Refine CA-based Seriation
Usage
refine(object, ...)
# S4 method for class 'AveragePermutationOrder'
refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)
# S4 method for class 'BootstrapCA'
refine(object, cutoff, margin = 1, axes = 1, ...)Arguments
- object
- A - PermutationOrderobject (typically returned by- seriate_average()) or a- dimensio::BootstrapCAobject (typically returned by- dimensio::bootstrap()).
- ...
- Currently not used. 
- cutoff
- A function that takes a numeric vector as argument and returns a single numeric value (see below). 
- margin
- A length-one - numericvector giving the subscripts which the refinement will be applied over:- 1indicates rows,- 2indicates columns.
- axes
- An - integervector giving the subscripts of the CA axes to be used.
- n
- A non-negative - integergiving the number of bootstrap replications.
Value
A list with the following elements:
- length
- A - numericvector giving the convex hull maximum dimension length.
- cutoff
- A - numericvalue giving the cutoff value for samples selection.
- exclude
- An - integervector giving the subscript of the observations to be removed.
- margin
- A - numericvalue specifying the dimension along which the refinement procedure has been applied:- 1indicates rows,- 2indicates columns.
Details
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. doi:10.1016/j.jas.2012.04.040 .
See also
Other seriation methods:
as_seriation(),
assess(),
order(),
permute(),
seriate_average(),
seriate_rank()
