A numeric
vector.
N. Frerebeau
Peet, R. K. (1974). The Measurement of Species Diversity. Annual Review of Ecology and Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441.
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Shannon, C. E. (1948). A Mathematical Theory of Communication. The Bell System Technical Journal, 27, 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_simpson()
,
index_squares()
,
observed()
Simpson Dominance Index
index_simpson(x, ...) ## S4 method for signature 'numeric' index_simpson(x, evenness = FALSE, unbiased = FALSE, na.rm = FALSE, ...)
index_simpson(x, ...) ## S4 method for signature 'numeric' index_simpson(x, evenness = FALSE, unbiased = FALSE, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
evenness |
A |
unbiased |
A |
na.rm |
A |
The Simpson index expresses the probability that two individuals randomly
picked from a finite sample belong to two different types. It can be
interpreted as the weighted mean of the proportional abundances. This
metric is a true probability value, it ranges from (all taxa are
equally present) to
(one taxon dominates the community completely).
This is a dominance index, so that an increase in the value of the index accompanies a decrease in diversity.
A numeric
vector.
N. Frerebeau
Simpson, E. H. (1949). Measurement of Diversity. Nature, 163(4148), 688-688. doi:10.1038/163688a0.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_squares()
,
observed()
Sorenson Qualitative Index
index_sorenson(x, y, ...) ## S4 method for signature 'logical,logical' index_sorenson(x, y) ## S4 method for signature 'numeric,numeric' index_sorenson(x, y)
index_sorenson(x, y, ...) ## S4 method for signature 'logical,logical' index_sorenson(x, y) ## S4 method for signature 'numeric,numeric' index_sorenson(x, y)
x , y
|
A |
... |
Currently not used. |
A numeric
vector.
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_whittaker()
,
index_wilson()
Squares Estimator
index_squares(x, ...) ## S4 method for signature 'numeric' index_squares(x, na.rm = FALSE, ...)
index_squares(x, ...) ## S4 method for signature 'numeric' index_squares(x, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
na.rm |
A |
A numeric
vector.
N. Frerebeau
Alroy, J. (2018). Limits to Species Richness in Terrestrial Communities. Ecology Letters, 21(12), 1781-1789. doi:10.1111/ele.13152.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
observed()
Whittaker Measure
index_whittaker(x, ...) ## S4 method for signature 'matrix' index_whittaker(x)
index_whittaker(x, ...) ## S4 method for signature 'matrix' index_whittaker(x)
x |
A |
... |
Currently not used. |
This assumes that the order of the matrix rows (from to
)
follows the progression along the gradient/transect.
A numeric
vector.
N. Frerebeau
Whittaker, R. H. (1960). Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30(3), 279-338. doi:10.2307/1943563.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_wilson()
Wilson Measure
index_wilson(x, ...) ## S4 method for signature 'matrix' index_wilson(x)
index_wilson(x, ...) ## S4 method for signature 'matrix' index_wilson(x)
x |
A |
... |
Currently not used. |
This assumes that the order of the matrix rows (from to
)
follows the progression along the gradient/transect.
A numeric
vector.
N. Frerebeau
Wilson, M. V., & Shmida, A. (1984). Measuring Beta Diversity with Presence-Absence Data. The Journal of Ecology, 72(3), 1055-1064. doi:10.2307/2259551.
Other beta diversity measures:
index_binomial()
,
index_brainerd()
,
index_bray()
,
index_cody()
,
index_jaccard()
,
index_morisita()
,
index_routledge
,
index_sorenson()
,
index_whittaker()
Jackknife Estimation
## S4 method for signature 'DiversityIndex' jackknife(object, f = NULL)
## S4 method for signature 'DiversityIndex' jackknife(object, f = NULL)
object |
An R object (typically a DiversityIndex object). |
f |
A |
If f
is NULL
(the default), jackknife()
returns a named numeric
vector with the following elements:
original
The observed value of do
applied to object
.
mean
The jackknife estimate of mean of do
.
bias
The jackknife estimate of bias of do
.
error
he jackknife estimate of standard error of do
.
If f
is a function
, jackknife()
returns the result of f
applied to
the leave-one-out values of do
.
N. Frerebeau
Other resampling methods:
bootstrap()
,
resample()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Shannon diversity index (h <- heterogeneity(cantabria, method = "shannon")) ## Jackknife resampling jackknife(h) jackknife(h, f = summary)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Shannon diversity index (h <- heterogeneity(cantabria, method = "shannon")) ## Jackknife resampling jackknife(h) jackknife(h, f = summary)
matrigraph()
produces a heatmap highlighting the deviations from
independence.
pvi()
computes for each cell of a numeric matrix the percentage to the
column theoretical independence value.
matrigraph(object, ...) pvi(object, ...) ## S4 method for signature 'matrix' pvi(object) ## S4 method for signature 'data.frame' pvi(object) ## S4 method for signature 'matrix' matrigraph(object, reverse = FALSE, axes = TRUE, ...) ## S4 method for signature 'data.frame' matrigraph(object, reverse = FALSE, ...)
matrigraph(object, ...) pvi(object, ...) ## S4 method for signature 'matrix' pvi(object) ## S4 method for signature 'data.frame' pvi(object) ## S4 method for signature 'matrix' matrigraph(object, reverse = FALSE, axes = TRUE, ...) ## S4 method for signature 'data.frame' matrigraph(object, reverse = FALSE, ...)
object |
A |
... |
Currently not used. |
reverse |
A |
axes |
A |
PVI (in french "pourcentages de valeur d'indépendance") is calculated for
each cell as the percentage to the column theoretical independence value:
PVI greater than represent positive deviations from the
independence, whereas PVI smaller than
represent negative
deviations (Desachy 2004).
The PVI matrix allows to explore deviations from independence (an
intuitive approach to ), in such a way that a
high-contrast matrix has quite significant deviations,
with a low risk of being due to randomness (Desachy 2004).
matrigraph()
displays the deviations from independence:
If the PVI is equal to (statistical independence), the cell of the
matrix is filled in grey.
If the PVI is less than (negative deviation from independence),
the size of the grey square is proportional to the PVI (the white margin
thus represents the fraction of negative deviation).
If the PVI is greater than (positive deviation), a black
square representing the fraction of positive deviations is
superimposed. For large positive deviations (PVI greater than
),
the cell in filled in black.
If reverse
is TRUE
, the fraction of negative deviations is displayed
as a white square.
matrigraph()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
N. Frerebeau
Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396.
Other plot methods:
plot_bertin()
,
plot_diceleraas()
,
plot_ford()
,
plot_heatmap()
,
plot_rank()
,
plot_spot()
,
seriograph()
## Data from Desachy 2004 data("compiegne", package = "folio") ## Matrigraph matrigraph(compiegne) matrigraph(compiegne, reverse = TRUE) ## Compute PVI counts_pvi <- pvi(compiegne) plot_heatmap(counts_pvi, col = khroma::color("iridescent")(12))
## Data from Desachy 2004 data("compiegne", package = "folio") ## Matrigraph matrigraph(compiegne) matrigraph(compiegne, reverse = TRUE) ## Compute PVI counts_pvi <- pvi(compiegne) plot_heatmap(counts_pvi, col = khroma::color("iridescent")(12))
Getters and setters to extract or replace parts of an object.
get_method(x) ## S4 method for signature 'DiversityIndex' labels(object, ...) ## S4 method for signature 'RarefactionIndex' labels(object, ...) ## S4 method for signature 'DiversityIndex' get_method(x)
get_method(x) ## S4 method for signature 'DiversityIndex' labels(object, ...) ## S4 method for signature 'RarefactionIndex' labels(object, ...) ## S4 method for signature 'DiversityIndex' get_method(x)
object , x
|
An R object from which to get or set element(s). |
... |
Currently not used. |
labels()
returns a suitable set of labels from an object for use in
printing or plotting.
N. Frerebeau
Other mutators:
data.frame
Number of Observed Species
observed(x, ...) singleton(x, ...) doubleton(x, ...) ## S4 method for signature 'numeric' observed(x, na.rm = FALSE, ...) ## S4 method for signature 'numeric' singleton(x, na.rm = FALSE, ...) ## S4 method for signature 'numeric' doubleton(x, na.rm = FALSE, ...)
observed(x, ...) singleton(x, ...) doubleton(x, ...) ## S4 method for signature 'numeric' observed(x, na.rm = FALSE, ...) ## S4 method for signature 'numeric' singleton(x, na.rm = FALSE, ...) ## S4 method for signature 'numeric' doubleton(x, na.rm = FALSE, ...)
x |
A |
... |
Currently not used. |
na.rm |
A |
A numeric
vector.
Other alpha diversity measures:
index_ace()
,
index_baxter()
,
index_berger()
,
index_boone()
,
index_brillouin()
,
index_chao1()
,
index_chao2()
,
index_hurlbert()
,
index_ice()
,
index_margalef()
,
index_mcintosh()
,
index_menhinick()
,
index_shannon()
,
index_simpson()
,
index_squares()
Co-Occurrence
occurrence(object, ...) ## S4 method for signature 'matrix' occurrence(object) ## S4 method for signature 'data.frame' occurrence(object)
occurrence(object, ...) ## S4 method for signature 'matrix' occurrence(object) ## S4 method for signature 'data.frame' occurrence(object)
object |
A |
... |
Currently not used. |
A co-occurrence matrix is a symmetric matrix with zeros on its main diagonal, which works out how many times each pairs of taxa/types occur together in at least one sample.
A stats::dist object.
N. Frerebeau
Other diversity measures:
heterogeneity()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Plot spot diagram of a co-occurrence matrix occ <- occurrence(cantabria) plot_spot(occ)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Plot spot diagram of a co-occurrence matrix occ <- occurrence(cantabria) plot_spot(occ)
Plots a Bertin diagram.
plot_bertin(object, ...) ## S4 method for signature 'matrix' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, color = c("white", "black"), flip = TRUE, axes = TRUE, ... ) ## S4 method for signature 'data.frame' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, color = c("white", "black"), flip = TRUE, axes = TRUE, ... )
plot_bertin(object, ...) ## S4 method for signature 'matrix' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, color = c("white", "black"), flip = TRUE, axes = TRUE, ... ) ## S4 method for signature 'data.frame' plot_bertin( object, threshold = NULL, freq = FALSE, margin = 1, color = c("white", "black"), flip = TRUE, axes = TRUE, ... )
object |
A |
... |
Currently not used. |
threshold |
A |
freq |
A |
margin |
An |
color |
A vector of colors or a |
flip |
A |
axes |
A |
plot_bertin()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
As de Falguerolles et al. (1997) points out: "In abstract terms, a Bertin matrix is a matrix of displays. ... To fix ideas, think of a data matrix, variable by case, with real valued variables. For each variable, draw a bar chart of variable value by case. High-light all bars representing a value above some sample threshold for that variable."
N. Frerebeau
Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Bibliothèque Scientifique.
de Falguerolles, A., Friedrich, F. & Sawitzki, G. (1997). A Tribute to J. Bertin's Graphical Data Analysis. In W. Badilla & F. Faulbaum (eds.), SoftStat '97: Advances in Statistical Software 6. Stuttgart: Lucius & Lucius, p. 11-20.
Other plot methods:
matrigraph()
,
plot_diceleraas()
,
plot_ford()
,
plot_heatmap()
,
plot_rank()
,
plot_spot()
,
seriograph()
## Data from Lipo et al. 2015 data("mississippi", package = "folio") ## Plot a Bertin diagram... ## ...without threshold plot_bertin(mississippi) ## ...with the variable mean as threshold plot_bertin(mississippi, threshold = mean) ## Plot conditional proportions plot_bertin(mississippi, freq = TRUE, margin = 1) plot_bertin(mississippi, freq = TRUE, margin = 2)
## Data from Lipo et al. 2015 data("mississippi", package = "folio") ## Plot a Bertin diagram... ## ...without threshold plot_bertin(mississippi) ## ...with the variable mean as threshold plot_bertin(mississippi, threshold = mean) ## Plot conditional proportions plot_bertin(mississippi, freq = TRUE, margin = 1) plot_bertin(mississippi, freq = TRUE, margin = 2)
Plots a Dice-Leraas diagram.
plot_diceleraas(object, ...) ## S4 method for signature 'matrix' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... ) ## S4 method for signature 'data.frame' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... )
plot_diceleraas(object, ...) ## S4 method for signature 'matrix' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... ) ## S4 method for signature 'data.frame' plot_diceleraas( object, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = FALSE, panel.first = NULL, panel.last = NULL, ... )
object |
A |
... |
Further graphical parameters. |
main |
A |
sub |
A |
ann |
A |
axes |
A |
frame.plot |
A |
panel.first |
An an |
panel.last |
An |
In a Dice-Leraas diagram, the horizontal line represents the range of data (min-max) and the small vertical line indicates the mean. The black rectangle is twice the standard error on the mean, while the white rectangle is one standard deviation on either side of the mean.
plot_diceleraas()
is called for its side-effects: it results in a
graphic being displayed (invisibly returns object
).
N. Frerebeau
Dice, L. R., & Leraas, H. J. (1936). A Graphic Method for Comparing Several Sets of Measurements. Contributions from the Laboratory of Vertebrate Genetics, 3: 1-3.
Hubbs, C. L., & C. Hubbs (1953). An Improved Graphical Analysis and Comparison of Series of Samples. Systematic Biology, 2(2): 49-56. doi:10.2307/sysbio/2.2.49.
Simpson, G. G., Roe, A., & Lewontin, R. C. Quantitative Zoology. New York: Harcourt, Brace and Company, 1960.
Other plot methods:
matrigraph()
,
plot_bertin()
,
plot_ford()
,
plot_heatmap()
,
plot_rank()
,
plot_spot()
,
seriograph()
## Data from Desachy 2004 data("compiegne", package = "folio") ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)
## Data from Desachy 2004 data("compiegne", package = "folio") ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)
Diversity Plot
## S4 method for signature 'DiversityIndex,missing' plot( x, log = "x", col.mean = "#DDAA33", col.interval = "#004488", lty.mean = "solid", lty.interval = "dashed", lwd.mean = 1, lwd.interval = 1, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, ... )
## S4 method for signature 'DiversityIndex,missing' plot( x, log = "x", col.mean = "#DDAA33", col.interval = "#004488", lty.mean = "solid", lty.interval = "dashed", lwd.mean = 1, lwd.interval = 1, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, ... )
x |
A DiversityIndex object to be plotted. |
log |
A |
col.mean , col.interval
|
A |
lty.mean , lty.interval
|
A |
lwd.mean , lwd.interval
|
A non-negative |
main |
A |
sub |
A |
ann |
A |
axes |
A |
frame.plot |
A |
panel.first |
An an |
panel.last |
An |
... |
Further graphical parameters to be passed to
|
plot()
is called for its side-effects: it results in a graphic being
displayed (invisibly returns x
).
N. Frerebeau
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = "shannon") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = "observed") r_sim <- simulate(r) plot(r_sim)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = "shannon") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = "observed") r_sim <- simulate(r) plot(r_sim)
Plots a Ford (battleship curve) diagram.
plot_ford(object, ...) ## S4 method for signature 'matrix' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... ) ## S4 method for signature 'data.frame' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... )
plot_ford(object, ...) ## S4 method for signature 'matrix' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... ) ## S4 method for signature 'data.frame' plot_ford( object, weights = FALSE, EPPM = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... )
object |
A |
... |
Currently not used. |
weights |
A |
EPPM |
A |
fill |
The color for filling the bars. |
border |
The color to draw the borders. |
axes |
A |
plot_ford()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
N. Frerebeau
Ford, J. A. (1962). A quantitative method for deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.
Other plot methods:
matrigraph()
,
plot_bertin()
,
plot_diceleraas()
,
plot_heatmap()
,
plot_rank()
,
plot_spot()
,
seriograph()
## Data from Lipo et al. 2015 data("mississippi", package = "folio") ## Plot a Ford diagram plot_ford(mississippi) plot_ford(mississippi, weights = TRUE)
## Data from Lipo et al. 2015 data("mississippi", package = "folio") ## Plot a Ford diagram plot_ford(mississippi) plot_ford(mississippi, weights = TRUE)
Plots a heatmap.
plot_heatmap(object, ...) ## S4 method for signature 'matrix' plot_heatmap( object, color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'data.frame' plot_heatmap( object, color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'dist' plot_heatmap( object, color = NULL, diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )
plot_heatmap(object, ...) ## S4 method for signature 'matrix' plot_heatmap( object, color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'data.frame' plot_heatmap( object, color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, fixed_ratio = TRUE, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'dist' plot_heatmap( object, color = NULL, diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )
object |
A |
... |
Currently not used. |
color |
A vector of colors or a |
diag |
A |
upper |
A |
lower |
A |
freq |
A |
margin |
An |
fixed_ratio |
A |
axes |
A |
legend |
A |
plot_heatmap()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
N. Frerebeau
Other plot methods:
matrigraph()
,
plot_bertin()
,
plot_diceleraas()
,
plot_ford()
,
plot_rank()
,
plot_spot()
,
seriograph()
## Data from Lipo et al. 2015 data("mississippi", package = "folio") ## Plot raw data plot_heatmap(mississippi) ## Change colors plot_heatmap(mississippi, color = color("iridescent")) ## Plot conditional proportions plot_heatmap(mississippi, freq = TRUE, margin = 1) plot_heatmap(mississippi, freq = TRUE, margin = 2)
## Data from Lipo et al. 2015 data("mississippi", package = "folio") ## Plot raw data plot_heatmap(mississippi) ## Change colors plot_heatmap(mississippi, color = color("iridescent")) ## Plot conditional proportions plot_heatmap(mississippi, freq = TRUE, margin = 1) plot_heatmap(mississippi, freq = TRUE, margin = 2)
Plots a rank vs relative abundance diagram.
plot_rank(object, ...) ## S4 method for signature 'matrix' plot_rank( object, log = NULL, color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... ) ## S4 method for signature 'data.frame' plot_rank( object, log = NULL, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... )
plot_rank(object, ...) ## S4 method for signature 'matrix' plot_rank( object, log = NULL, color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... ) ## S4 method for signature 'data.frame' plot_rank( object, log = NULL, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... )
object |
A |
... |
Further graphical parameters. |
log |
A |
color |
A vector of colors (will be mapped to the rownames of |
symbol |
A specification for the line type (will be mapped to
the rownames of |
main |
A |
sub |
A |
ann |
A |
axes |
A |
frame.plot |
A |
panel.first |
An an |
panel.last |
An |
legend |
A |
plot_rank()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Other plot methods:
matrigraph()
,
plot_bertin()
,
plot_diceleraas()
,
plot_ford()
,
plot_heatmap()
,
plot_spot()
,
seriograph()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Plot rank vs abundance plot_rank(cantabria) ## Change graphical parameters plot_rank(cantabria, color = color("bright"), symbol = 15:19)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Plot rank vs abundance plot_rank(cantabria) ## Change graphical parameters plot_rank(cantabria, color = color("bright"), symbol = 15:19)
Rarefaction Plot
## S4 method for signature 'RarefactionIndex,missing' plot( x, color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topleft"), ... )
## S4 method for signature 'RarefactionIndex,missing' plot( x, color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topleft"), ... )
x |
A RarefactionIndex object to be plotted. |
color |
A vector of colors (will be mapped to the rownames of |
symbol |
A specification for the line type (will be mapped to
the names of |
main |
A |
sub |
A |
ann |
A |
axes |
A |
frame.plot |
A |
panel.first |
An an |
panel.last |
An |
legend |
A |
... |
Further graphical parameters to be passed to
|
plot()
is called for its side-effects: it results in a graphic being
displayed (invisibly returns x
).
N. Frerebeau
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = "baxter") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters plot(rare, color = color("bright")(5), symbol = 1:5)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = "baxter") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters plot(rare, color = color("bright")(5), symbol = 1:5)
Plots a spot matrix.
plot_spot(object, ...) ## S4 method for signature 'matrix' plot_spot( object, type = c("ring", "plain"), color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'data.frame' plot_spot( object, type = c("ring", "plain"), color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'dist' plot_spot( object, type = c("ring", "plain"), color = NULL, diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )
plot_spot(object, ...) ## S4 method for signature 'matrix' plot_spot( object, type = c("ring", "plain"), color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'data.frame' plot_spot( object, type = c("ring", "plain"), color = NULL, diag = TRUE, upper = TRUE, lower = TRUE, freq = FALSE, margin = 1, axes = TRUE, legend = TRUE, ... ) ## S4 method for signature 'dist' plot_spot( object, type = c("ring", "plain"), color = NULL, diag = FALSE, upper = FALSE, lower = !upper, axes = TRUE, legend = TRUE, ... )
object |
A |
... |
Currently not used. |
type |
A |
color |
A vector of colors or a |
diag |
A |
upper |
A |
lower |
A |
freq |
A |
margin |
An |
axes |
A |
legend |
A |
The spot matrix can be considered as a variant of the Bertin diagram where the data are first transformed to relative frequencies.
plot_spot()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
Adapted from Dan Gopstein's original idea.
N. Frerebeau
Other plot methods:
matrigraph()
,
plot_bertin()
,
plot_diceleraas()
,
plot_ford()
,
plot_heatmap()
,
plot_rank()
,
seriograph()
## Data from Huntley 2004, 2008 data("pueblo") ## Plot spot diagram of count data plot_spot(pueblo, type = "ring") plot_spot(pueblo, type = "plain") ## Plot conditional proportions plot_spot(pueblo, freq = TRUE, margin = 1) plot_spot(pueblo, freq = TRUE, margin = 2)
## Data from Huntley 2004, 2008 data("pueblo") ## Plot spot diagram of count data plot_spot(pueblo, type = "ring") plot_spot(pueblo, type = "plain") ## Plot conditional proportions plot_spot(pueblo, freq = TRUE, margin = 1) plot_spot(pueblo, freq = TRUE, margin = 2)
Diversity Profiles
profiles(object, ...) ## S4 method for signature 'matrix' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... ) ## S4 method for signature 'data.frame' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... )
profiles(object, ...) ## S4 method for signature 'matrix' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... ) ## S4 method for signature 'data.frame' profiles( object, alpha = seq(from = 0, to = 4, by = 0.04), color = NULL, symbol = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "topright"), ... )
object |
A |
... |
Further graphical parameters to be passed to
|
alpha |
A |
color |
A vector of colors (will be mapped to the rownames of |
symbol |
A specification for the line type (will be mapped to
the rownames of |
main |
A |
sub |
A |
ann |
A |
axes |
A |
frame.plot |
A |
panel.first |
An an |
panel.last |
An |
legend |
A |
If the profiles cross, the diversities are non-comparable across samples.
profiles()
is called for its side-effects: it results in a graphic being
displayed (invisibly returns object
).
N. Frerebeau
Tóthmérész, B. (1995). Comparison of Different Methods for Diversity Ordering. Journal of Vegetation Science, 6(2), 283-290. doi:10.2307/3236223.
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Replicate fig. 1 of Tóthmérész 1995 spc <- matrix( data = c(33, 29, 28, 5, 5, 0, 0, 42, 30, 10, 8, 5, 5, 0, 32, 21, 16, 12, 9, 6, 4), nrow = 3, byrow = TRUE, dimnames = list(c("Z", "B", "C"), NULL) ) profiles(spc, color = color("bright"))
## Replicate fig. 1 of Tóthmérész 1995 spc <- matrix( data = c(33, 29, 28, 5, 5, 0, 0, 42, 30, 10, 8, 5, 5, 0, 32, 21, 16, 12, 9, 6, 4), nrow = 3, byrow = TRUE, dimnames = list(c("Z", "B", "C"), NULL) ) profiles(spc, color = color("bright"))
A dataset of ceramic counts from the Zuni region.
pueblo
pueblo
A data.frame
with 9 rows and 5 variables (compositional groups).
Huntley, D. L. (2004). Interaction, Boundaries, and Identities: A Multiscalar Approach to the Organizational Scale of Pueblo IV Zuni Society. Ph.D. Dissertation, Arizona State University.
Huntley, D. L. (2022). Ancestral Zuni Glaze-Decorated Pottery: Viewing Pueblo IV Regional Organization through Ceramic Production and Exchange. Anthropological Papers of the University of Arizona 72. Tucson: University of Arizona Press. doi:10.2307/j.ctv2ngx5n8.
Other datasets:
aves
,
cantabria
,
woodland
Rarefaction
rarefaction(object, ...) ## S4 method for signature 'matrix' rarefaction(object, sample = NULL, method = c("hurlbert", "baxter"), step = 1) ## S4 method for signature 'data.frame' rarefaction(object, sample = NULL, method = c("hurlbert", "baxter"), step = 1)
rarefaction(object, ...) ## S4 method for signature 'matrix' rarefaction(object, sample = NULL, method = c("hurlbert", "baxter"), step = 1) ## S4 method for signature 'data.frame' rarefaction(object, sample = NULL, method = c("hurlbert", "baxter"), step = 1)
object |
A |
... |
Currently not used. |
sample |
A length-one |
method |
A |
step |
An |
A RarefactionIndex object.
The following rarefaction measures are available for count data:
baxter
hurlbert
Hurlbert's unbiased estimate of Sander's rarefaction.
The number of observed taxa, provides an instantly comprehensible
expression of diversity. While the number of taxa within a sample
is easy to ascertain, as a term, it makes little sense: some taxa
may not have been seen, or there may not be a fixed number of taxa
(e.g. in an open system; Peet 1974). As an alternative, richness
() can be used for the concept of taxa number (McIntosh 1967).
It is not always possible to ensure that all sample sizes are equal
and the number of different taxa increases with sample size and
sampling effort (Magurran 1988). Then, rarefaction
() is the number of taxa expected if all samples were of a
standard size (i.e. taxa per fixed number of individuals).
Rarefaction assumes that imbalances between taxa are due to sampling and
not to differences in actual abundances.
N. Frerebeau
index_baxter()
, index_hurlbert()
, plot()
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = "baxter") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters plot(rare, color = color("bright")(5), symbol = 1:5)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = "baxter") plot(rare, panel.first = graphics::grid()) ## Change graphical parameters plot(rare, color = color("bright")(5), symbol = 1:5)
Simulates observations from a multinomial distribution.
resample(object, ...) ## S4 method for signature 'numeric' resample(object, do, n, size = sum(object), ..., f = NULL)
resample(object, ...) ## S4 method for signature 'numeric' resample(object, do, n, size = sum(object), ..., f = NULL)
object |
A |
... |
Extra arguments passed to |
do |
A |
n |
A non-negative |
size |
A non-negative |
f |
A |
If f
is NULL
, resample()
returns the n
values of do
. Else,
returns the result of f
applied to the n
values of do
.
N. Frerebeau
Other resampling methods:
bootstrap()
,
jackknife()
## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) ## Estimate the 25th, 50th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) } resample(x, n = 100, do = median, f = quant)
## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) ## Estimate the 25th, 50th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) } resample(x, n = 100, do = median, f = quant)
richness()
computes sample richness.
composition()
computes asymptotic species richness.
richness(object, ...) composition(object, ...) ## S4 method for signature 'matrix' richness(object, ..., method = c("observed", "margalef", "menhinick")) ## S4 method for signature 'data.frame' richness(object, ..., method = c("observed", "margalef", "menhinick")) ## S4 method for signature 'matrix' composition(object, ..., method = c("chao1", "ace", "squares", "chao2", "ice")) ## S4 method for signature 'data.frame' composition(object, ..., method = c("chao1", "ace", "squares", "chao2", "ice"))
richness(object, ...) composition(object, ...) ## S4 method for signature 'matrix' richness(object, ..., method = c("observed", "margalef", "menhinick")) ## S4 method for signature 'data.frame' richness(object, ..., method = c("observed", "margalef", "menhinick")) ## S4 method for signature 'matrix' composition(object, ..., method = c("chao1", "ace", "squares", "chao2", "ice")) ## S4 method for signature 'data.frame' composition(object, ..., method = c("chao1", "ace", "squares", "chao2", "ice"))
object |
A |
... |
Further arguments to be passed to internal methods (see below). |
method |
A |
richness()
returns a RichnessIndex object.
composition()
returns a CompositionIndex object.
The number of observed taxa, provides an instantly comprehensible
expression of diversity. While the number of taxa within a sample
is easy to ascertain, as a term, it makes little sense: some taxa
may not have been seen, or there may not be a fixed number of taxa
(e.g. in an open system; Peet 1974). As an alternative, richness
() can be used for the concept of taxa number (McIntosh 1967).
It is not always possible to ensure that all sample sizes are equal
and the number of different taxa increases with sample size and
sampling effort (Magurran 1988). Then, rarefaction
() is the number of taxa expected if all samples were of a
standard size (i.e. taxa per fixed number of individuals).
Rarefaction assumes that imbalances between taxa are due to sampling and
not to differences in actual abundances.
The following richness measures are available for count data:
observed
Number of observed taxa/types.
margalef
menhinick
The following measures are available for count data:
ace
chao1
(improved/unbiased) Chao1 estimator.
squares
The following measures are available for replicated incidence data:
ice
chao2
(improved/unbiased) Chao2 estimator.
N. Frerebeau
Kintigh, K. W. (1989). Sample Size, Significance, and Measures of Diversity. In Leonard, R. D. and Jones, G. T., Quantifying Diversity in Archaeology. New Directions in Archaeology. Cambridge: Cambridge University Press, p. 25-36.
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Magurran, A E. & Brian J. McGill (2011). Biological Diversity: Frontiers in Measurement and Assessment. Oxford: Oxford University Press.
McIntosh, R. P. (1967). An Index of Diversity and the Relation of Certain Concepts to Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674.
Peet, R. K. (1974). The Measurement of Species Diversity. Annual Review of Ecology and Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441.
index_margalef()
, index_menhinick()
, index_ace()
,
index_chao1()
, index_squares()
, index_ice()
, index_chao2()
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Magurran 1988, p. 128-129 trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0), nrow = 2, byrow = TRUE, dimnames = list(c("A", "B"), NULL)) ## Margalef and Menhinick index richness(trap, method = "margalef") # 2.55 1.88 richness(trap, method = "menhinick") # 1.95 1.66 ## Data from Chao & Chiu 2016 brazil <- matrix( data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41, 45, 46, 49, 52, 89, 110, 123, 140), times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4, 3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)), nrow = 1, byrow = TRUE ) ## Chao1-type estimators (asymptotic species richness) composition(brazil, method = c("chao1"), unbiased = FALSE) # 461.625 composition(brazil, method = c("ace"), k = 10) # 445.822
## Data from Magurran 1988, p. 128-129 trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0), nrow = 2, byrow = TRUE, dimnames = list(c("A", "B"), NULL)) ## Margalef and Menhinick index richness(trap, method = "margalef") # 2.55 1.88 richness(trap, method = "menhinick") # 1.95 1.66 ## Data from Chao & Chiu 2016 brazil <- matrix( data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41, 45, 46, 49, 52, 89, 110, 123, 140), times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4, 3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)), nrow = 1, byrow = TRUE ) ## Chao1-type estimators (asymptotic species richness) composition(brazil, method = c("chao1"), unbiased = FALSE) # 461.625 composition(brazil, method = c("ace"), k = 10) # 445.822
seriograph()
produces a Ford diagram highlighting the relationships
between rows and columns.
eppm()
computes for each cell of a numeric matrix the positive
difference from the column mean percentage.
seriograph(object, ...) eppm(object, ...) ## S4 method for signature 'matrix' eppm(object) ## S4 method for signature 'data.frame' eppm(object) ## S4 method for signature 'matrix' seriograph( object, weights = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... ) ## S4 method for signature 'data.frame' seriograph( object, weights = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... )
seriograph(object, ...) eppm(object, ...) ## S4 method for signature 'matrix' eppm(object) ## S4 method for signature 'data.frame' eppm(object) ## S4 method for signature 'matrix' seriograph( object, weights = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... ) ## S4 method for signature 'data.frame' seriograph( object, weights = FALSE, fill = "darkgrey", border = NA, axes = TRUE, ... )
object |
A |
... |
Currently not used. |
weights |
A |
fill |
The color for filling the bars. |
border |
The color to draw the borders. |
axes |
A |
The positive difference from the column mean percentage (in french "écart positif au pourcentage moyen", EPPM) represents a deviation from the situation of statistical independence. As independence can be interpreted as the absence of relationships between types and the chronological order of the assemblages, EPPM is a useful tool to explore significance of relationship between rows and columns related to seriation (Desachy 2004).
seriograph()
superimposes the frequencies (grey) and EPPM values (black)
for each row-column pair in a Ford diagram.
seriograph()
is called for its side-effects: it results in a graphic
being displayed (invisibly returns object
).
N. Frerebeau
Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396.
Other plot methods:
matrigraph()
,
plot_bertin()
,
plot_diceleraas()
,
plot_ford()
,
plot_heatmap()
,
plot_rank()
,
plot_spot()
## Data from Desachy 2004 data("compiegne", package = "folio") ## Seriograph seriograph(compiegne) seriograph(compiegne, weights = TRUE) ## Compute EPPM counts_eppm <- eppm(compiegne) plot_heatmap(counts_eppm, col = khroma::color("YlOrBr")(12))
## Data from Desachy 2004 data("compiegne", package = "folio") ## Seriograph seriograph(compiegne) seriograph(compiegne, weights = TRUE) ## Compute EPPM counts_eppm <- eppm(compiegne) plot_heatmap(counts_eppm, col = khroma::color("YlOrBr")(12))
SHE Analysis
she(object, ...) ## S4 method for signature 'matrix' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "right"), ... ) ## S4 method for signature 'data.frame' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "right"), ... )
she(object, ...) ## S4 method for signature 'matrix' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "right"), ... ) ## S4 method for signature 'data.frame' she( object, unbiased = FALSE, main = NULL, sub = NULL, ann = graphics::par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL, panel.last = NULL, legend = list(x = "right"), ... )
object |
A |
... |
Further graphical parameters to be passed to
|
unbiased |
A |
main |
A |
sub |
A |
ann |
A |
axes |
A |
frame.plot |
A |
panel.first |
An an |
panel.last |
An |
legend |
A |
If samples are taken along a gradient or stratigraphic section, breaks in the curve may be used to infer discontinuities.
This assumes that the order of the matrix rows (from to
)
follows the progression along the gradient/transect.
she()
is called for its side-effects: it results in a graphic being
displayed (invisibly returns object
).
N. Frerebeau
Buzas, M. A. & Hayek, L.-A. C. (1998). SHE analysis for biofacies identification. Journal of Foraminiferal Research, 1998, 28(3), 233-239.
Hayek, L.-A. C. & Buzas, M. A. (2010). Surveying Natural Populations: Quantitative Tools for Assessing Biodiversity. Second edition. New York: Columbia University Press.
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## SHE analysis she(cantabria)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## SHE analysis she(cantabria)
Similarity
similarity(object, ...) ## 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") )
similarity(object, ...) ## 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") )
object |
A |
... |
Currently not used. |
method |
A |
-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 -
where
is perfect similarity and
is
no similarity. The Brainerd-Robinson index is scaled between
and
. The Binomial co-occurrence assessment approximates a Z-score.
binomial
brainerd
bray
jaccard
morisita
sorenson
A stats::dist object.
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
index_binomial()
, index_brainerd()
, index_bray()
,
index_jaccard()
, index_morisita()
, index_sorenson()
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
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
## 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
Measure Diversity by Comparing to Simulated Assemblages
## S4 method for signature 'DiversityIndex' simulate( object, n = 1000, step = 1, interval = c("percentiles", "student", "normal"), level = 0.8, progress = getOption("tabula.progress") )
## S4 method for signature 'DiversityIndex' simulate( object, n = 1000, step = 1, interval = c("percentiles", "student", "normal"), level = 0.8, progress = getOption("tabula.progress") )
object |
A DiversityIndex object. |
n |
A non-negative |
step |
An |
interval |
A |
level |
A length-one |
progress |
A |
Returns a DiversityIndex object.
N. Frerebeau
Baxter, M. J. (2001). Methodological Issues in the Study of Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184.
Kintigh, K. W. (1984). Measuring Archaeological Diversity by Comparison with Simulated Assemblages. American Antiquity, 49(1), 44-54. doi:10.2307/280511.
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
turnover()
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = "shannon") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = "observed") r_sim <- simulate(r) plot(r_sim)
## Data from Conkey 1980, Kintigh 1989 data("cantabria") ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = "shannon") h_sim <- simulate(h) plot(h_sim) r <- richness(cantabria, method = "observed") r_sim <- simulate(r) plot(r_sim)
Compares Shannon/Simpson diversity between samples.
test_shannon(x, y, ...) test_simpson(x, y, ...) ## S4 method for signature 'numeric,numeric' test_shannon(x, y, ...) ## S4 method for signature 'matrix,missing' test_shannon(x, adjust = "holm", ...) ## S4 method for signature 'data.frame,missing' test_shannon(x, adjust = "holm", ...) ## S4 method for signature 'numeric,numeric' test_simpson(x, y, adjust = "holm", ...) ## S4 method for signature 'matrix,missing' test_simpson(x, adjust = "holm", ...) ## S4 method for signature 'data.frame,missing' test_simpson(x, adjust = "holm", ...)
test_shannon(x, y, ...) test_simpson(x, y, ...) ## S4 method for signature 'numeric,numeric' test_shannon(x, y, ...) ## S4 method for signature 'matrix,missing' test_shannon(x, adjust = "holm", ...) ## S4 method for signature 'data.frame,missing' test_shannon(x, adjust = "holm", ...) ## S4 method for signature 'numeric,numeric' test_simpson(x, y, adjust = "holm", ...) ## S4 method for signature 'matrix,missing' test_simpson(x, adjust = "holm", ...) ## S4 method for signature 'data.frame,missing' test_simpson(x, adjust = "holm", ...)
x , y
|
A |
... |
Further arguments to be passed to internal methods. |
adjust |
A |
If x
and y
are numeric
vectors, returns a list
containing the
following components:
statistic
The value of the t-statistic.
parameter
The degrees of freedom for the t-statistic.
p.value
The p-value for the test.
If x
is a matrix
or a data.frame
, returns a table of adjusted p-values
in lower triangular form.
test_shannon(x = matrix, y = missing)
: Produces two sided pairwise comparisons.
test_shannon(x = data.frame, y = missing)
: Produces two sided pairwise comparisons.
test_simpson(x = matrix, y = missing)
: Produces two sided pairwise comparisons.
test_simpson(x = data.frame, y = missing)
: Produces two sided pairwise comparisons.
N. Frerebeau
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
## Data from Magurran 1988, p. 145-149 oakwood <- c(35, 26, 25, 21, 16, 11, 6, 5, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 0, 0) spruce <- c(30, 30, 3, 65, 20, 11, 0, 4, 2, 14, 0, 3, 9, 0, 0, 5, 0, 0, 0, 0, 1, 1) test_shannon(oakwood, spruce) test_simpson(oakwood, spruce) ## Data from Conkey 1980, Kintigh 1989 data("cantabria") test_shannon(cantabria) test_simpson(cantabria)
## Data from Magurran 1988, p. 145-149 oakwood <- c(35, 26, 25, 21, 16, 11, 6, 5, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 0, 0) spruce <- c(30, 30, 3, 65, 20, 11, 0, 4, 2, 14, 0, 3, 9, 0, 0, 5, 0, 0, 0, 0, 1, 1) test_shannon(oakwood, spruce) test_simpson(oakwood, spruce) ## Data from Conkey 1980, Kintigh 1989 data("cantabria") test_shannon(cantabria) test_simpson(cantabria)
Returns the degree of turnover in taxa composition along a gradient or transect.
turnover(object, ...) ## S4 method for signature 'matrix' turnover( object, ..., method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson") ) ## S4 method for signature 'data.frame' turnover( object, ..., method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson") )
turnover(object, ...) ## S4 method for signature 'matrix' turnover( object, ..., method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson") ) ## S4 method for signature 'data.frame' turnover( object, ..., method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson") )
object |
A |
... |
Further arguments to be passed to internal methods. |
method |
A |
The following methods can be used to ascertain the degree of turnover
in taxa composition along a gradient (-diversity) on qualitative
(presence/absence) data:
cody
routledge1
routledge2
routledge3
Routledge third measure (exponential form of the second measure).
whittaker
wilson
This assumes that the order of the matrix rows (from to
)
follows the progression along the gradient/transect.
A numeric
vector.
N. Frerebeau
index_cody()
, index_routledge1()
, index_routledge2()
,
index_routledge3()
, index_whittaker()
, index_wilson()
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
## Data from Magurran 1988, p. 162 data("woodland") ## Whittaker's measure turnover(woodland, "whittaker") # 1 ## Cody's measure turnover(woodland, "cody") # 3 ## Routledge's measures turnover(woodland, "routledge1") # 0.29 turnover(woodland, "routledge2") # 0.56 turnover(woodland, "routledge3") # 1.75 ## Wilson and Shmida's measure turnover(woodland, "wilson") # 1
## Data from Magurran 1988, p. 162 data("woodland") ## Whittaker's measure turnover(woodland, "whittaker") # 1 ## Cody's measure turnover(woodland, "cody") # 3 ## Routledge's measures turnover(woodland, "routledge1") # 0.29 turnover(woodland, "routledge2") # 0.56 turnover(woodland, "routledge3") # 1.75 ## Wilson and Shmida's measure turnover(woodland, "wilson") # 1
A dataset of presence or absence of trees in six (10 x 10 m) quadarts along a transect through a deciduous woodland.
woodland
woodland
A data.frame
with 6 rows (quadarts) and 6 variables (tree species).
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Other datasets:
aves
,
cantabria
,
pueblo