Refine CA-based Seriation

## Usage

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

## 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`

).

## 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."

## Methods (by class)

`hist(RefinePermutationOrder)`

: Compute and plot a histogram of convex hull maximum dimension length.

## 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:
`permute()`

,
`seriate_average()`

,
`seriate_rank()`