PCC.buildGroup {stemmatology}

PCC.buildGroup: Group Witnesses in Clusters

Description

PCC.buildGroup groups together witnesses in relevant clusters, based on the absence (or number inferior to a limit) of severe disagreements between them.

Usage

PCC.buildGroup(x, limit = 0, ask = TRUE)

Arguments

x

A PCC.disagreement object.

limit

The maximum number of severe disagreements allowed for two witnesses in the same group. Default (and advised) value: 0.

ask

logical; if FALSE, decisions will be made without asking the user for input. Default: TRUE

Details

Witnesses a number of severe disagreements between them lesser than or equal to limit are grouped together. This disagreement-based method is described in Camps & Cafiero 2015.

Value

The function returns a list containing:

database

The original database.

groups

A list of the groups that were created, identified by their labels.

Author(s)

Jean-Baptiste Camps ([e-mail]) & Florian Cafiero

References

Camps, Jean-Baptiste, and Florian Cafiero. ‘Stemmatology: An R Package for the Computer-Assisted Analysis of Textual Traditions’. Proceedings of the Second Workshop on Corpus-Based Research in the Humanities (CRH-2), edited by Andrew U. Frank et al., 2018, pp. 65–74, https://halshs.archives-ouvertes.fr/hal-01695903v1.

Camps, Jean-Baptiste, and Florian Cafiero. ‘Genealogical Variant Locations and Simplified Stemma: A Test Case’. Analysis of Ancient and Medieval Texts and Manuscripts: Digital Approaches, edited by Tara Andrews and Caroline Macé, Brepols, 2015, pp. 69–93, https://halshs.archives-ouvertes.fr/halshs-01435633, DOI: 10.1484/M.LECTIO-EB.5.102565.

Poole, Eric. ‘L’analyse stemmatique des textes documentaires’. La pratique des ordinateurs dans la critique des textes, Paris, 1979, p. 151-161.

Poole, Eric, ‘The Computer in Determining Stemmatic Relationships’. Computers and the Humanities, 8-4 (1974), p. 207-16.

See Also

PCC.Stemma, PCC.disagreement, PCC.reconstructModel.

Examples

# A fictional simple tradition
x = matrix(
    c(
      1,0,1,1,1,1,1,1,
      1,0,1,2,2,2,1,2,
      1,0,0,3,2,1,NA,3,
      2,0,1,4,NA,1,1,1,
      2,1,2,5,2,1,1,4
    ), nrow = 8, ncol = 5,
    dimnames = list(c("VL1","VL2","VL3","VL4","VL5","VL6","VL7","VL8"),
                    c("A","B","C","D","E")))
# Compute disagreement(s)
x = PCC.disagreement(x)
# And now build the groups
PCC.buildGroup(x)