
Statistical Significance of Seriation Solutions
Source:R/AllGenerics.R, R/seriation_assess.R
      assess.RdTests the significance of seriation solutions.
Usage
assess(object, ...)
# S4 method for class 'AveragePermutationOrder'
assess(object, axes = 1, n = 1000, progress = getOption("kairos.progress"))Arguments
- object
- A - PermutationOrderobject giving the permutation order for rows and columns (typically returned by- seriate_average()).
- ...
- Currently not used. 
- axes
- An - integervector giving the subscripts of the CA axes to be used.
- n
- A non-negative - integergiving the number of bootstrap replications.
- progress
- A - logicalscalar: should a progress bar be displayed?
Value
A list with the following elements:
- random
- A - numericvector giving the randomized total number of modes values.
- observed
- A - numericvalue giving the observed total number of modes.
- expected
- A - numericvalue giving the expected total number of modes if all types had unimodal distributions.
- maximum
- A - numericvalue giving the maximum total number of modes.
- coef
- A - numericvalue giving the seriation coefficient (a value close to 1 indicates a strong fit to the seriation model, while a value close to 0 indicates a poor fit).
References
Porčić, M. (2013). The Goodness of Fit and Statistical Significance of Seriation Solutions. Journal of Archaeological Science, 40(12): 4552-4559. doi:10.1016/j.jas.2013.07.013 .
See also
Other seriation methods:
as_seriation(),
order(),
permute(),
refine(),
seriate_average(),
seriate_rank()
Examples
if (FALSE) { # \dontrun{
## Data from Desachy 2004
data("compiegne", package = "folio")
## Correspondance analysis based seriation
(indices <- seriate_average(compiegne, margin = c(1, 2), axes = 1))
## Test significance of seriation results
## Warning: this may take a few seconds!
signif <- assess(indices, axes = 1, n = 1000)
## Histogram of randomized total number of modes
hist(signif$random)
## Observed value is smaller than the 5th percentile of the
## distribution of randomized samples
quantile(signif$random, probs = 0.05)
signif$observed
## Seriation coefficient
## (close to 1: relatively strong and significant signal of unimodality)
signif$coef
} # }