PCC.reconstructModel: PCC.reconstructModel: Reconstruct the Model of Groups of...
In stemmatology: Stemmatological Analysis of Textual Traditions

Description Usage Arguments Details Value Author(s) References See Also Examples

PCC.reconstructModel examines coherent clusters of witnesses (PCC.buildGroup), to either identify their model in the tradition, either suggest a reconstructed model for the group.

1 2	PCC.reconstructModel(x, omissionsAsReadings = FALSE, recoverNAs = TRUE, ask = TRUE, verbose = FALSE)

`x`	The output of `PCC.buildGroup`.
`omissionsAsReadings`	logical; if `TRUE`, omissions are treated as variant readings. They are taken into account in determining conflicts between variant locations or in computing severe disagreements between witnesses). Default: `FALSE`.
`recoverNAs`	logical; if `TRUE`, when an actual witness or reconstructed subarchetype is identified to the reconstructed model of a group, every NA it has is recovered by taking the value of the reconstructed model; if `FALSE`, their NAs values are kept. Default: `TRUE`.
`ask`	logical; if FALSE, decisions will be made without asking the user for input. Default: TRUE
`verbose`	logical; if FALSE, the function will only return the results, without information on the operations. Default: FALSE

This function takes PCC.buildGroup objects as input. It assesses the characteristics of the model of each group, and compares it to the existing witnesses. If a witness has the same characteristics as the computed model, it is identified as the model for the group. If no witness seems to be a good fit, the function adds a reconstructed model to the tradition.

The function returns a list containing

`fullDatabase`	The full database, with the new reconstructed models and recovered NAs (if applicable).
`database`	The same with the descripti removed.
`edgelist`	An edgelist expressing the relations between the witnesses of each group with, as a third column, the distances between witnesses.
`models`	A list containing the database of readings for each model at the time of their reconstruction (i.e., before they are compared to extant witnesses).
`modelsByGroup`	A matrix with, in columns the groups, and a single row containing the label of their model.

Jean-Baptiste Camps (jbcamps@hotmail.com) & Florian Cafiero

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.

PCC.Stemma, PCC.disagreement, PCC.buildGroup.

#A fictional simple tradition
x = list(database = 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"))), 
    groups = list(c("A", "B", "C"), c("D", "E")))
#And now, reconstruct the groups
PCC.reconstructModel(x)