PCC {stemmatology}

PCC (Poole-Camps-Cafiero) stemmatological method

Description

Global shell for all the PCC functions, both exploratory and stemma-building. This command successively executes PCC.Exploratory and PCC.Stemma, while asking user for input when necessary.

Usage

PCC(x, omissionsAsReadings = FALSE, alternateReadings = FALSE, limit = 0, 
    recoverNAs = TRUE, layout_as_stemma = FALSE, pauseAtPlot = FALSE, 
    ask = TRUE, threshold = NULL, verbose = FALSE)

Arguments

x

if alternateReadings = FALSE (default), a numeric matrix, with witnesses in columns, variant locations in rows, and readings coded by a number; if alternateReadings = TRUE, a character matrix, with witnesses in columns, variant locations in rows, and, in each cell, one or several readings, coded by numbers and separated by a comma (e.g. '1,2,3', if the witness has three different readings).

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.

alternateReadings

logical; if TRUE, a witness can have multiple variants for a single variant location (contaminated manuscripts, editio variorum, …), encoded as comma-separated values. Default: FALSE.

limit

The maximum number of severe disagreements expected for witnesses to be in the same group. Default: 0.

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.

layout_as_stemma

logical; if TRUE, the witnesses will be placed vertically according to the distance from their parent, as per the function layout_as_stemma (experimental!) Default: FALSE

pauseAtPlot

logical; if TRUE, the algorithms stops at each plot during the execution of PCC.contam. Default: FALSE.

ask

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

threshold

numeric; the centrality threshold above which variant locations are considered to be over-conflicting. Used only with ask = FALSE.

verbose

logical; if FALSE, the function will only return the results, without information on the operations. Default: FALSE.

Details

This function provides a single entry to all the algorithms used in the PCC method. It successively calls PCC.Exploratory and PCC.Stemma. The algorithmic principles of the PCC method are described in Camps & Cafiero 2015. It builds on the propositions of Poole 1974, 1979.

In a first stage, problematic configurations in the traditions (i.e. configurations that cannot be linked to a normal genealogy, without contamination or polygenesis) are identified by crossing every possible pair of variant locations, and are then plotted as a network. When the most unreliable variant locations (i.e. unreliable) are identified, different methods for eliminating them are offered.

In a second time, a stemma is iteratively built, using the variant locations selected in the first stage. At each step, witness with no severe disagreements (i.e. disagreements between two witnesses, on two readings both shared with at least one other witness, cf. Trennfehler, errores separativi) are grouped together. A model is then reconstructed for each group, and either identified to a witness of the group or to an hypothetical subarchetype.

The option recoverNAs=TRUE is a novelty not described in the original paper (Camps & Cafiero 2015).

For more information about the underlying principles behind the method applied here, particularly the distinction between severe and benign disagreement, the different status given to readings, omissions and lacunae, the notion of conflict between variant locations or the way the stemma is built, see the references section.

Value

The function returns either a single object of class "pccStemma", or a list containing several objects of class "pccStemma" (if multiple stemmata were drawn); see PCC.Stemma.

Author(s)

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

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.Exploratory, PCC.Stemma.

Examples

# Load data
data("fournival")
# or alternatively, import it
# fournival = import.TEIApparatus(file = "myFournival.xml", 
#    appTypes = c("substantive"))

# Analyse it with the PCC functions

# Non interactive mode
PCC(fournival, ask = FALSE, threshold = 0.06)

## Not run: 
# Interactive mode
PCC(fournival)

## End(Not run)