seriate_* computes a permutation order for rows and/or columns.
permute rearranges a data matrix according to a permutation order.
get_order returns the seriation order for rows and columns.
refine_seriation performs a partial bootstrap correspondence analysis
seriation.
seriate_reciprocal(object, ...)seriate_correspondence(object, subset, ...)
seriate_idds(object, ...)
permute(object, order, ...)
refine_seriation(object, ...)
get_order(object)
# S4 method for PermutationOrder
get_order(object)
# S4 method for CountMatrix
refine_seriation(object, cutoff, n = 1000,
axes = c(1, 2), ...)
# S4 method for CountMatrix
seriate_reciprocal(object, EPPM = FALSE,
margin = c(1, 2), stop = 100)
# S4 method for IncidenceMatrix
seriate_reciprocal(object, margin = c(1, 2),
stop = 100)
# S4 method for CountMatrix,missing
seriate_correspondence(object,
margin = c(1, 2), ...)
# S4 method for IncidenceMatrix,missing
seriate_correspondence(object,
margin = c(1, 2), ...)
# S4 method for CountMatrix,BootCA
seriate_correspondence(object, subset,
margin = c(1, 2), ...)
# S4 method for CountMatrix,PermutationOrder
permute(object, order)
# S4 method for IncidenceMatrix,PermutationOrder
permute(object, order)
Further arguments to be passed to internal methods.
A function that takes a numeric vector as argument and returns a single numeric value (see below).
A non-negative integer giving the number of partial
bootstrap replications (see below).
A numeric vector giving the subscripts of the CA
axes to be used (see below).
A logical scalar: should the seriation be computed
on EPPM instead of raw data?
A numeric vector giving the subscripts which the
rearrangement will be applied over: 1 indicates rows, 2
indicates columns, c(1, 2) indicates rows then columns,
c(2, 1) indicates columns then rows.
An integer giving the stopping rule
(i.e. maximum number of iterations) to avoid infinite loop.
seriate_* returns a '>PermutationOrder object.
permute returns either a '>CountMatrix or an
'>IncidenceMatrix (the same as object).
The matrix seriation problem in archaeology is based on three conditions and two assumptions, which Dunell (1970) summarizes as follows.
The homogeneity conditions state that all the groups included in a seriation must:
Be of comparable duration.
Belong to the same cultural tradition.
Come from the same local area.
The mathematical assumptions state that the distribution of any historical or temporal class:
Is continuous through time.
Exhibits the form of a unimodal curve.
Theses assumptions create a distributional model and ordering is accomplished by arranging the matrix so that the class distributions approximate the required pattern. The resulting order is inferred to be chronological.
The following seriation methods are available:
Correspondence analysis-based seriation. Correspondence analysis (CA) is an effective method for the seriation of archaeological assemblages. The order of the rows and columns is given by the coordinates along one dimension of the CA space, assumed to account for temporal variation. The direction of temporal change within the correspondence analysis space is arbitrary: additional information is needed to determine the actual order in time.
Reciprocal ranking seriation. These procedures iteratively rearrange rows and/or columns according to their weighted rank in the data matrix until convergence. Note that this procedure could enter into an infinite loop. If no convergence is reached before the maximum number of iterations, it stops with a warning.
refine_seriation allows to identify samples that are subject to
sampling error or samples that have underlying structural relationships
and might be influencing the ordering along the CA space.
This relies on a partial bootstrap approach to CA-based seriation where each
sample is replicated n times. The maximum dimension length of
the convex hull around the sample point cloud allows to remove samples for
a given cutoff value.
According to Peebles and Schachner (2012), "[this] point removal procedure [results in] a reduced dataset where the position of individuals within the CA are highly stable and which produces an ordering consistent with the assumptions of frequency seriation."
If the results of refine is used as an input argument in
seriate, a correspondence analysis is performed on the subset of
object which matches the samples to be kept. Then excluded samples
are projected onto the dimensions of the CA coordinate space using the row
transition formulae. Finally, row coordinates onto the first dimension
give the seriation order.
Desachy, B. (2004). Le s<U+00E9>riographe EPPM: un outil informatis<U+00E9> de s<U+00E9>riation graphique pour tableaux de comptages. Revue arch<U+00E9>ologique de Picardie, 3(1), 39-56. DOI: 10.3406/pica.2004.2396.
Dunnell, R. C. (1970). Seriation Method and Its Evaluation. American Antiquity, 35(03), 305-319. DOI: 10.2307/278341.
Ihm, P. (2005). A Contribution to the History of Seriation in Archaeology. In C. Weihs & W. Gaul (Eds.), Classification: The Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. DOI: 10.1007/3-540-28084-7_34.
Peeples, M. A., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation of regional data sets. Journal of Archaeological Science, 39(8), 2818-2827. DOI: 10.1016/j.jas.2012.04.040.
# NOT RUN {
## Matrix seriation
## Replicates Desachy 2004 results
## Coerce dataset to abundance matrix
compiegne_count <- as_count(compiegne)
## Get seriation order for columns on EPPM using the reciprocal averaging method
## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H
(compiegne_indices <- seriate_reciprocal(compiegne_count, EPPM = TRUE,
margin = 2))
## Permute columns
compiegne_new <- permute(compiegne_count, compiegne_indices)
## Plot new matrix
plot_ford(compiegne_new, EPPM = FALSE)
## Refined seriation
## See the vignette:
# }
# NOT RUN {
utils::vignette("seriation", package = "tabula")
# }
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