seriate_refine: Refine CA-based Seriation

Description

Refine CA-based Seriation

Usage

seriate_refine(object, ...)
# S4 method for AveragePermutationOrder
seriate_refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)
# S4 method for BootstrapCA
seriate_refine(object, cutoff, margin = 1, axes = 1, ...)
# S4 method for RefinePermutationOrder
hist(x, ...)

Value

seriate_refine() returns a RefinePermutationOrder object.
hist() is called it for its side-effects: it results in a histogram being displayed (invisibly returns x).

Arguments

object: A PermutationOrder object (typically returned by seriate_average()).
...: Further arguments to be passed to internal methods.
cutoff: A function that takes a numeric vector as argument and returns a single numeric value (see below).
margin: A length-one numeric vector giving the subscripts which the refinement will be applied over: 1 indicates rows, 2 indicates columns.
axes: An integer vector giving the subscripts of the CA axes to be used.
n: A non-negative integer giving the number of bootstrap replications.
x: A RefinePermutationOrder object

Methods (by class)

hist(RefinePermutationOrder): Compute and plot a histogram of convex hull maximum dimension length.

Author

N. Frerebeau

Details

seriate_refine() 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."

References

Peeples, M. A., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation of regional data sets. Journal of Archaeological Science, 39(8), 2818-2827. tools:::Rd_expr_doi("10.1016/j.jas.2012.04.040").